Leveraging artificial intelligence to predict ERG gene fusion status in prostate cancer.

BACKGROUND: TMPRSS2-ERG gene rearrangement, the most common E26 transformation specific (ETS) gene fusion within prostate cancer, is known to contribute to the pathogenesis of this disease and carries diagnostic annotations for prostate cancer patients clinically. The ERG rearrangement status in prostatic adenocarcinoma currently cannot be reliably identified from histologic features on H&E-stained slides alone and hence requires ancillary studies such as immunohistochemistry (IHC), fluorescent in situ hybridization (FISH) or next generation sequencing (NGS) for identification. METHODS: OBJECTIVE: We accordingly sought to develop a deep learning-based algorithm to identify ERG rearrangement status in prostatic adenocarcinoma based on digitized slides of H&E morphology alone. DESIGN: Setting, and Participants: Whole slide images from 392 in-house and TCGA cases were employed and annotated using QuPath. Image patches of 224 × 224 pixel were exported at 10 ×, 20 ×, and 40 × for input into a deep learning model based on MobileNetV2 convolutional neural network architecture pre-trained on ImageNet. A separate model was trained for each magnification. Training and test datasets consisted of 261 cases and 131 cases, respectively. The output of the model included a prediction of ERG-positive (ERG rearranged) or ERG-negative (ERG not rearranged) status for each input patch. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Various accuracy measurements including area under the curve (AUC) of the receiver operating characteristic (ROC) curves were used to evaluate the deep learning model. RESULTS AND LIMITATIONS: All models showed similar ROC curves with AUC results ranging between 0.82 and 0.85. The sensitivity and specificity of these models were 75.0% and 83.1% (20 × model), respectively. CONCLUSIONS: A deep learning-based model can successfully predict ERG rearrangement status in the majority of prostatic adenocarcinomas utilizing only H&E-stained digital slides. Such an artificial intelligence-based model can eliminate the need for using extra tumor tissue to perform ancillary studies in order to assess for ERG gene rearrangement in prostatic adenocarcinoma.

Experimental testicular tissue banking to generate spermatogenesis in the future: A multidisciplinary team approach.

Spermatogonial stem cell (SSC) loss due to cancer treatment, developmental disorder or genetic abnormality may cause permanent infertility. Cryopreservation of ejaculated sperm is an effective method of fertility preservation in adult males at risk of infertility. However this is not an option in pre-pubertal boys because spermatogenesis has not yet started, and it is difficult in adolescents who are not sexually mature. Therefore testicular tissue cryopreservation to preserve SSCs for future generation of spermatogenesis, either in vivo or in vitro, could be an option for these groups of patients. Although SSC transplantation has been successful in several species including non-human primates, it is still experimental in humans. There are several remaining concerns which need to be addressed before initiating trials of human SSC autotransplantation. Establishment of a testicular tissue banking system is a fundamental step towards using SSC technology as a fertility preservation method. It is important to understand the consultation, harvesting the testicular tissue, histological evaluation, cryopreservation, and long term storage aspects. We describe here a multidisciplinary approach to establish testicular tissue banking for males at risk of infertility.

Utility of artificial intelligence in a binary classification of soft tissue tumors.

Soft tissue tumors (STTs) pose diagnostic and therapeutic challenges due to their rarity, complexity, and morphological overlap. Accurate differentiation between benign and malignant STTs is important to set treatment directions, however, this task can be difficult. The integration of machine learning and artificial intelligence (AI) models can potentially be helpful in classifying these tumors. The aim of this study was to investigate AI and machine learning tools in the classification of STT into benign and malignant categories. This study consisted of three components: (1) Evaluation of whole-slide images (WSIs) to classify STT into benign and malignant entities. Five specialized soft tissue pathologists from different medical centers independently reviewed 100 WSIs, representing 100 different cases, with limited clinical information and no additional workup. The results showed an overall concordance rate of 70.4% compared to the reference diagnosis. (2) Identification of cell-specific parameters that can distinguish benign and malignant STT. Using an image analysis software (QuPath) and a cohort of 95 cases, several cell-specific parameters were found to be statistically significant, most notably cell count, nucleus/cell area ratio, nucleus hematoxylin density mean, and cell max caliper. (3) Evaluation of machine learning library (Scikit-learn) in differentiating benign and malignant STTs. A total of 195 STT cases (156 cases in the training group and 39 cases in the validation group) achieved approximately 70% sensitivity and specificity, and an AUC of 0.68. Our limited study suggests that the use of WSI and AI in soft tissue pathology has the potential to enhance diagnostic accuracy and identify parameters that can differentiate between benign and malignant STTs. We envision the integration of AI as a supportive tool to augment the pathologists' diagnostic capabilities.

Artificial Intelligence-Enabled Gastric Cancer Interpretations: Are We There yet?

