High-quality medical data is critical to the development and implementation of machine learning (ML) algorithms in healthcare; however, security, and privacy concerns continue to limit access. We sought to determine the utility of “synthetic data” in training ML algorithms for the detection of tuberculosis (TB) from inflammatory biomarker profiles. A retrospective dataset (A) comprised of 278 patients was used to generate synthetic datasets (B, C, and D) for training models prior to secondary validation on a generalization dataset. ML models trained and validated on the Dataset A (real) demonstrated an accuracy of 90%, a sensitivity of 89% (95% CI, 83–94%), and a specificity of 100% (95% CI, 81–100%). Models trained using the optimal synthetic dataset B showed an accuracy of 91%, a sensitivity of 93% (95% CI, 87–96%), and a specificity of 77% (95% CI, 50–93%). Synthetic datasets C and D displayed diminished performance measures (respective accuracies of 71% and 54%). This pilot study highlights the promise of synthetic data as an expedited means for ML algorithm development.

Background: Prediction of clinical outcomes for individual cancer patients is an important step in the disease diagnosis and subsequently guides the treatment and patient counseling. In this work, we develop and evaluate a joint outcome and biomarker supervised (estrogen receptor expression and ERBB2 expression and gene amplification) multitask deep learning model for prediction of outcome in breast cancer patients in two nation-wide multicenter studies in Finland (the FinProg and FinHer studies). Our approach combines deep learning with expert knowledge to provide more accurate, robust, and integrated prediction of breast cancer outcomes. Materials and Methods: Using deep learning, we trained convolutional neural networks (CNNs) with digitized tissue microarray (TMA) samples of primary hematoxylin-eosin-stained breast cancer specimens from 693 patients in the FinProg series as input and breast cancer-specific survival as the endpoint. The trained algorithms were tested on 354 TMA patient samples in the same series. An independent set of whole-slide (WS) tumor samples from 674 patients in another multicenter study (FinHer) was used to validate and verify the generalization of the outcome prediction based on CNN models by Cox survival regression and concordance index (c-index). Visual cancer tissue characterization, i.e., number of mitoses, tubules, nuclear pleomorphism, tumor-infiltrating lymphocytes, and necrosis was performed on TMA samples in the FinProg test set by a pathologist and combined with deep learning-based outcome prediction in a multitask algorithm. Results: The multitask algorithm achieved a hazard ratio (HR) of 2.0 (95% confidence interval [CI] 1.30–3.00), P < 0.001, c-index of 0.59 on the 354 test set of FinProg patients, and an HR of 1.7 (95% CI 1.2–2.6), P = 0.003, c-index 0.57 on the WS tumor samples from 674 patients in the independent FinHer series. The multitask CNN remained a statistically independent predictor of survival in both test sets when adjusted for histological grade, tumor size, and axillary lymph node status in a multivariate Cox analyses. An improved accuracy (c-index 0.66) was achieved when deep learning was combined with the tissue characteristics assessed visually by a pathologist. Conclusions: A multitask deep learning algorithm supervised by both patient outcome and biomarker status learned features in basic tissue morphology predictive of survival in a nationwide, multicenter series of patients with breast cancer. The algorithms generalized to another independent multicenter patient series and whole-slide breast cancer samples and provide prognostic information complementary to that of a comprehensive series of established prognostic factors.

Background: The objective of the project was to evaluate the feasibility of introducing a single-networked digital histopathology reporting platform in the Southwest Peninsula region of England by allowing pathologists to experience the technology and recording their perceptions. This information was then used in planning future service development. The project was funded by the National Health Service (NHS) Peninsula Cancer Alliance and took place in 2020 during the COVID-19 pandemic. Materials and Methods: Digital slides of 500 cases from Taunton were reported remotely in Truro, Plymouth, Exeter, Bristol, or Bath by using a single remote reporting platform located on the secure Health and Social Care Network (HSCN) that links NHS sites. These were mainly small gastrointestinal, skin, and gynecological specimens. The digital diagnoses were compared with the diagnoses issued on reporting the glass slides. At the end of the project, the pathologists completed a Google Forms questionnaire of their perceptions of digital pathology. The results were presented at a meeting with the funder and discussed. Results: From the 500 cases there were nine cases of significant diagnostic discrepancy, seven of which involved the misrecognition of Helicobacter pylori in gastric biopsies. The questionnaire at the end of the project showed that there was a general agreement that the platform was easy to use, and the image quality was acceptable. It was agreed that extra work, such as deeper levels, was easy to request on the software platform. Most pathologists did not agree that digital reporting was quicker than glass slide reporting. Some were less confident in their digital diagnoses than glass diagnoses. They agreed that some types of specimens cannot easily be reported digitally. All users indicated that they would like to report at least half of their work digitally in the future if they could, and all strongly agreed that digital pathology would improve access to expert opinions, teaching, and multidisciplinary meetings. It was difficult to find pathologists with time to undertake remote digital reporting, in addition to their existing commitments. Conclusions: Overall, the pathologists developed a positive perception of digital pathology and wished to continue using it.