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The rise of AI in medicine

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By now, it's almost old news that artificial intelligence (AI) will have a transformative role in medicine. Algorithms have the potential to work tirelessly, at faster rates and now with potentially greater accuracy than clinicians. In 2016, it was predicted that'machine learning will displace much of the work of radiologists and anatomical pathologists'. In the same year, a University of Toronto professor controversially announced that'we should stop training radiologists now'. But is it really the beginning of the end for some medical specialties?


Automated histologic diagnosis of CNS tumors with machine learning

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A new mass discovered in the CNS is a common reason for referral to a neurosurgeon. CNS masses are typically discovered on MRI or computed tomography (CT) scans after a patient presents with new neurologic symptoms. Presenting symptoms depend on the location of the tumor and can include headaches, seizures, difficulty expressing or comprehending language, weakness affecting extremities, sensory changes, bowel or bladder dysfunction, gait and balance changes, vision changes, hearing loss and endocrine dysfunction. A mass in the CNS has a broad differential diagnosis, including tumor, infection, inflammatory or demyelinating process, infarct, hemorrhage, vascular malformation and radiation treatment effect. The most likely diagnoses can be narrowed based on patient demographics, medical history, imaging characteristics and adjunctive laboratory studies. However, accurate histopathologic interpretation of tissue obtained at the time of surgery is frequently required to make a diagnosis and guide intraoperative decision making. Over half of CNS tumors in adults are metastases from systemic cancer originating elsewhere in the body [1]. An estimated 9.6% of adults with lung cancer, melanoma, breast cancer, renal cell carcinoma and colorectal cancer have brain metastases [2].



Artificial intelligence brings pancreatic cancer screening one step closer to reality

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Artificial intelligence (AI) holds promise for enabling earlier detection of pancreatic cancer, which is crucial to saving lives. The potential of AI is showcased in a study to be presented at the ESMO World Congress on Gastrointestinal Cancer, 1–4 July 2020. Overall, 12 in every 100,000 people develop pancreatic cancer. This means that screening everyone would be inefficient and would expose many people to unnecessary tests and potential side-effects. Between 70-80% of patients are diagnosed at an advanced stage when it is too late for curative treatment and five years after diagnosis, just 6% of patients have survived.


Feasibility of blood testing combined with PET-CT to screen for cancer and guide intervention

Science

Cancers diagnosed early are often more responsive to treatment. Blood tests that detect molecular markers of cancer have successfully identified individuals already known to have the disease. Lennon et al. conducted an exploratory study that more closely reflects the way in which such blood tests would be used in the future. They evaluated the feasibility and safety of incorporating a multicancer blood test into the routine clinical care of 10,000 women with no history of cancer. Over a 12-month period, the blood test detected 26 cancers of different types. A combination of the blood test and positron emission tomography–computed tomography (PET-CT) imaging led to surgical removal of nine of these cancers. Use of the blood test did not result in a large number of futile follow-up procedures. Science , this issue p. [eabb9601][1] ### INTRODUCTION The goal of earlier cancer detection is to identify the disease at a stage when it can be effectively treated, thereby offering the patient a better chance of long-term survival. Adherence to screening modalities known to decrease cancer mortality such as colonoscopy, mammography, low-dose computed tomography, and Pap smears varies widely. Moreover, the majority of cancer types are diagnosed only when symptoms occur. Multicancer blood tests offer the exciting possibility of detecting many cancer types at a relatively early stage and in a minimally invasive manner. ### RATIONALE Evaluation of the feasibility and safety of multicancer blood testing requires prospective interventional studies. We designed such a study to answer four critical questions: (i) Can a multicancer blood test detect cancers not previously detected by other means? (ii) Can a positive test result lead to surgical intervention with curative intent? (iii) Can testing be incorporated into routine clinical care and not discourage patients from undergoing recommended screening tests such as mammography? (iv) Can testing be performed safely, without incurring a large number of unnecessary, invasive follow-up tests? ### RESULTS We evaluated a blood test that detects DNA mutations and protein biomarkers of cancer in a prospective, interventional study of 10,006 women who were 65 to 75 years old and who had no prior history of cancer. Positive blood tests were followed by diagnostic positron emission tomography–computed tomography (PET-CT), which served to independently confirm and precisely localize the site and extent of disease if present. The study design incorporated several features to maximize the safety of testing to the participants. Of the 10,006 enrollees, 9911 (99.1%) could be assessed with respect to the four questions posed above. (i) Detection: Of 96 cancers incident during the study period, 26 were first detected by blood testing and 24 additional cancers by conventional screening. Fifteen of the 26 patients in whom cancer was first detected by blood testing underwent PET-CT imaging, and 11 patients developed signs or symptoms of cancer after the blood test that led to imaging procedures other than PET-CT. The specificity and positive predictive value (PPV) of blood testing alone were 98.9% and 19.4%, respectively, and combined with PET-CT, the specificity and PPV increased to 99.6% and 28.3%. The blood test first detected 14 of 45 cancers (31%) in seven organs for which no standard-of-care screening test is available. (ii) Intervention: Of the 26 cancers first detected by blood testing, 17 (65%) had localized or regional disease. Of the 15 participants with positive blood tests as well as positive PET-CT scans, 9 (60%) underwent surgery with curative intent. (iii) Incorporation into clinical care: Blood testing could be combined with conventional screening, leading to detection of more than half of the total incident cancers observed during the study period. Blood testing did not deter participants from undergoing mammography, and surveys revealed that 99% of participants would join a similar, subsequent study if offered. (iv) Safety: 99% of participants did not require any follow-up of blood testing results, and only 0.22% underwent an unnecessary invasive diagnostic procedure as a result of a false-positive blood test. ### CONCLUSION A minimally invasive blood test in combination with PET-CT can safely detect and precisely localize several types of cancers in individuals not previously known to have cancer, in some cases enabling treatment with intent to cure. Further studies will be required to assess the clinical utility, risk-benefit ratio, and cost-effectiveness of such testing. ![Figure][2] Overview of cancers detected by blood testing. Twenty-six cancers (blue dots) in 10 organs were first detected by blood testing. The blue dots with the red halo represent 12 of the 26 cancers that were surgically treated with intent to cure. Nine of these 12 were detected by the combination of the blood test and PET-CT, with the remaining three identified by the blood test combined with another imaging modality. Cancer treatments are often more successful when the disease is detected early. We evaluated the feasibility and safety of multicancer blood testing coupled with positron emission tomography–computed tomography (PET-CT) imaging to detect cancer in a prospective, interventional study of 10,006 women not previously known to have cancer. Positive blood tests were independently confirmed by a diagnostic PET-CT, which also localized the cancer. Twenty-six cancers were detected by blood testing. Of these, 15 underwent PET-CT imaging and nine (60%) were surgically excised. Twenty-four additional cancers were detected by standard-of-care screening and 46 by neither approach. One percent of participants underwent PET-CT imaging based on false-positive blood tests, and 0.22% underwent a futile invasive diagnostic procedure. These data demonstrate that multicancer blood testing combined with PET-CT can be safely incorporated into routine clinical care, in some cases leading to surgery with intent to cure. [1]: /lookup/doi/10.1126/science.abb9601 [2]: pending:yes


