HistoMIL: A Python package for training multiple instance learning models on histopathology slides

Hematoxylin and eosin (H&E) stained slides are widely used in disease diagnosis. Remarkable advances in deep learning have made it possible to detect complex molecular patterns in these histopathology slides, suggesting automated approaches could help inform pathologists’ decisions. Multiple instance learning (MIL) algorithms have shown promise in this context, outperforming transfer learning (TL) methods for various tasks, but their implementation and usage remains complex. We introduce HistoMIL, a Python package designed to streamline the implementation, training and inference process of MIL-based algorithms for computational pathologists and biomedical researchers. It integrates a self-supervised learning module for feature encoding, and a full pipeline encompassing TL and three MIL algorithms: ABMIL, DSMIL, and TransMIL. The PyTorch Lightning framework enables effortless customization and algorithm implementation. We illustrate HistoMIL's capabilities by building predictive models for 2,487 cancer hallmark genes on breast cancer histology slides, achieving AUROC performances of up to 85%

Semi-supervised ViT knowledge distillation network with style transfer normalization for colorectal liver metastases survival prediction

Colorectal liver metastases (CLM) significantly impact colon cancer patients, influencing survival based on systemic chemotherapy response. Traditional methods like tumor grading scores (e.g., tumor regression grade - TRG) for prognosis suffer from subjectivity, time constraints, and expertise demands. Current machine learning approaches often focus on radiological data, yet the relevance of histological images for survival predictions, capturing intricate tumor microenvironment characteristics, is gaining recognition. To address these limitations, we propose an end-to-end approach for automated prognosis prediction using histology slides stained with H&E and HPS. We first employ a Generative Adversarial Network (GAN) for slide normalization to reduce staining variations and improve the overall quality of the images that are used as input to our prediction pipeline. We propose a semi-supervised model to perform tissue classification from sparse annotations, producing feature maps. We use an attention-based approach that weighs the importance of different slide regions in producing the final classification results. We exploit the extracted features for the metastatic nodules and surrounding tissue to train a prognosis model. In parallel, we train a vision Transformer (ViT) in a knowledge distillation framework to replicate and enhance the performance of the prognosis prediction. In our evaluation on a clinical dataset of 258 patients, our approach demonstrates superior performance with c-indexes of 0.804 (0.014) for OS and 0.733 (0.014) for TTR. Achieving 86.9% to 90.3% accuracy in predicting TRG dichotomization and 78.5% to 82.1% accuracy for the 3-class TRG classification task, our approach outperforms comparative methods. Our proposed pipeline can provide automated prognosis for pathologists and oncologists, and can greatly promote precision medicine progress in managing CLM patients.Comment: 16 pages, 7 figures and 7 tables. Submitted to Medical Journal Analysis (MedIA) journa

Artificial Intelligence for Digital and Computational Pathology

Advances in digitizing tissue slides and the fast-paced progress in artificial intelligence, including deep learning, have boosted the field of computational pathology. This field holds tremendous potential to automate clinical diagnosis, predict patient prognosis and response to therapy, and discover new morphological biomarkers from tissue images. Some of these artificial intelligence-based systems are now getting approved to assist clinical diagnosis; however, technical barriers remain for their widespread clinical adoption and integration as a research tool. This Review consolidates recent methodological advances in computational pathology for predicting clinical end points in whole-slide images and highlights how these developments enable the automation of clinical practice and the discovery of new biomarkers. We then provide future perspectives as the field expands into a broader range of clinical and research tasks with increasingly diverse modalities of clinical data

Machine Learning Approaches to Predict Recurrence of Aggressive Tumors

Cancer recurrence is the major cause of cancer mortality. Despite tremendous research efforts, there is a dearth of biomarkers that reliably predict risk of cancer recurrence. Currently available biomarkers and tools in the clinic have limited usefulness to accurately identify patients with a higher risk of recurrence. Consequently, cancer patients suffer either from under- or over- treatment. Recent advances in machine learning and image analysis have facilitated development of techniques that translate digital images of tumors into rich source of new data. Leveraging these computational advances, my work addresses the unmet need to find risk-predictive biomarkers for Triple Negative Breast Cancer (TNBC), Ductal Carcinoma in-situ (DCIS), and Pancreatic Neuroendocrine Tumors (PanNETs). I have developed unique, clinically facile, models that determine the risk of recurrence, either local, invasive, or metastatic in these tumors. All models employ hematoxylin and eosin (H&E) stained digitized images of patient tumor samples as the primary source of data. The TNBC (n=322) models identified unique signatures from a panel of 133 protein biomarkers, relevant to breast cancer, to predict site of metastasis (brain, lung, liver, or bone) for TNBC patients. Even our least significant model (bone metastasis) offered superior prognostic value than clinopathological variables (Hazard Ratio [HR] of 5.123 vs. 1.397 p\u3c0.05). A second model predicted 10-year recurrence risk, in women with DCIS treated with breast conserving surgery, by identifying prognostically relevant features of tumor architecture from digitized H&E slides (n=344), using a novel two-step classification approach. In the validation cohort, our DCIS model provided a significantly higher HR (6.39) versus any clinopathological marker (p\u3c0.05). The third model is a deep-learning based, multi-label (annotation followed by metastasis association), whole slide image analysis pipeline (n=90) that identified a PanNET high risk group with over an 8x higher risk of metastasis (versus the low risk group p\u3c0.05), regardless of cofounding clinical variables. These machine-learning based models may guide treatment decisions and demonstrate proof-of-principle that computational pathology has tremendous clinical utility