Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer

ConvPath: A software tool for lung adenocarcinoma digital pathological image analysis aided by a convolutional neural network

BACKGROUND

The spatial distributions of different types of cells could reveal a cancer cell's growth pattern, its relationships with the tumor microenvironment and the immune response of the body, all of which represent key "hallmarks of cancer". However, the process by which pathologists manually recognize and localize all the cells in pathology slides is extremely labor intensive and error prone.

METHODS

In this study, we developed an automated cell type classification pipeline, ConvPath, which includes nuclei segmentation, convolutional neural network-based tumor cell, stromal cell, and lymphocyte classification, and extraction of tumor microenvironment-related features for lung cancer pathology images. To facilitate users in leveraging this pipeline for their research, all source scripts for ConvPath software are available at https://qbrc.swmed.edu/projects/cnn/.

FINDINGS

The overall classification accuracy was 92.9% and 90.1% in training and independent testing datasets, respectively. By identifying cells and classifying cell types, this pipeline can convert a pathology image into a "spatial map" of tumor, stromal and lymphocyte cells. From this spatial map, we can extract features that characterize the tumor micro-environment. Based on these features, we developed an image feature-based prognostic model and validated the model in two independent cohorts. The predicted risk group serves as an independent prognostic factor, after adjusting for clinical variables that include age, gender, smoking status, and stage.

INTERPRETATION

The analysis pipeline developed in this study could convert the pathology image into a "spatial map" of tumor cells, stromal cells and lymphocytes. This could greatly facilitate and empower comprehensive analysis of the spatial organization of cells, as well as their roles in tumor progression and metastasis.

Translational Highlights in Breast and Ovarian Cancer 2019 - Immunotherapy, DNA Repair, PI3K Inhibition and CDK4/6 Therapy

In the near future, important translational questions of clinical relevance will be adressed by studies currently in progress. On the one hand, the role of PD-L1 expression must be further understood, after it was found to be relevant in the use of atezolizumab in first-line therapy of patients with metastatic triple-negative breast cancer (TNBC). No association between efficacy and PD-L1 expression was found in a neoadjuvant study that included pembrolizumab in TNBC. The pathological complete response rate (pCR) was higher in both patient groups with and without PD-L1 expression when pembrolizumab was added to chemotherapy. Another future question is the identification of further patient groups in which efficacy of PARP inhibitors is seen, which are licensed for the pBRCA1/2 germline mutation. These include, for example, patients with mutations in other genes, which are involved in homologous recombination, or patients with tumours that show an abnormality in global tests of homologous recombination deficiencies (HRD tests). The question of whether a PARP inhibitor can be given and with which chemotherapy combination partners is currently being investigated in both breast and ovarian cancer. While the data on improved overall survival are being consolidated for the CDK4/6 inhibitors, knowledge of molecular changes during the therapy and during progression on the therapy is growing. Both the accumulation of PI3K mutations and also PTEN changes might play a part in planning subsequent therapies. This review article summarises these recent developments in breast cancer and in part also in ovarian cancer.

Identification of 12 cancer types through genome deep learning

Cancer is a major cause of death worldwide, and an early diagnosis is required for a favorable prognosis. Histological examination is the gold standard for cancer identification; however, large amount of inter-observer variability exists in histological diagnosis. Numerous studies have shown cancer genesis is accompanied by an accumulation of harmful mutations, potentiating the identification of cancer based on genomic information. We have proposed a method, GDL (genome deep learning), to study the relationship between genomic variations and traits based on deep neural networks. We analyzed 6,083 samples’ WES (Whole Exon Sequencing) mutations files from 12 cancer types obtained from the TCGA (The Cancer Genome Atlas) and 1,991 healthy samples’ WES data from the 1000 Genomes project. We constructed 12 specific models to distinguish between certain type of cancer and healthy tissues, a total-specific model that can identify healthy and cancer tissues, and a mixture model to distinguish between all 12 types of cancer based on GDL. We demonstrate that the accuracy of specific, mixture and total specific model are 97.47%, 70.08% and 94.70% for cancer identification. We developed an efficient method for the identification of cancer based on genomic information that offers a new direction for disease diagnosis.

