Dr. Rubens Riotorto Bellomo
Medical Oncologist
Medical Oncologist
Compassionate and dedicated professional with extensive experience diagnosing and treating cancer across hospitals and clinical settings. Well-versed in improving patient’s lives, delivering palliative care, interfacing with radiotherapy team, and recommending effective medical oncology treatments regarding bowel, breast, and lung cancer. Proficient in meeting co-ordination, budgeting, consultancy services, case, personnel, and project management. Possess multi-lingual proficiencies in Spanish, English, French, Italian, and Portuguese.
Professional Experience
CHU UCL Louvain, Dinant
Medical Oncologist, 2018
Headed departmental functions at the Day Medical Hospital to accomplish bottom-line results. Assisted four multi-disciplinary meetings regarding breast, gynaecology, colon, and dermatology with St. Elizabeth and Mt. Godinne centres.
Provided robust consultancy services to patients.
Managed patients’ medical oncology treatments.
Genesis Care Group, Spain
Medical Oncologist, 2015–2018
Recommended treatments to cancer patients at Algeciras Clinic in Radon Clinic Algeciras. Discussed patients’ condition with radiotherapy team and proposed RT treatment by interfacing with radiotherapist specialist at Algeciras Clinic.
Conducted follow up visits to oncologic outpatients with all solid tumours and newly cancer patients at Quore Clinic.
Attended weekly MDT meeting and mentored admitted oncologic patients across Clinica La Salud Cadiz.
Clinica Diagonal, Barcelona, Spain
Medical Oncologist, 2012–2015
Delivered solid tumours and chemotherapy services to newly diagnosed cancer patients. Controlled toxicity management. Participated in multi-disciplinary meeting and discussed oncologic patients’ history with general surgeon, radiologist, pathologist, internal medicine, psycologist, and nurse.
Managed solid tumours service with follow up visits.
Hospital Santos Reyes, Aranda de Duero, Burgos
In charge - Tumor Registry, 2010–2011
Gathered and upheld appropriate diagnostic, therapeutic, and survival data on patients with neoplastic diseases while attending regular meetings of the central Tumor Registry in Burgos.
Technical Commission, Spanish Association against Cancer, Melilla, Spain
President, 2008–2009
Provided expert opinion to board of directors on technical aspects of actions of Association.
Hospital Comarcal de Melilla, Spain
Medical Oncologist, 2006–2009
Saw patients with all solid tumors at a Day Hospital to give all medical oncology treatments available at Hospital on a daily basis. Administered patients with follow up visits and newly diagnosed cancer patients. Managed all the patients with an oncologic diagnosis admitted at Hospital and supervised inpatient chemotherapy, toxicity, and palliative care. Oversaw admissions and review of internal medicine patients admitted to Hospital with all internal medicine pathology. Managed all kind of emergency patients including children.
Credited for providing oncology service to about 85K inhabitants within a city.
Additional Experience:
Consultant Assistant, USP Dexeus University Institute, Barcelona | Hospital of Barcelona, Assistencia Sanitaria
Committee Member, Tumor Committee, Hospital Santos Reyes, Aranda de Duero, Burgos
Member of the Breast Pathology Unit, Hospital Santos Reyes, Aranda de duero
Medical Oncologist, Hospital Santos Reyes, Aranda de Duero, Burgos, Spain
Secretary, Tumor Registry Commission, Hospital Comarcal de Melilla, Spain
Medical Oncologist, Asociación Española Primera de Socorros Mutuos, Montevideo, Uruguay
Director, National Institute of Oncology, Ministry of Public Health, Montevideo Uruguay
Committe Member, Comisión Honoraria de Lucha contra el Cancer, Montevideo, Uruguay
Head of Department, Department of Medical Oncology, Instituto Nacional de Oncología, Montevideo Uruguay
Commitee Member, Central Tumor Comittee, (Dirección General de la Administración de Servicios de Salud del Estado), Ministry of Public Health, Montevideo, Uruguay
Committee Member, Advisor Committe for purchases of Antitumor medications, Instituto Nacional de Oncología, Montevideo, Uruguay
Medical Advisor, Anti- tumor National medication Bank, Instituto Nacional de Oncología, Montevideo Uruguay
Advisor, Technical education for Professionals, Comisión Honoraria de Lucha contra el Cáncer, Montevideo, Uruguay
Committee Member, Climateric Commission Ministry of Public Health, Montevideo, Uruguay
Head of Department, Chemotherapy Service, Instituto Nacional de Oncologia
Designed Medical Doctor, Instituto Nacional de Oncología
Education & Credentials
Medical Oncology
Graduate School of Facultad de Medicina de la República Oriental del Uruguay – Montevideo Uruguay
homologation diplomes In Spain, UK and Belgium
Degree of Medical Doctor
Universidad de la República Oriental del Uruguay –Montevideo Uruguay
homologation diplomes In Spain, UK and Belgium
Professional Development Program for Goverment Employees of Oficina Nacional del Registro Civil
Honorary Collaborator, Immunology Department, Facultad de Química, Montevideo Uruguay under the Supervision of Fernando Goñi in the area of Immunoglobulins & Cancer
Aguarded with a scholarship by Insituto Nacional de Oncología and Comisión Honoraria de Lucha contra el Cáncer at the INSTITUT CURIE of Paris reporting to Prof. Dr. Pierre Pouillart
Courses & Conferences Attended
European Congress 2018 Munich, Germany; European Cancer Congress 2017, Madrid, Spain; ESMO Congress, Copenhagen, Denmark; VI Symposium SOGUG (Spanish Genitourinary Group); Vienna, European Cancer Congress 2015 (ECC 2015); V Symposisum SOGUG; Madrid. ESMO Congress; Madrid, IV Symposium SOGUG; 15th World Congress On Gastrointestinal Cancer, Barcelona; Praga, Prostate Cancer up to date. AGUCC Congress; Updates on Cancer. Dr. Díaz Rubio, Madrid; Intrernational Meeting in Cambridge: Advances on Brest Cancer, UK; 5th EAN- EGFR Advisors Network Meeting, Netherlands
Professional Associations
Member: Spanish Oncology Genitourinary Group, European Society of Medical Oncology, Sociedad Española de Oncología Médica
Publication
1. 1995 ASCO Annual Meeting Abstract N: 730. Authors: E Lasalvia, S Cuchi, T Carlevaro, J Vazquez, R Riotorto, L Fierro, Antimetastatic effect of a blood fraction from cancer patients (Meeting Abstract). 2006.
