scholarly journals Predicting Primary Site of Secondary Liver Cancer with a Neural Estimator of Metastatic Origin (NEMO)

2019 ◽  
Author(s):  
Geoffrey F. Schau ◽  
Erik A. Burlingame ◽  
Guillaume Thibault ◽  
Tauangtham Anekpuritanang ◽  
Ying Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Liver Cancer ◽  
Clinical Information ◽  
Structural Features ◽  
Tissue Sections ◽  
Liver Cancers ◽  
Hematoxylin And Eosin ◽  
Whole Slide Images ◽  
Clinical Determination

AbstractPathologists rely on clinical information, tissue morphology, and sophisticated molecular diagnostics to accurately infer the metastatic origin of secondary liver cancer. In this paper, we introduce a deep learning approach to identify spatially localized regions of cancerous tumor within hematoxylin and eosin stained tissue sections of liver cancer and to generate predictions of the cancer’s metastatic origin. Our approach achieves an accuracy of 90.2% when classifying metastatic origin of whole slide images into three distinct classes, which compares favorably to an established clinical benchmark by three board-certified pathologists whose accuracies ranged from 90.2% to 94.1% on the same prediction task. This approach illustrates the potential impact of deep learning systems to leverage morphological and structural features of H&E stained tissue sections to guide pathological and clinical determination of the metastatic origin of secondary liver cancers.

scholarly journals Breast Tumor Cellularity Assessment using Deep Neural Networks

2019 ◽  
Author(s):  
Alexander Rakhlin ◽  
Aleksei Tiulpin ◽  
Alexey A. Shvets ◽  
Alexandr A. Kalinin ◽  
Vladimir I. Iglovikov ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Automatic Assessment ◽  
Appropriate Treatment ◽  
Image Patches ◽  
Expert Pathologist ◽  
Hematoxylin And Eosin ◽  
Treated Breast ◽  
Intra Class Correlation Coefficient ◽  
Whole Slide Images

AbstractBreast cancer is one of the main causes of death world-wide. Histopathological cellularity assessment of residual tumors in post-surgical tissues is used to analyze a tumor’s response to a therapy. Correct cellularity assessment increases the chances of getting an appropriate treatment and facilitates the patient’s survival. In current clinical practice, tumor cellularity is manually estimated by pathologists; this process is tedious and prone to errors or low agreement rates between assessors. In this work, we evaluated three strong novel Deep Learning-based approaches for automatic assessment of tumor cellularity from post-treated breast surgical specimens stained with hematoxylin and eosin. We validated the proposed methods on the BreastPathQ SPIE challenge dataset that consisted of 2395 image patches selected from whole slide images acquired from 64 patients. Compared to expert pathologist scoring, our best performing method yielded the Cohen’s kappa coefficient of 0.69 (vs. 0.42 previously known in literature) and the intra-class correlation coefficient of 0.89 (vs. 0.83). Our results suggest that Deep Learning-based methods have a significant potential to alleviate the burden on pathologists, enhance the diagnostic workflow, and, thereby, facilitate better clinical outcomes in breast cancer treatment.

scholarly journals Deep learning quantified mucus-tumor ratio predicting survival of patients with colorectal cancer using whole-slide images

2021 ◽  
Author(s):  
Ke Zhao ◽  
Lin Wu ◽  
Yanqi Huang ◽  
Su Yao ◽  
Zeyan Xu ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Deep Learning ◽  
Prognostic Value ◽  
Validation Cohort ◽  
Survival Rates ◽  
Training Cohort ◽  
Mucinous Component ◽  
Hematoxylin And Eosin ◽  
High Consistency ◽  
Whole Slide Images

Abstract Background In colorectal cancer (CRC), mucinous adenocarcinoma differs from other adenocarcinomas in gene-phenotype, morphology, and prognosis. However, mucinous components are present in a large amount of adenocarcinoma, and the prognostic value of mucus proportion has not been investigated. Artificial intelligence provides a way to quantify mucus proportion on whole-slide images (WSIs) accurately. We aimed to quantify mucus proportion by deep learning and further investigate its prognostic value in two CRC patients cohorts. Methods Deep learning was used to segment WSIs stained with hematoxylin and eosin. Mucus-tumor ratio (MTR) was defined as the proportion of mucinous component in the tumor area. A training cohort (N = 419) and a validation cohort (N = 315) were used to evaluate the prognostic value of MTR. Survival analysis was performed by the Cox proportional hazard model. Result Patients were stratified to mucus-low and mucus-high groups by 24.1% as the threshold. In the training cohort, patients with mucus-high had unfavorable outcomes (hazard ratio for high vs. low 1.88, 95% confidence interval 1.18-2.99, P = 0.008), with 5-year overall survival rates of 54.8% and 73.7% in mucus-high and mucus-low groups, respectively. The results were confirmed in the validation cohort (2.09, 1.21-3.60, 0.008; 79.8% vs. 62.8%). The prognostic value of MTR was maintained in multivariate analysis for both cohorts. Conclusion The deep learning quantified MTR was an independent prognostic factor in CRC. With the advantages of advanced efficiency and high consistency, our method is suitable for clinical application and promotes precision medicine development.