[Basis and perspectives of artificial intelligence in radiation therapy]

Artificial intelligence is a highly polysemic term. In computer science, with the objective of being able to solve totally new problems in new contexts, artificial intelligence includes connectionism (neural networks) for learning and logics for reasoning. Artificial intelligence algorithms mimic tasks normally requiring human intelligence, like deduction, induction, and abduction. All apply to radiation oncology. Combined with radiomics, neural networks have obtained good results in image classification, natural language processing, phenotyping based on electronic health records, and adaptive radiation therapy. General adversial networks have been tested to generate synthetic data. Logics based systems have been developed for providing formal domain ontologies, supporting clinical decision and checking consistency of the systems. Artificial intelligence must integrate both deep learning and logic approaches to perform complex tasks and go beyond the so-called narrow artificial intelligence that is tailored to perform some highly specialized task. Combined together with mechanistic models, artificial intelligence has the potential to provide new tools such as digital twins for precision oncology.

Development and validation of deep learning algorithms for scoliosis screening using back images

Adolescent idiopathic scoliosis is the most common spinal disorder in adolescents with a prevalence of 0.5-5.2% worldwide. The traditional methods for scoliosis screening are easily accessible but require unnecessary referrals and radiography exposure due to their low positive predictive values. The application of deep learning algorithms has the potential to reduce unnecessary referrals and costs in scoliosis screening. Here, we developed and validated deep learning algorithms for automated scoliosis screening using unclothed back images. The accuracies of the algorithms were superior to those of human specialists in detecting scoliosis, detecting cases with a curve ≥20°, and severity grading for both binary classifications and the four-class classification. Our approach can be potentially applied in routine scoliosis screening and periodic follow-ups of pretreatment cases without radiation exposure.

Computer-Aided Diagnosis in Histopathological Images of the Endometrium Using a Convolutional Neural Network and Attention Mechanisms

Uterine cancer (also known as endometrial cancer) can seriously affect the female reproductive system, and histopathological image analysis is the gold standard for diagnosing endometrial cancer. Due to the limited ability to model the complicated relationships between histopathological images and their interpretations, existing computer-aided diagnosis (CAD) approaches using traditional machine learning algorithms often failed to achieve satisfying results. In this study, we develop a CAD approach based on a convolutional neural network (CNN) and attention mechanisms, called HIENet. In the ten-fold cross-validation on ∼3,300 hematoxylin and eosin (H&E) image patches from ∼500 endometrial specimens, HIENet achieved a 76.91 ± 1.17% (mean ± s. d.) accuracy for four classes of endometrial tissue, i.e., normal endometrium, endometrial polyp, endometrial hyperplasia, and endometrial adenocarcinoma. Also, HIENet obtained an area-under-the-curve (AUC) of 0.9579 ± 0.0103 with an 81.04 ± 3.87% sensitivity and 94.78 ± 0.87% specificity in a binary classification task that detected endometrioid adenocarcinoma. Besides, in the external validation on 200 H&E image patches from 50 randomly-selected female patients, HIENet achieved an 84.50% accuracy in the four-class classification task, as well as an AUC of 0.9829 with a 77.97% (95% confidence interval, CI, 65.27%∼87.71%) sensitivity and 100% (95% CI, 97.42%∼100.00%) specificity. The proposed CAD method outperformed three human experts and five CNN-based classifiers regarding overall classification performance. It was also able to provide pathologists better interpretability of diagnoses by highlighting the histopathological correlations of local pixel-level image features to morphological characteristics of endometrial tissue.

An augmented reality microscope with real-time artificial intelligence integration for cancer diagnosis

The microscopic assessment of tissue samples is instrumental for the diagnosis and staging of cancer, and thus guides therapy. However, these assessments demonstrate considerable variability and many regions of the world lack access to trained pathologists. Though artificial intelligence (AI) promises to improve the access and quality of healthcare, the costs of image digitization in pathology and difficulties in deploying AI solutions remain as barriers to real-world use. Here we propose a cost-effective solution: the augmented reality microscope (ARM). The ARM overlays AI-based information onto the current view of the sample in real time, enabling seamless integration of AI into routine workflows. We demonstrate the utility of ARM in the detection of metastatic breast cancer and the identification of prostate cancer, with latency compatible with real-time use. We anticipate that the ARM will remove barriers towards the use of AI designed to improve the accuracy and efficiency of cancer diagnosis.

Machine learning for tissue diagnostics in oncology: brave new world

Machine learning is an exciting technology with broad application in big data analysis, as well as increasingly in specialised healthcare. As a diagnostic tool in tissue workup and pathology, it has the potential for personalised and stratified approaches, but the limitations and pitfalls need to be better understood and characterised especially in this critical area of medical care.

Artificial Intelligence Systems Assisting Oncologists? Resist and Desist or Enlist and Coexist

The use of artificial intelligence (AI) has become an integral part of patient care, but there are concerns about the impact of non‐human decision assistance on patient outcomes. This commentary focuses on how AI can assist oncologists and benefit patients.