2. Phase II, open-label, randomized study (SIGN) of single agent Gefitinib (IRESSA) or docetaxel as second-line therapy in patients with advanced (stage IIIb or IV) nbon-small-cell lung cancer. Cufer T; Vrdoljak E; Gaafar R; Erensoy I; Pemberton K; on behalf of the SIGN study proup, Principal resercher for Uruguay. Fase II Gefitinib.
3. Anales de Otorrinolaringología del Uruguay, Tomo LXXXI-2004-29 Importancia del Factor de Crecimiento Epidérmico en Oncología. Caso Clínico. Authors: Falconi E; Riotorto R, García E.
4. Communication in the 57 Congress of the National Society of Spanish Hospital Pharmacy. “Usefulness of Cystatin C for Therapeutic Drug Monitoring of Amikacin in an Oncologic Patient”. Authors: Rebollo Díaz N; Moreno Gómez AM; Revilla Cuesta N; Riotorto Bellomo RE; Cepeda Piorno J.Rubens Riotorto Bellomo
Anhée
ruriotorto@gmail.com +32460977053
Personal Details:
March 3rd, 1955 Montevideo Spain
4. Male Spanish Citizenship
sábado, 9 de marzo de 2019
sábado, 25 de agosto de 2018
IBM Watson Oncology: Not Living Up to Expectations
IBM Watson Oncology: Not Living Up to Expectations
Roxanne Nelson, BSN, RN
August 15, 2018
If it sounds too good to be true, then maybe it is.
Such seems to be the case with IBM Watson, which has been aggressively marketed as a tool to assist oncologists in selecting the optimal treatment regimen for their patients.
However, some of its recommendations for cancer treatment have been questionable, according to a new investigative report by STAT, which suggests it is not yet ready for prime time. Internal IBM documents, which STAT had access to, showed that Watson often gave erroneous cancer treatment advice and that company medical specialists and customers identified "multiple examples of unsafe and incorrect treatment recommendations" at the same time that IBM was promoting its supercomputer to hospitals and physicians across the globe.
The report certainly gives oncologists pause for thought, and maybe second thoughts before enlisting the help of Watson.
With an almost never ending series of new biomarkers and mutations being detected and a dazzling array of new therapies pouring out of the pipeline, choosing a cancer treatment regimen can take an arduous amount of time.
In a recent report, the supercomputer was shown to significantly speed up the process of analyzing whole-genome sequencing, which is a "key bottleneck in cancer genomics." It took IBM Watson 10 minutes to come up with conclusions that were similar to those reached by a team of experts after 160 hours of analysis.
However, when implemented in real-world oncology settings, Watson fares less well, according to the STAT report. Part of the problem appears to stem from how the system was trained. IBM's internal documents largely blame the problems on the training of Watson by IBM engineers and oncologists at Memorial Sloan Kettering Cancer Center in New York City, who had been tapped to train Watson in 2012. The resulting software was drilled with a small number of "synthetic" cancer cases, or hypothetical patients, instead of real patient data. Thus, Watson was trained on the basis of the expertise of a few specialists for each cancer type, rather than from guidelines or evidence.
STAT also noted that product information posted on the IBM website implies that Watson is continuing to be trained using real patient data and says that the supercomputer "analyzes patient data against thousands of historical cases and insights gleaned from thousands of Memorial Sloan Kettering MD and analyst hours." However, the number of cases for each of the eight cancers that are covered is a small fraction of the grand total, which ranges from 635 cases for lung cancer to 106 for ovarian cancer.
Gloom and Doom?
The Wall Street Journal recently issued its own report about IBM Watson, saying that despite the initial promise and hype, "six years and billions of dollars later, the diagnosis for Watson is gloomy."
Echoing some of the concerns brought up in the STAT articles, the Wall Street Journal notes that in many cases, the tools did not add much value, and in other cases, Watson was inaccurate. Some problems were that Watson could be "tripped up" by a lack of data for rare or recurring cancers and that treatments were evolving faster than Watson's human "trainers" were able to update the supercomputer. They also pointed out that thus far, there is no published research demonstrating that Watson has improved patient
Watson for Genomics has been piloted at a number of US cancer centers, but according to the Wall Street Journal, physicians at several centers have reported that results were not always accurate, and when they were, it was often information that oncologists already knew.
