Statistical biopsy: An emerging screening approach for early detection of cancers.

Despite large investment cancer continues to be a major source of mortality and morbidity throughout the world. Traditional methods of detection and diagnosis such as biopsy and imaging, tend to be expensive and have risks of complications. As data becomes more abundant and machine learning continues advancing, it is natural to ask how they can help solve some of these problems. In this paper we show that using a person's personal health data it is possible to predict their risk for a wide variety of cancers. We dub this process a "statistical biopsy." Specifically, we train two neural networks, one predicting risk for 16 different cancer types in females and the other predicting risk for 15 different cancer types in males. The networks were trained as binary classifiers identifying individuals that were diagnosed with the different cancer types within 5 years of joining the PLOC trial. However, rather than use the binary output of the classifiers we show that the continuous output can instead be used as a cancer risk allowing a holistic look at an individual's cancer risks. We tested our multi-cancer model on the UK Biobank dataset showing that for most cancers the predictions generalized well and that looking at multiple cancer risks at once from personal health data is a possibility. While the statistical biopsy will not be able to replace traditional biopsies for diagnosing cancers, we hope there can be a shift of paradigm in how statistical models are used in cancer detection moving to something more powerful and more personalized than general population screening guidelines.

Population-Based Screening for Endometrial Cancer: Human vs. Machine Intelligence.

Incidence and mortality rates of endometrial cancer are increasing, leading to increased interest in endometrial cancer risk prediction and stratification to help in screening and prevention. Previous risk models have had moderate success with the area under the curve (AUC) ranging from 0.68 to 0.77. Here we demonstrate a population-based machine learning model for endometrial cancer screening that achieves a testing AUC of 0.96. We train seven machine learning algorithms based solely on personal health data, without any genomic, imaging, biomarkers, or invasive procedures. The data come from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO). We further compare our machine learning model with 15 gynecologic oncologists and primary care physicians in the stratification of endometrial cancer risk for 100 women. We find a random forest model that achieves a testing AUC of 0.96 and a neural network model that achieves a testing AUC of 0.91. We test both models in risk stratification against 15 practicing physicians. Our random forest model is 2.5 times better at identifying above-average risk women with a 2-fold reduction in the false positive rate. Our neural network model is 2 times better at identifying above-average risk women with a 3-fold reduction in the false positive rate. Our machine learning models provide a non-invasive and cost-effective way to identify high-risk sub-populations who may benefit from early screening of endometrial cancer, prior to disease onset. Through statistical biopsy of personal health data, we have identified a new and effective approach for early cancer detection and prevention for individual patients.

Digital Models as an Alternative to Plaster Casts in Assessment of Orthodontic Treatment Outcomes.

OBJECTIVE: This study aimed to compare the use of digital models and plaster casts in assessing the improvement in occlusion following orthodontic treatment. MATERIALS AND METHODS: Digital models and plaster casts of 39 consecutive patients at pre- and posttreatment stages were obtained and assessed using the Peer Assessment Rating (PAR) index and the Index of Complexity and Treatment Need (ICON). PAR and ICON scores were compared at individual and group levels. Categorization of improvement level was compared using Kappa (κ) statistics. RESULTS: There was no significant difference in neither PAR scores (p > 0.05) nor ICON scores (p > 0.05) between digital and plaster cast assessments. The Intraclass Correlation Coefficient (ICC) values for changes in PAR and ICON scores were excellent (ICC > 0.80). Agreement of ratings of occlusal improvement level between digital and plaster model assessments was 0.83 (κ) for PAR and 0.59 (κ) for ICON, respectively. CONCLUSION: The study supported the use of digital models as an alternative to plaster casts when assessing changes in occlusion at the 'individual patient' level using ICON or PAR. However, it could not fully support digital models as an alternate to plaster casts at 'the group level' (as in the case of clinical audit/research).