Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

Machine learning based prediction of squamous cell carcinoma in ex vivo confocal laser scanning microscopy

Image classification with convolutional neural networks (CNN) offers an unprecedented opportunity to medical imaging. Regulatory agencies in the USA and Europe have already cleared numerous deep learning/machine learning based medical devices and algorithms. While the field of radiology is on the forefront of artificial intelligence (AI) revolution, conventional pathology, which commonly relies on examination of tissue samples on a glass slide, is falling behind in leveraging this technology. On the other hand, ex vivo confocal laser scanning microscopy (ex vivo CLSM), owing to its digital workflow features, has a high potential to benefit from integrating AI tools into the assessment and decision-making process. Aim of this work was to explore a preliminary application of CNN in digitally stained ex vivo CLSM images of cutaneous squamous cell carcinoma (cSCC) for automated detection of tumor tissue. Thirty-four freshly excised tissue samples were prospectively collected and examined immediately after resection. After the histologically confirmed ex vivo CLSM diagnosis, the tumor tissue was annotated for segmentation by experts, in order to train the MobileNet CNN. The model was then trained and evaluated using cross validation. The overall sensitivity and specificity of the deep neural network for detecting cSCC and tumor free areas on ex vivo CLSM slides compared to expert evaluation were 0.76 and 0.91, respectively. The area under the ROC curve was equal to 0.90 and the area under the precision-recall curve was 0.85. The results demonstrate a high potential of deep learning models to detect cSCC regions on digitally stained ex vivo CLSM slides and to distinguish them from tumor-free skin

LABRAD : Vol 46, Issue 4 - October 2021

Role of Barcoding in a Clinical Laboratory to Reduce Pre-Analytical Errors Congenital Dyserythropoietic Anemia: The Morphological Diagnosis Digital Imaging in Hematology: A New Beginning Metabolomics: Identification of Fatty Acid Oxidation (FAO) Disorders Next-Generation Sequencing for HLA Genotyping Urine Metabolomics to identify Organic Academia Next-Generation Sequencing (NGS) of Solid Tumor Importance of using Genomic Tool in Microbial Identification Radiology Practice in 21st Century: Role of Artificial Intelligence Case Quiz Best of the Recent Past Polaroidhttps://ecommons.aku.edu/labrad/1036/thumbnail.jp

MALDI Mass Spectrometry Imaging for the Discovery of Prostate Carcinoma Biomarkers

The elucidation of new biological markers of prostate cancer (PCa) should aid in the detection, and prognosis of this disease. Diagnostic decision making by pathologists in prostate cancer is highly dependent on tissue morphology. The ability to localize disease-specific molecular changes in tissue would help improve this critical pathology decision making process. Direct profiling of proteins in tissue sections using MALDI imaging mass spectrometry (MALDI-IMS) has the power to link molecular detail to morphological and pathological changes, enhancing the ability to identify candidates for new specific biomarkers. However, critical questions remain regarding the integration of this technique with clinical decision making. To address these questions, and to investigate the potential of MALDI-IMS for the diagnosis of prostate cancer, we have used this approach to analyze prostate tissue for the determination of the cellular origins of different protein signals to improve cancer detection and to identify specific protein markers of PCa. We found that specific protein/peptide expression changes correlated with the presence or absence of prostate cancer as well as the presence of micro-metastatic disease. Additionally, the over-expression of a single peptide (m/z = 4355) was able to accurately define primary cancer tissue from adjacent normal tissue. Tandem mass spectrometry analysis identified this peptide as a fragment of MEKK2, a member of the MAP kinase signaling pathway. Validation of MEKK2 overexpression in moderately differentiated PCa and prostate cancer cell lines was performed using immunohistochemistry and Western Blot analysis. Classification algorithms using specific ions differentially expressed in PCa tissue and a ROC cut-off value for the normalized intensity of the MEKK2 fragment at m/z 4355 were used to classify a blinded validation set. Finally, the optimization of sample processing in a new fixative which preserves macromolecules has led to improved through-put of samples making MALDI-IMS more compatible with current histological applications, facilitating its implementation in a clinical setting. This study highlights the potential of MALDI-IMS to define the molecular events involved in prostate tumorigenesis and demonstrates the applicability of this approach to clinical diagnostics as an aid to pathological decision making in prostate cancer

Investigation of Endogenous In-Vivo Sodium Concentration in Human Prostate Cancer Measured With 23Na Magnetic Resonance Imaging

Prostate cancer (PCa) is the most common malignancy in men. Aggressive prostate tumours must be identified, differentiated from indolent tumours, and treated to ensure survival of the patient. Currently, clinicians use a combination of multi-parametric magnetic resonance imaging (mpMRI) contrasts to improve PCa detection. While these techniques provide very good spatial resolution, the specificity is often insufficient to unequivocally identify malignant lesions. Utilizing specialized MRI hardware developed for sensitive in-vivo detection of sodium, this work has investigated differences in sodium concentration between healthy and malignant prostate tissue. Patients with biopsy-proven PCa underwent conventional mpMRI and sodium MRI followed by radical prostatectomy. Subsequent whole-mount histopathology of the excised prostate was then contoured according to Gleason Grade, a radiological assessment of tumour stage and aggressiveness for PCa. Tissue sodium concentration (TSC) measured by sodium MRI was successfully co-registered with standard image contrasts from multi-parametric MRI and also with pathologist confirmed histopathology as the gold standard. This proposed method provides quantitative, in-vivo sodium information from cancerous human prostates. The results of this study establish the relationship between TSC and malignant PCa, which could prove useful in initial characterization of the disease and for active surveillance of indolent lesions

AI in Medical Imaging Informatics: Current Challenges and Future Directions

This paper reviews state-of-the-art research solutions across the spectrum of medical imaging informatics, discusses clinical translation, and provides future directions for advancing clinical practice. More specifically, it summarizes advances in medical imaging acquisition technologies for different modalities, highlighting the necessity for efficient medical data management strategies in the context of AI in big healthcare data analytics. It then provides a synopsis of contemporary and emerging algorithmic methods for disease classification and organ/ tissue segmentation, focusing on AI and deep learning architectures that have already become the de facto approach. The clinical benefits of in-silico modelling advances linked with evolving 3D reconstruction and visualization applications are further documented. Concluding, integrative analytics approaches driven by associate research branches highlighted in this study promise to revolutionize imaging informatics as known today across the healthcare continuum for both radiology and digital pathology applications. The latter, is projected to enable informed, more accurate diagnosis, timely prognosis, and effective treatment planning, underpinning precision medicine