"The discomfort that I have — and that others have had with using it — has been the sense that you never know what you're really going to get...and how much faith you can put in those results," Lukas Wartman, MD, of the McDonnell Genome Institute at the Washington University School of Medicine in St. Louis, Missouri, told the newspaper. He also said that even though he has complimentary access to Watson, he rarely uses it.
IBM Responds
IBM has taken issue with the negative media reports that were published recently. It singled out the Wall Street Journal article specifically.
In an article entitled "Watson Health: Setting the Record Straight" posted on the company website, John E. Kelly III, MD, IBM senior vice president, Cognitive Solutions and IBM Research, writes that the media reports distort and ignore facts when they suggest that IBM has not made "enough" progress in bringing the benefits of artificial intelligence (AI) to healthcare.
"It is true, as the article reports, that we at IBM have placed a big bet on healthcare," Kelly writes. He says that IBM has done this for two reasons: "AI can make a big difference in solving medical challenges and supporting the work of the healthcare industry," he writes, and also there is an "enormous business opportunity in this area as the adoption of AI increases." IBM has built three distinct cancer tools, Kelly notes:
Watson for Oncology, which offers treatment recommendations/options that can augment an oncologist's own expertise,
Watson for Clinical Trial Matching, which helps match patients to clinical trials, and
Watson for Genomics, which uses genetic sequencing to provide information that can be used to personalize treatment for cancer patients.
"Together, they are now in use at 230 hospitals and health organizations globally and have nearly doubled the number of patients they've reached in the first six months of the year to 84,000," he writes.
Kelly also addresses the allegation that there has been no benefit to patients. "To suggest there has been no patient benefit is to ignore both what we know the Wall Street Journal was told by a number of physicians around the world and these institutions' own public comments — which we believe speak for themselves," he writes.
These were the examples that Kelly listed to support this statement:
Mayo Clinic physicians presented a poster at the annual meeting of the American Society of Clinical Oncology in which they reported that Watson for Clinical Trial Matching boosted enrollment in breast cancer trials by 80%, to 6.3 patients/month, up from 3.5 patients/month, during an 18-month period following its implementation.
Thaddeus Beck, MD, and colleagues at the Highland Oncology Group in Arkansas reported that Watson Clinical Trial Matching reduced the time for clinical trials matching by 78%.
Mark Kris, MD, and oncologists at the Memorial Sloan Kettering Cancer Center have helped to train Watson for Oncology regarding 13 cancers that represent 80% of global cancer incidence and prevalence.
S. P. Somashekhar, MBBS, and Manipal Hospital, Bangalore, India, reported a 93% concordance rate in breast cancer for their multidisciplinary tumor board in an article published in the Annals of Oncology earlier this year. They recently stated that their multidisciplinary tumor board uses Watson for Oncology for all of their complex cases and that it is changing their treatment recommendations in 9% to 11% of cases.
Michael Kelley, MD, and the Department of Veterans Affairs recently extended their contract for Watson for Genomics. Thus far, nearly 3000 veterans with stage IV cancer have been supported by this tool.
William Kim, MD, and the University of North Carolina Lineberger Cancer Center, Chapel Hill, reported that Watson for Genomics found new, actionable mutations in 32% of patients.
Oncologist Dilemma
So what should oncologists do with the extra information provided by the supercomputer?
Approached for comment, Nigam Shah, MBBS, PhD, associate professor of medicine and biomedical data science at Stanford University, California, said that he doesn't think there is much to debate about, given that the system is not being trained using real patient data.
"If I was an oncologist, I'd want to see their system validated in the same way we validate other interventions in medicine, such as with a prospective study, either with or without randomization, depending on the nature of the intervention," Shah told Medscape Medical News.
As an example, he suggested that a medical center could run IBM Watson in silent mode. The computer would be asked to assess and opine on every case the medical center sees. Its recommendations would be recorded, but not acted upon. The center would then assess how often Watson's recommendations corresponded with what the physicians did. "If that test establishes safety, then I'd start showing the recommendations and acting on them," Shah said, "then follow patients over time to see if those that got a Watson-generated recommendation did better than those who didn't."
But for now, he reiterated that if he were a practicing oncologist, "I'd just ignore the noise and chatter and wait for more proof points."
Shah added that IBM Watson should clearly disclose the data upon which the system is trained. "Any AI system is only as good as the data it gets to train on," he explained. "If the system is trained on idealized patient records generated by a small set of doctors, it is improper to say it is trained from real patient data at Sloan Kettering."
Michael Hogarth, MD, a professor in the Division of Biomedical Informatics, Department of Internal Medicine, the University of California, San Diego, said that although he is not a legal expert, he believes that there is a fair amount of case law that indicates that the physician using such a system is fully responsible for its use (ie, the system itself is not culpable).
"This is no different than a physician relying on a reference textbook that has misprinted or wrongly characterized something and using that information and having a bad patient outcome as a result," he explained. "Ultimately, the treating physician is 'always' responsible for the decision making — they can't blame a computer, journal article, or book for making them do the wrong thing."