scholarly journals A deep learning model for the classification of indeterminate lung carcinoma in biopsy whole slide images

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Fahdi Kanavati ◽  
Gouji Toyokawa ◽  
Seiya Momosaki ◽  
Hiroaki Takeoka ◽  
Masaki Okamoto ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Deep Learning ◽  
Area Under The Curve ◽  
Learning Model ◽  
Primary Sources ◽  
Independent Test ◽  
Hematoxylin And Eosin ◽  
Deep Learning Model ◽  
Whole Slide Images

AbstractThe differentiation between major histological types of lung cancer, such as adenocarcinoma (ADC), squamous cell carcinoma (SCC), and small-cell lung cancer (SCLC) is of crucial importance for determining optimum cancer treatment. Hematoxylin and Eosin (H&E)-stained slides of small transbronchial lung biopsy (TBLB) are one of the primary sources for making a diagnosis; however, a subset of cases present a challenge for pathologists to diagnose from H&E-stained slides alone, and these either require further immunohistochemistry or are deferred to surgical resection for definitive diagnosis. We trained a deep learning model to classify H&E-stained Whole Slide Images of TBLB specimens into ADC, SCC, SCLC, and non-neoplastic using a training set of 579 WSIs. The trained model was capable of classifying an independent test set of 83 challenging indeterminate cases with a receiver operator curve area under the curve (AUC) of 0.99. We further evaluated the model on four independent test sets—one TBLB and three surgical, with combined total of 2407 WSIs—demonstrating highly promising results with AUCs ranging from 0.94 to 0.99.

scholarly journals Deep learning enables the atomic structure determination of the Fanconi Anemia core complex from cryoEM

IUCrJ ◽  
2020 ◽  
Vol 7 (5) ◽  
pp. 881-892
Author(s):  
Daniel P. Farrell ◽  
Ivan Anishchenko ◽  
Shabih Shakeel ◽  
Anna Lauko ◽  
Lori A. Passmore ◽  
...  
Keyword(s):  
Deep Learning ◽  
Fanconi Anemia ◽  
Structure Determination ◽  
Model Building ◽  
Protein Complexes ◽  
Structural Features ◽  
Coarse Grained ◽  
Core Complex ◽  
Distance Distributions

Cryo-electron microscopy of protein complexes often leads to moderate resolution maps (4–8 Å), with visible secondary-structure elements but poorly resolved loops, making model building challenging. In the absence of high-resolution structures of homologues, only coarse-grained structural features are typically inferred from these maps, and it is often impossible to assign specific regions of density to individual protein subunits. This paper describes a new method for overcoming these difficulties that integrates predicted residue distance distributions from a deep-learned convolutional neural network, computational protein folding using Rosetta, and automated EM-map-guided complex assembly. We apply this method to a 4.6 Å resolution cryoEM map of Fanconi Anemia core complex (FAcc), an E3 ubiquitin ligase required for DNA interstrand crosslink repair, which was previously challenging to interpret as it comprises 6557 residues, only 1897 of which are covered by homology models. In the published model built from this map, only 387 residues could be assigned to the specific subunits with confidence. By building and placing into density 42 deep-learning-guided models containing 4795 residues not included in the previously published structure, we are able to determine an almost-complete atomic model of FAcc, in which 5182 of the 6557 residues were placed. The resulting model is consistent with previously published biochemical data, and facilitates interpretation of disease-related mutational data. We anticipate that our approach will be broadly useful for cryoEM structure determination of large complexes containing many subunits for which there are no homologues of known structure.

scholarly journals Deep learning enables the atomic structure determination of the Fanconi Anemia core complex from cryoEM