UK rolls out AI-based cancer detection for NHS patients

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Leader in AI-powered cancer diagnostics, Ibex Medical Analytics and provider of digital pathology services in the NHS, LDPath, have announced the UK's first rollout of clinical grade AI application for cancer detection in pathology. This platform will support pathologists in enhancing diagnostic accuracy and efficiency. Over the years, a global increase in cancer cases has coincided with a decline in the number of pathologists around the world. Traditional pathology involves manual processes that have remained the same for years. These processes involve slides to be analysed by pathologists using microscopes, and reporting is often carried out on pieces of paper.


Stop training more models, start deploying them - KDnuggets

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The rumours that AI (and ML) will revolutionise healthcare have been around for a while [1]. And yes, we have seen some amazing uses of AI in healthcare [see, e.g., 2,3]. But, in my personal experience, the majority of the models trained in healthcare never make it to practice. Let's see why (or, scroll down and see how we solve it). Note: The statement "the majority of the models trained in … never make it to practice" is probably true across disciplines. Healthcare happens to be the one I am sure about.


Human-Artificial intelligence collaborations best for skin cancer diagnosis

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Artificial intelligence (AI) improved skin cancer diagnostic accuracy when used in collaboration with human clinical checks, an international study including University of Queensland researchers has found. The global team tested for the first time whether a'real world', collaborative approach involving clinicians assisted by AI improved the accuracy of skin cancer clinical decision making. UQ's Professor Monika Janda said the highest diagnostic accuracy was achieved when crowd wisdom and AI predictions were combined, suggesting human-AI and crowd-AI collaborations were preferable to individual experts or AI alone "This is important because AI decision support has slowly started to infiltrate healthcare settings, and yet few studies have tested its performance in real world settings or how clinicians interact with it," Professor Janda said. "Inexperienced evaluators gained the highest benefit from AI decision support and expert evaluators confident in skin cancer diagnosis achieved modest or no benefit. "These findings indicated a combined AI-human approach to skin cancer diagnosis may be the most relevant for clinicians in the future." Although AI diagnostic software has demonstrated expert level accuracy in several image-based medical studies, researchers have remained unclear on whether its use improved clinical practice. "Our study found that good quality AI support was useful to clinicians but needed to be simple, concrete, and in accordance with a given task," Professor Janda said. "For clinicians of the future this means that AI-based screening and diagnosis might soon be available to support them on a daily basis.


AUA 2020: Automated Performance Metrics to Predict Continence Recovery After Robotic Radical Prostatectomy Utilizing Machine Learning

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The robot-assisted radical prostatectomy was segmented into 12 steps, and for each step, 41 validated automated performance metrics were reported. The predictive models were trained with three data sets: 1) 492 automated performance metrics; 2) 16 clinicopathological data (for example prostate volume, Gleason score); 3) automated performance metrics plus clinicopathological data. The authors utilized a random forest model (800 trees) to predict continence recovery (no pads or one safety pad) at three and six months after surgery. The prediction accuracy was estimated through a 10-fold cross-validation process. The area under the curve (AUC) and standard error (SE) was used to estimate prediction accuracy. Finally, the out-of-bag Gini index was used to rank the variables of importance.