Hogarth noted that he and many others in his discipline of health informatics were a bit skeptical of IBM when the company claimed Watson could improve oncologist decision making. Essentially, the reason is that specialists such as oncologists are experts in their fields, and some are "super experts" regarding specific conditions within their discipline and treat only patients with those conditions.
Such specialization has become very common in healthcare. It has been shown that clinical decision support systems tend to offer the most assistance to physicians who are not specialists of the condition at hand. "The more 'general' the physician, the more a clinical decision support system focused on specialized content — like IBM Watson Oncology — might surface up information that is not known by that physician," explained Hogarth. "When it comes to specialists, the amount of relevant knowledge is less overwhelming for the specialist, who has less to retain, since they don't have to know everything about every health condition and only need to know about the conditions they see in their specialty."
Hogarth added that the most concerning thing is that there is much talk about "machine learning" tools being used at the bedside for diagnosis and treatment decision making. "That's fine, but they still need to be validated," he emphasized. "Many of them are built using data from EHRs [electronic health records], and a lot of that data is incomplete in terms of knowing everything about a patient, since many patients get care in multiple health systems, so nobody has a complete record on them."
The notion of validating machine learning–based diagnostic aids is just starting to be discussed. "The question is whether it should be regulated as a medical device," he said.
Another physician who was approached for comment cautioned that oncologists need to be aware of where Watson's data are coming. "One thing that has to be emphasized is that Watson's output suffers a huge bias related to the fact that it's all Memorial Sloan Kettering doctors who are training Watson," David H. Gorski, MD, PhD, professor and chief, Breast Surgery Section, Wayne State University School of Medicine, Detroit, Michigan, told Medscape Medical News.
"That means Watson is basically the MSKCC way, which might or not be the right way in every case," he explained. "Remember, academic physicians approach the medical literature with their own biases and interpret it in light of them, and that's why we can see differing recommendations coming from different institutions."
He added that that is also why he wishes that there was a much broader base of physicians training Watson and feeding it studies. "In the end, Watson is a tool, nothing more," said Gorski. "It's a recommendation for treatment that suffers all the flaws of the medical literature and the specific doctors who taught it how to derive recommendations from that literature.
"If a doctor keeps that in mind and isn't afraid to overrule Watson when he or she thinks it's wrong," he added, "it certainly can be potentially useful."
Not Ready for Prime Time
STAT published an article in September 2017 about some of the problems that IBM was having with Watson. It reported that Watson was "still struggling with the basic step of learning about different forms of cancer. Only a few dozen hospitals have adopted the system, which is a long way from IBM's goal of establishing dominance in a multibillion-dollar market."
For that article, STAT conducted numerous interviews with various stakeholders, including physicians, IBM executives, and experts in AI. It also assessed its use, marketing, and performance in hospitals across the globe. The interviews suggested that "IBM, in its rush to bolster flagging revenue, unleashed a product without fully assessing the challenges of deploying it in hospitals globally," and as a result, "its flaws are getting exposed on the front lines of care by doctors and researchers who say that the system, while promising in some respects, remains undeveloped."
"Watson for Oncology is in their toddler stage, and we have to wait and actively engage, hopefully to help them grow healthy," said Taewoo Kang, MD, a South Korean cancer specialist who has used the product and was quoted in the STAT article.
Earlier this year, IBM confirmed rumors of company layoffs, but denied that the cuts affected 50% to 70% of the workforce at its Watson Health operation, as had been reported. IBM has not reported the actual number of layoffs.
"IBM is continuing to reposition our team to focus on the high-value segments of the IT market, and we continue to hire aggressively in critical new areas that deliver value for our clients and IBM. This activity affects a small percentage of our Watson Health workforce, as we move to more technology-intensive offerings, simplified processes, and automation to drive speed," IBM told Medscape Medical News in a statement earlier this year.
Hogarth commented in an interview with Medscape Medical News: "I understand that IBM Watson has laid off/moved many technical staff that were working on IBM Watson Health, so I think they discovered it was not as easy as they thought, plus actually improving decision making is not a trivial maneuver.... It involves many factors beyond just supplying summarized information."
Hogarth believes that had IBM put its sights on a more modest goal, it could have achieved it. The bar that IBM set Watson Health, as well as its expectations, were quite high and were perhaps misplaced or mistargeted. "Instead of creating a system for oncologists, they probably should have focused on a system that can assist general internists, family physicians, and other midlevel extenders and focused its help on patients with enigmatic diagnoses or patterns of symptoms and signs that are not frequently seen by generalists," he said. "That is where an engine like IBM Watson can potentially provide significant value."
IBM recently reported that it will be modifying its Watson software to better reflect geographic differences in cancer treatment, according to the most recent article from STAT about this topic. To date, Watson Oncology has made the most headway in Asia, and most of the hospitals currently using Watson are based outside of the United States. Some physicians have complained that Watson's "decisions" do not reflect treatment protocols in their country and have expressed dissatisfaction with its American bias, that article reports.
At an internal meeting for Watson Health employees worldwide, IBM announced that it would begin using data from real patients for the first time, and it recommended that treatments incorporate more localized treatment advice.
Roxanne Nelson, BSN, RN
August 15, 2018
If it sounds too good to be true, then maybe it is.
Such seems to be the case with IBM Watson, which has been aggressively marketed as a tool to assist oncologists in selecting the optimal treatment regimen for their patients.