2020 ◽  
Author(s):  
Daniel P. Farrell ◽  
Ivan Anishchenko ◽  
Shabih Shakeel ◽  
Anna Lauko ◽  
Lori A. Passmore ◽  
...  
Keyword(s):  
Deep Learning ◽  
Fanconi Anemia ◽  
Structure Determination ◽  
Model Building ◽  
Protein Complexes ◽  
Structural Features ◽  
Coarse Grained ◽  
Core Complex ◽  
Distance Distributions

AbstractCryo-electron microscopy of protein complexes often leads to moderate resolution maps (4-8 Å), with visible secondary structure elements but poorly resolved loops, making model-building challenging. In the absence of high-resolution structures of homologues, only coarse-grained structural features are typically inferred from these maps, and it is often impossible to assign specific regions of density to individual protein subunits. This paper describes a new method for overcoming these difficulties that integrates predicted residue distance distributions from a deep-learned convolutional neural network, computational protein folding using Rosetta, and automated EM-map-guided complex assembly. We apply this method to a 4.6 Å resolution cryoEM map of Fanconi Anemia core complex (FAcc), an E3 ubiquitin ligase required for DNA interstrand crosslink repair, which was previously challenging to interpret as it is comprised of 6557 residues, only 1897 of which are covered by homology models. In the published structure built from this map, only 387 residues could be assigned to specific subunits. By building and placing into density 42 deep-learning guided models containing 4795 residues not included in the previously published structure, we are able to determine an almost-complete atomic model of FAcc, in which 5182 of the 6557 residues were placed. The resulting model is consistent with previously published biochemical data, and facilitates interpretation of disease related mutational data. We anticipate that our approach will be broadly useful for cryoEM structure determination of large complexes containing many subunits for which there are no homologues of known structure.

scholarly journals Deep Learning Global Glomerulosclerosis in Transplant Kidney Frozen Sections

2018 ◽  
Author(s):  
Jon N. Marsh ◽  
Matthew K. Matlock ◽  
Satoru Kudose ◽  
Ta-Chiang Liu ◽  
Thaddeus S. Stappenbeck ◽  
...  
Keyword(s):  
Deep Learning ◽  
Frozen Section ◽  
Extended Model ◽  
Donor Kidney ◽  
Automated Method ◽  
Image Patches ◽  
Transplant Kidney ◽  
Global Glomerulosclerosis ◽  
Whole Slide Images

AbstractTransplantable kidneys are in very limited supply. Accurate viability assessment prior to transplantation could minimize organ discard. Rapid and accurate evaluation of intra-operative donor kidney biopsies is essential for determining which kidneys are eligible for transplantation. The criteria for accepting or rejecting donor kidneys relies heavily on pathologist determination of the percent of glomeruli (determined from a frozen section) that are normal and sclerotic. This percentage is a critical measurement that correlates with transplant outcome. Inter- and intra-observer variability in donor biopsy evaluation is, however, significant. An automated method for determination of percent global glomerulosclerosis could prove useful in decreasing evaluation variability, increasing throughput, and easing the burden on pathologists. Here, we describe the development of a deep learning model that identifies and classifies non-sclerosed and sclerosed glomeruli in whole-slide images of donor kidney frozen section biopsies. This model extends a convolutional neural network (CNN) pre-trained on a large database of digital images. The extended model, when trained on just 48 whole slide images, exhibits slide-level evaluation performance on par with expert renal pathologists. The model substantially outperforms a model trained on image patches of isolated glomeruli. Encouragingly, the model’s performance is robust to slide preparation artifacts associated with frozen section preparation. As the first model reported that identifies and classifies normal and sclerotic glomeruli in frozen kidney sections, and thus the first model reported in the literature relevant to kidney transplantation, it may become an essential part of donor kidney biopsy evaluation in the clinical setting.

DETERMINATION OF THE TYPE OF A SINGLE-USE GRENADE LAUNCHER BASED ON ITS COMPOSITE PARTS AND FRAGMENTS OF REACTIVE GRENADE FOUND AT THE PLACE OF ACCIDENT

2017 ◽  
Vol 17 ◽  
pp. 245-252
Author(s):  
V. V. Somov
Keyword(s):  
Combustion Chamber ◽  
Structural Features ◽  
Outlet Section ◽  
Technical Expertise ◽  
Jet Engine ◽  
Size Ofthe ◽  
Single Use ◽  
Nozzle Block ◽  
Considerable Damage