The integration of digital pathology and artificial intelligence (AI) is revolutionizing pathology by providing pathologists with new tools to improve workflow, enhance diagnostic accuracy, and undertake novel discovery. The capability of AI to recognize patterns and features in digital images beyond human perception is particularly valuable, thereby providing additional information for prognostic and predictive purposes. AI-based tools diagnose gastric carcinoma in digital images, detect gastric carcinoma metastases in lymph nodes, automate Ki-67 scoring in gastric neuroendocrine tumors, and quantify tumor-infiltrating lymphocytes. This article provides an overview of all of these applications of AI pertaining to gastric cancer.

A deep-learning approach for reconstructing 3D turbulent flows from 2D observation data.

Turbulence is a complex phenomenon that has a chaotic nature with multiple spatio-temporal scales, making predictions of turbulent flows a challenging topic. Nowadays, an abundance of high-fidelity databases can be generated by experimental measurements and numerical simulations, but obtaining such accurate data in full-scale applications is currently not possible. This motivates utilising deep learning on subsets of the available data to reduce the required cost of reconstructing the full flow in such full-scale applications. Here, we develop a generative-adversarial-network (GAN)-based model to reconstruct the three-dimensional velocity fields from flow data represented by a cross-plane of unpaired two-dimensional velocity observations. The model could successfully reconstruct the flow fields with accurate flow structures, statistics and spectra. The results indicate that our model can be successfully utilised for reconstructing three-dimensional flows from two-dimensional experimental measurements. Consequently, a remarkable reduction in the complexity of the experimental setup and the storage cost can be achieved.

Artificial intelligence applied to breast pathology.

The convergence of digital pathology and computer vision is increasingly enabling computers to perform tasks performed by humans. As a result, artificial intelligence (AI) is having an astoundingly positive effect on the field of pathology, including breast pathology. Research using machine learning and the development of algorithms that learn patterns from labeled digital data based on "deep learning" neural networks and feature-engineered approaches to analyze histology images have recently provided promising results. Thus far, image analysis and more complex AI-based tools have demonstrated excellent success performing tasks such as the quantification of breast biomarkers and Ki67, mitosis detection, lymph node metastasis recognition, tissue segmentation for diagnosing breast carcinoma, prognostication, computational assessment of tumor-infiltrating lymphocytes, and prediction of molecular expression as well as treatment response and benefit of therapy from routine H&E images. This review critically examines the literature regarding these applications of AI in the area of breast pathology.

Quantitative Image Analysis as an Adjunct to Manual Scoring of ER, PgR, and HER2 in Invasive Breast Carcinoma.

OBJECTIVES: Biomarker expression evaluation for estrogen receptor (ER), progesterone receptor (PgR), and human epidermal growth factor receptor 2 (HER2) is an essential prognostic and predictive parameter for breast cancer and critical for guiding hormonal and neoadjuvant therapy. This study compared quantitative image analysis (QIA) with pathologists' scoring for ER, PgR, and HER2. METHODS: A retrospective analysis was undertaken of 1,367 invasive breast carcinomas, including all histopathology subtypes, for which ER, PgR, and HER2 were analyzed by manual scoring and QIA. The resulting scores were compared, and in a subset of HER2 cases (n = 373, 26%), scores were correlated with available fluorescence in situ hybridization (FISH) results. RESULTS: Concordance between QIA and manual scores for ER, PgR, and HER2 was 93%, 96%, and 90%, respectively. Discordant cases had low positive scores (1%-10%) for ER (n = 33), were due to nonrepresentative region selection (eg, ductal carcinoma in situ) or tumor heterogeneity for PgR (n = 43), and were of one-step difference (negative to equivocal, equivocal to positive, or vice versa) for HER2 (n = 90). Among HER2 cases where FISH results were available, only four (1.0%) showed discordant QIA and FISH results. CONCLUSIONS: QIA is a computer-aided diagnostic support tool for pathologists. It significantly improves ER, PgR, and HER2 scoring standardization. QIA demonstrated excellent concordance with pathologists' scores. To avoid pitfalls, pathologist oversight of representative region selection is recommended.

Differentiating Intrarenal Ectopic Adrenal Tissue From Renal Cell Carcinoma in the Kidney.

BACKGROUND: Adrenal rest (AR) is the presence of ectopic adrenal cortical tissue, often identified incidentally during autopsy (20% of postmortem examination). In the kidney, AR can be found in 6% of the general population. Ectopic adrenal tissue is of no functional significance but may in some cases, pose a diagnostic challenge for the pathologist, especially in the context of renal clear cell renal cell carcinoma (RCC) and small needle biopsies. AIM: To investigate the utility of immunohistochemical stains in distinguishing AR from RCC. METHODS: Archival cases of AR, in our institution, were reviewed and compared with a cohort of RCC cases using a panel of immunohistochemical stains, including PAX2, PAX8, calretinin, and inhibin. RESULTS: Nine of 10 (90%) cases of AR showed positive staining for inhibin and negative staining for calretinin, PAX2 and PAX8. One AR case was positive for PAX2 and PAX8 in addition to inhibin. All (100%) RCC cases were positive for PAX2 and PAX8, but negative for inhibin and calretinin. CONCLUSIONS: A panel of PAX2, PAX8 and inhibin may be useful markers for distinguishing AR from RCC. Calretinin was noncontributory in our study.