However, some of its recommendations for cancer treatment have been questionable, according to a new investigative report by STAT, which suggests it is not yet ready for prime time. Internal IBM documents, which STAT had access to, showed that Watson often gave erroneous cancer treatment advice and that company medical specialists and customers identified "multiple examples of unsafe and incorrect treatment recommendations" at the same time that IBM was promoting its supercomputer to hospitals and physicians across the globe.
The report certainly gives oncologists pause for thought, and maybe second thoughts before enlisting the help of Watson.
With an almost never ending series of new biomarkers and mutations being detected and a dazzling array of new therapies pouring out of the pipeline, choosing a cancer treatment regimen can take an arduous amount of time.
In a recent report, the supercomputer was shown to significantly speed up the process of analyzing whole-genome sequencing, which is a "key bottleneck in cancer genomics." It took IBM Watson 10 minutes to come up with conclusions that were similar to those reached by a team of experts after 160 hours of analysis.
However, when implemented in real-world oncology settings, Watson fares less well, according to the STAT report. Part of the problem appears to stem from how the system was trained. IBM's internal documents largely blame the problems on the training of Watson by IBM engineers and oncologists at Memorial Sloan Kettering Cancer Center in New York City, who had been tapped to train Watson in 2012. The resulting software was drilled with a small number of "synthetic" cancer cases, or hypothetical patients, instead of real patient data. Thus, Watson was trained on the basis of the expertise of a few specialists for each cancer type, rather than from guidelines or evidence.
STAT also noted that product information posted on the IBM website implies that Watson is continuing to be trained using real patient data and says that the supercomputer "analyzes patient data against thousands of historical cases and insights gleaned from thousands of Memorial Sloan Kettering MD and analyst hours." However, the number of cases for each of the eight cancers that are covered is a small fraction of the grand total, which ranges from 635 cases for lung cancer to 106 for ovarian cancer.
Gloom and Doom?
The Wall Street Journal recently issued its own report about IBM Watson, saying that despite the initial promise and hype, "six years and billions of dollars later, the diagnosis for Watson is gloomy."
Echoing some of the concerns brought up in the STAT articles, the Wall Street Journal notes that in many cases, the tools did not add much value, and in other cases, Watson was inaccurate. Some problems were that Watson could be "tripped up" by a lack of data for rare or recurring cancers and that treatments were evolving faster than Watson's human "trainers" were able to update the supercomputer. They also pointed out that thus far, there is no published research demonstrating that Watson has improved patient
Watson for Genomics has been piloted at a number of US cancer centers, but according to the Wall Street Journal, physicians at several centers have reported that results were not always accurate, and when they were, it was often information that oncologists already knew.
"The discomfort that I have — and that others have had with using it — has been the sense that you never know what you're really going to get...and how much faith you can put in those results," Lukas Wartman, MD, of the McDonnell Genome Institute at the Washington University School of Medicine in St. Louis, Missouri, told the newspaper. He also said that even though he has complimentary access to Watson, he rarely uses it.
IBM Responds
IBM has taken issue with the negative media reports that were published recently. It singled out the Wall Street Journal article specifically.
In an article entitled "Watson Health: Setting the Record Straight" posted on the company website, John E. Kelly III, MD, IBM senior vice president, Cognitive Solutions and IBM Research, writes that the media reports distort and ignore facts when they suggest that IBM has not made "enough" progress in bringing the benefits of artificial intelligence (AI) to healthcare.
"It is true, as the article reports, that we at IBM have placed a big bet on healthcare," Kelly writes. He says that IBM has done this for two reasons: "AI can make a big difference in solving medical challenges and supporting the work of the healthcare industry," he writes, and also there is an "enormous business opportunity in this area as the adoption of AI increases." IBM has built three distinct cancer tools, Kelly notes:
Watson for Oncology, which offers treatment recommendations/options that can augment an oncologist's own expertise,
Watson for Clinical Trial Matching, which helps match patients to clinical trials, and
Watson for Genomics, which uses genetic sequencing to provide information that can be used to personalize treatment for cancer patients.
"Together, they are now in use at 230 hospitals and health organizations globally and have nearly doubled the number of patients they've reached in the first six months of the year to 84,000," he writes.
Kelly also addresses the allegation that there has been no benefit to patients. "To suggest there has been no patient benefit is to ignore both what we know the Wall Street Journal was told by a number of physicians around the world and these institutions' own public comments — which we believe speak for themselves," he writes.
These were the examples that Kelly listed to support this statement:
Mayo Clinic physicians presented a poster at the annual meeting of the American Society of Clinical Oncology in which they reported that Watson for Clinical Trial Matching boosted enrollment in breast cancer trials by 80%, to 6.3 patients/month, up from 3.5 patients/month, during an 18-month period following its implementation.
Thaddeus Beck, MD, and colleagues at the Highland Oncology Group in Arkansas reported that Watson Clinical Trial Matching reduced the time for clinical trials matching by 78%.
Mark Kris, MD, and oncologists at the Memorial Sloan Kettering Cancer Center have helped to train Watson for Oncology regarding 13 cancers that represent 80% of global cancer incidence and prevalence.
S. P. Somashekhar, MBBS, and Manipal Hospital, Bangalore, India, reported a 93% concordance rate in breast cancer for their multidisciplinary tumor board in an article published in the Annals of Oncology earlier this year. They recently stated that their multidisciplinary tumor board uses Watson for Oncology for all of their complex cases and that it is changing their treatment recommendations in 9% to 11% of cases.