In carrying out an investigation into the explosion, among others, the investigative version of the use of a single-use reactive grenade launcher is being considered. The most common for criminal explosions are applied grenade launchers RPG-18, RPG-22, RPG-26. Their use is due to a number of such properties as small size and weight, which makes it possible to transfer them covertly, the range of the shot significantly exceeding the range of the hand grenade throw, the high detonating effect of the rocket grenade explosion. The single-use rocket launchers are generally of the same design. Their differences are in the features of the components construction and dimensional characteristics, which are given in the article. On the basis of expert practice, details ofgrenade launchers that remain at the site of the explosion and have the least damage are determined. These details are the objects of investigation of the explosion technical expertise. These objects include launchers of grenade launchers and rocket parts ofjet grenades. The design features of the launchers, their dimensional characteristics and marking symbols make it possible to determine their belonging to a specific type of jet grenade launchers. Missile parts of jet grenades differ in the form of the combustion chamber of the jet engine, nozzle, in the size ofthe outlet section of the nozzle, in the form and size of the stabilizerfeathers. To determine the belonging of the rocket part of the grenade to a specific type ofjet grenade launcher, it’s necessary to establish a set of structural features and dimensional characteristics. At considerable damage of the combustion chamber of the jet engine, as a rule, the nozzle block remains intact that allows to define diameter of critical section of a nozzle, and on it to establish type of the used single-use grenade launcher.

scholarly journals A Digital Pathology Solution to Resolve the Tissue Floater Conundrum

2020 ◽  
Author(s):  
Liron Pantanowitz ◽  
Pamela Michelow ◽  
Scott Hazelhurst ◽  
Shivam Kalra ◽  
Charles Choi ◽  
...  
Keyword(s):  
Digital Pathology ◽  
Primary Diagnosis ◽  
Image Search ◽  
Molecular Tests ◽  
Tissue Identification ◽  
Digital Database ◽  
Glass Slides ◽  
Search Tool ◽  
Hematoxylin And Eosin ◽  
Whole Slide Images

Context.— Pathologists may encounter extraneous pieces of tissue (tissue floaters) on glass slides because of specimen cross-contamination. Troubleshooting this problem, including performing molecular tests for tissue identification if available, is time consuming and often does not satisfactorily resolve the problem. Objective.— To demonstrate the feasibility of using an image search tool to resolve the tissue floater conundrum. Design.— A glass slide was produced containing 2 separate hematoxylin and eosin (H&E)-stained tissue floaters. This fabricated slide was digitized along with the 2 slides containing the original tumors used to create these floaters. These slides were then embedded into a dataset of 2325 whole slide images comprising a wide variety of H&E stained diagnostic entities. Digital slides were broken up into patches and the patch features converted into barcodes for indexing and easy retrieval. A deep learning-based image search tool was employed to extract features from patches via barcodes, hence enabling image matching to each tissue floater. Results.— There was a very high likelihood of finding a correct tumor match for the queried tissue floater when searching the digital database. Search results repeatedly yielded a correct match within the top 3 retrieved images. The retrieval accuracy improved when greater proportions of the floater were selected. The time to run a search was completed within several milliseconds. Conclusions.— Using an image search tool offers pathologists an additional method to rapidly resolve the tissue floater conundrum, especially for those laboratories that have transitioned to going fully digital for primary diagnosis.

scholarly journals Predicting gastric cancer outcome from resected lymph node histopathology images using deep learning

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Xiaodong Wang ◽  
Ying Chen ◽  
Yunshu Gao ◽  
Huiqing Zhang ◽  
Zehui Guan ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Deep Learning ◽  
Lymph Node ◽  
Lymph Nodes ◽  
Main Method ◽  
Learning Framework ◽  
Prognostic Assessment ◽  
N Stage ◽  
Cancer Outcome ◽  
Whole Slide Images

AbstractN-staging is a determining factor for prognostic assessment and decision-making for stage-based cancer therapeutic strategies. Visual inspection of whole-slides of intact lymph nodes is currently the main method used by pathologists to calculate the number of metastatic lymph nodes (MLNs). Moreover, even at the same N stage, the outcome of patients varies dramatically. Here, we propose a deep-learning framework for analyzing lymph node whole-slide images (WSIs) to identify lymph nodes and tumor regions, and then to uncover tumor-area-to-MLN-area ratio (T/MLN). After training, our model’s tumor detection performance was comparable to that of experienced pathologists and achieved similar performance on two independent gastric cancer validation cohorts. Further, we demonstrate that T/MLN is an interpretable independent prognostic factor. These findings indicate that deep-learning models could assist not only pathologists in detecting lymph nodes with metastases but also oncologists in exploring new prognostic factors, especially those that are difficult to calculate manually.

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