Michael Kelley, MD, and the Department of Veterans Affairs recently extended their contract for Watson for Genomics. Thus far, nearly 3000 veterans with stage IV cancer have been supported by this tool.
William Kim, MD, and the University of North Carolina Lineberger Cancer Center, Chapel Hill, reported that Watson for Genomics found new, actionable mutations in 32% of patients.
Oncologist Dilemma
So what should oncologists do with the extra information provided by the supercomputer?
Approached for comment, Nigam Shah, MBBS, PhD, associate professor of medicine and biomedical data science at Stanford University, California, said that he doesn't think there is much to debate about, given that the system is not being trained using real patient data.
"If I was an oncologist, I'd want to see their system validated in the same way we validate other interventions in medicine, such as with a prospective study, either with or without randomization, depending on the nature of the intervention," Shah told Medscape Medical News.
As an example, he suggested that a medical center could run IBM Watson in silent mode. The computer would be asked to assess and opine on every case the medical center sees. Its recommendations would be recorded, but not acted upon. The center would then assess how often Watson's recommendations corresponded with what the physicians did. "If that test establishes safety, then I'd start showing the recommendations and acting on them," Shah said, "then follow patients over time to see if those that got a Watson-generated recommendation did better than those who didn't."
But for now, he reiterated that if he were a practicing oncologist, "I'd just ignore the noise and chatter and wait for more proof points."
Shah added that IBM Watson should clearly disclose the data upon which the system is trained. "Any AI system is only as good as the data it gets to train on," he explained. "If the system is trained on idealized patient records generated by a small set of doctors, it is improper to say it is trained from real patient data at Sloan Kettering."
Michael Hogarth, MD, a professor in the Division of Biomedical Informatics, Department of Internal Medicine, the University of California, San Diego, said that although he is not a legal expert, he believes that there is a fair amount of case law that indicates that the physician using such a system is fully responsible for its use (ie, the system itself is not culpable).
"This is no different than a physician relying on a reference textbook that has misprinted or wrongly characterized something and using that information and having a bad patient outcome as a result," he explained. "Ultimately, the treating physician is 'always' responsible for the decision making — they can't blame a computer, journal article, or book for making them do the wrong thing."
Hogarth noted that he and many others in his discipline of health informatics were a bit skeptical of IBM when the company claimed Watson could improve oncologist decision making. Essentially, the reason is that specialists such as oncologists are experts in their fields, and some are "super experts" regarding specific conditions within their discipline and treat only patients with those conditions.
Such specialization has become very common in healthcare. It has been shown that clinical decision support systems tend to offer the most assistance to physicians who are not specialists of the condition at hand. "The more 'general' the physician, the more a clinical decision support system focused on specialized content — like IBM Watson Oncology — might surface up information that is not known by that physician," explained Hogarth. "When it comes to specialists, the amount of relevant knowledge is less overwhelming for the specialist, who has less to retain, since they don't have to know everything about every health condition and only need to know about the conditions they see in their specialty."
Hogarth added that the most concerning thing is that there is much talk about "machine learning" tools being used at the bedside for diagnosis and treatment decision making. "That's fine, but they still need to be validated," he emphasized. "Many of them are built using data from EHRs [electronic health records], and a lot of that data is incomplete in terms of knowing everything about a patient, since many patients get care in multiple health systems, so nobody has a complete record on them."
The notion of validating machine learning–based diagnostic aids is just starting to be discussed. "The question is whether it should be regulated as a medical device," he said.
Another physician who was approached for comment cautioned that oncologists need to be aware of where Watson's data are coming. "One thing that has to be emphasized is that Watson's output suffers a huge bias related to the fact that it's all Memorial Sloan Kettering doctors who are training Watson," David H. Gorski, MD, PhD, professor and chief, Breast Surgery Section, Wayne State University School of Medicine, Detroit, Michigan, told Medscape Medical News.
"That means Watson is basically the MSKCC way, which might or not be the right way in every case," he explained. "Remember, academic physicians approach the medical literature with their own biases and interpret it in light of them, and that's why we can see differing recommendations coming from different institutions."
He added that that is also why he wishes that there was a much broader base of physicians training Watson and feeding it studies. "In the end, Watson is a tool, nothing more," said Gorski. "It's a recommendation for treatment that suffers all the flaws of the medical literature and the specific doctors who taught it how to derive recommendations from that literature.
"If a doctor keeps that in mind and isn't afraid to overrule Watson when he or she thinks it's wrong," he added, "it certainly can be potentially useful."
Not Ready for Prime Time
STAT published an article in September 2017 about some of the problems that IBM was having with Watson. It reported that Watson was "still struggling with the basic step of learning about different forms of cancer. Only a few dozen hospitals have adopted the system, which is a long way from IBM's goal of establishing dominance in a multibillion-dollar market."
For that article, STAT conducted numerous interviews with various stakeholders, including physicians, IBM executives, and experts in AI. It also assessed its use, marketing, and performance in hospitals across the globe. The interviews suggested that "IBM, in its rush to bolster flagging revenue, unleashed a product without fully assessing the challenges of deploying it in hospitals globally," and as a result, "its flaws are getting exposed on the front lines of care by doctors and researchers who say that the system, while promising in some respects, remains undeveloped."
"Watson for Oncology is in their toddler stage, and we have to wait and actively engage, hopefully to help them grow healthy," said Taewoo Kang, MD, a South Korean cancer specialist who has used the product and was quoted in the STAT article.
Earlier this year, IBM confirmed rumors of company layoffs, but denied that the cuts affected 50% to 70% of the workforce at its Watson Health operation, as had been reported. IBM has not reported the actual number of layoffs.
"IBM is continuing to reposition our team to focus on the high-value segments of the IT market, and we continue to hire aggressively in critical new areas that deliver value for our clients and IBM. This activity affects a small percentage of our Watson Health workforce, as we move to more technology-intensive offerings, simplified processes, and automation to drive speed," IBM told Medscape Medical News in a statement earlier this year.
Hogarth commented in an interview with Medscape Medical News: "I understand that IBM Watson has laid off/moved many technical staff that were working on IBM Watson Health, so I think they discovered it was not as easy as they thought, plus actually improving decision making is not a trivial maneuver.... It involves many factors beyond just supplying summarized information."
Hogarth believes that had IBM put its sights on a more modest goal, it could have achieved it. The bar that IBM set Watson Health, as well as its expectations, were quite high and were perhaps misplaced or mistargeted. "Instead of creating a system for oncologists, they probably should have focused on a system that can assist general internists, family physicians, and other midlevel extenders and focused its help on patients with enigmatic diagnoses or patterns of symptoms and signs that are not frequently seen by generalists," he said. "That is where an engine like IBM Watson can potentially provide significant value."
IBM recently reported that it will be modifying its Watson software to better reflect geographic differences in cancer treatment, according to the most recent article from STAT about this topic. To date, Watson Oncology has made the most headway in Asia, and most of the hospitals currently using Watson are based outside of the United States. Some physicians have complained that Watson's "decisions" do not reflect treatment protocols in their country and have expressed dissatisfaction with its American bias, that article reports.
At an internal meeting for Watson Health employees worldwide, IBM announced that it would begin using data from real patients for the first time, and it recommended that treatments incorporate more localized treatment advice.
sábado, 28 de julio de 2018
Metformin in cancer prevention and therapy
Ann Transl Med. 2014 Jun; 2(6): 57.
doi: 10.3978/j.issn.2305-5839.2014.06.01
PMCID: PMC4200668
PMID: 25333032
Metformin in cancer prevention and therapy
Jacek Kasznicki,corresponding author Agnieszka Sliwinska, and Józef Drzewoski
Abstract
The prevalence of diabetes is dramatically increasing worldwide. The results of numerous epidemiological studies indicate that diabetic population is not only at increased risk of cardiovascular complications, but also at substantially higher risk of many forms of malignancies. The use of metformin, the most commonly prescribed drug for type 2 diabetes, was repeatedly associated with the decreased risk of the occurrence of various types of cancers, especially of pancreas and colon and hepatocellular carcinoma. This observation was also confirmed by the results of numerous meta-analyses. There are however, several unanswered questions regarding the exact mechanism of the anticancer effect of metformin as well as its activity against various types of cancer both in diabetic and nondiabetic populations. In the present work we discuss the proposed mechanism(s) of anticancer effect of metformin and preclinical and clinical data suggesting its anticancer effect in different populations.
The prevalence of diabetes is dramatically increasing worldwide reaching epidemic proportion. Landmark of diabetes, chronic hyperglycemia leads to the development and progression of life-treating complications, predominantly cardiovascular. The results of several studies indicate that people with diabetes (mainly type 2, T2DM) are also at substantially higher risk of cancer of the pancreas, liver, endometrium, breast, colon, rectum and urinary bladder compared to individuals without this chronic disease (1).
However, the incidence of other types of cancer (e.g., lung, kidney, non-Hodgkin lymphomas) does not seem to be strongly associated with diabetes or the evidence is inconclusive (2). Interestingly enough, it has been suggested that diabetes is associated with a lower risk for prostate cancer (2,3). According to the American Diabetes Association and the American Cancer Society consensus report the relative risks imparted by diabetes are greatest (about two fold or higher) for cancers of the liver, pancreas, and endometrium, and lesser (about 1.2-1.5 fold) for cancers of the colon and rectum, breast, and bladder (2).
Clinical observations indicate that the prevalence of diabetes in newly diagnosed cancer patients ranges from 8% to 18%, suggesting bidirectional association between these two disease (4,5). The association of diabetes and cancer was first reported as an incidental finding in 1932 (6). Nowadays, this coexistence is well recognized, however in spite of the intensive studies its mechanism still remains unclear. There is a general agreement that T2DM and cancer share several common potential risk factors (e.g., aging, sex, obesity, physical inactivity, diet, alcohol, and smoking).
In T2DM, insulin resistance and hyperinsulinemia (either endogenous due to insulin resistance or induced by administration of exogenous insulin formulations) are considered to be independent risk factors for cancer development (1,2). In addition, hyperglycemia-related oxidative stress, accumulation of advances glycation end products as well as low-grade inflammation may also enhance the risk of malignant transformation (7,8).
Recent publications have also suggested the link between hypoglycemic medications and cancer (8-11). The results of numerous preclinical, epidemiological and clinical studies suggested that metformin use is associated with inhibition of cancer cell growth and proliferation and reduction in all-cancer incidents in comparison with users of other hypoglycemic drugs. In the present work we discuss the proposed mechanism(s) of anticancer effect of metformin as well as preclinical and clinical data suggesting this beneficial effect.
Molecular action of metformin in cancer cell
The current proposed anticancer molecular action of metformin is mainly associated with the inhibition of the mammalian target of rapamycin complex 1 (mTORC1). The mTOR pathway plays a pivotal role in metabolism, growth and proliferation of cancer cell (12). Metformin is thought to inhibit mTORC1 pathway (Figure 1).
It is believed that systemic effect of metformin manifested by the reduction of circulating level of insulin and insulin-like growth factor 1 (IGF-1) might be associated with anticancer action (13).
Insulin/IGF-1 is involved not only in regulation of glucose uptake but also in carcinogenesis through upregulation of insulin/IGF receptor signaling pathway (14).
The excessive food consumption (insulin) leads to increased liver production of IGF-1 that binds to IGF-1 receptor and insulin receptor.
Then, through insulin receptor substrate (IRS) the signal is transmitted to phosphoinositide 3-kinase (PI3K), and Akt/protein kinase B (PKB) that indirectly activates (not phosphorylates) mTORC1.
Additionally, insulin receptor through growth factor receptor-bound protein 2 (GRB2) propagates signal to Ras/Raf/ERK pathway that drives cell growth. Evidences indicate that these pathways play important role in changes of cellular metabolism that are typical feature of tumor cells (15).
Increased levels of circulating insulin/IGF1 and upregulation of insulin/IGF receptor signaling pathways were demonstrated to be involved in the formation of many types of cancer.
Metformin was found to reduce insulin level, inhibit insulin/IGF signaling pathways, and modify cellular metabolism in normal and cancer cells (16).
Evidences suggest that the inhibition of mTOR pathway by metformin proceeds dependent and independent on AMP-activated protein kinase (AMPK) activation.
AMPK phosphorylates tuberous sclerosis complex protein 2 (TSC2) that inhibits mTORC1 leading to decrease in protein synthesis and cell growth (17).
Among the first studies that showed the participation of AMPK activation in antitumor action of metformin were researches performed on breast cancer cells (18,19). Dowling et al. showed that compound C, an inhibitor of AMPK, reversed inhibition of initiation of translation evoked by metformin (18).
More recently, Mohammed et al. showed reduction of carcinoma spread in pancreas of transgenic mice fed with metformin (20). Additionally, pancreatic tissue of mice fed with metformin revealed a significant inhibition of mTOR, and an increase of phosphorylated AMPK and TSC2 (20). However, Gwinn el al. demonstrated that inhibition of mTOR could be independent on TSC2, since AMPK directly phosphorylates the rotor compartment of mTOR (21).
Several studies identified that liver kinase B1 (LKB1), a major upstream kinase of AMPK, may be involved in anticancer action of metfromin associated with inhibition of mTOR. In vitro and in vivo studies revealed that deletion of LKB1 function accelerated proliferation of tumor cell and sensitized them to activators of AMPK such as biguanide (22-24).
Due to the fact that p53 expression and phosphorylation is regulated by AMPK and p53 is involved in cell metabolism and control of cell cycle its participation in metformin action is discussed.
Growing evidences from in vivo and in vitro studies of various cancers revealed that metformin blocked cell cycle in G0/G1 phase with a significant decrease expression of G1 cyclins (including cyclin D1) without changes in p53 status (25-27).
However, others researches indicated that inhibitory effect on cancer cell growth of metformin was associated with p53 activity (28-31).
Taking together the results of preclinical studies are inconclusive whether antitumor action of metformin is associated with p53. Some investigators hypothesize that the dose of metformin may determine the effect of metformin. Yi et al. demonstrated on hepatoma cells that low concentration of metformin induced p53-dependent senescence, whereas higher doses induced apoptotic cell death (32).
Inhibition of mTOR by metformin independent on AMPK activation was demonstrated by Memmott et al. in mice lung cancer cells (16). Metformin evoked inhibition of mTOR pathway with accompanied decreasing activation of IGF-1/insulin receptor, Akt, extracellular signal-regulated kinase (ERK) without AMPK activation (16).
Kalender et al. demonstrated in Drosophilla cells that inhibition of mTOR signaling induced by metformin occurred in the absence of AMPK. They reveal the existence of an alternative TSC1/2-mTOR AMPK-independent pathway mediated by RAG GTPase (33).
Metformin was found to inhibit breast carcinoma cell growth through decreasing level of epidermal growth factor receptor 2 (HER2). This effect was mediated by inhibition of the mTOR effector, p70S6K1 (34).
p70S6K is responsible for the phosphorylation of S6 ribosomal protein and thereby protein synthesis at the ribosome (35). Antiproliferative action of metformin related to enhancement of DNA-damage-inducible transcript 4 protein (DDIT4, REDD1) expression, a negative regulator of mTOR, was reported in prostate cancer cells by Ben Sahra et al. (36).
This effect of metformin was also independent on AMPK activation (36).
The results of preclinical studies undoubtedly confirm the efficacy of metformin to inhibit cancer cell growth in vitro and to reduce tumor spread in animal models of various cancers. However, it should be stressed that molecular action of metformin is still investigated and seems to be affected by the type of tumor cell line.
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