Encoding histopathology whole slide images with location-aware graphs for diagnostically relevant regions retrieval

2021 ◽  
pp. 102308
Author(s):  
Yushan Zheng ◽  
Zhiguo Jiang ◽  
Jun Shi ◽  
Fengying Xie ◽  
Haopeng Zhang ◽  
...  
Keyword(s):  
Location Aware ◽  
Whole Slide Images

Pathology image-based lung cancer subtyping using deeplearning features and cell-density maps

2020 ◽  
Vol 2020 (10) ◽  
pp. 64-1-64-5
Author(s):  
Mustafa I. Jaber ◽  
Christopher W. Szeto ◽  
Bing Song ◽  
Liudmila Beziaeva ◽  
Stephen C. Benz ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cell Density ◽  
Majority Voting ◽  
Training Set ◽  
Density Maps ◽  
Color Deconvolution ◽  
Map Generation ◽  
Density Map ◽  
Pathology Image ◽  
Whole Slide Images

In this paper, we propose a patch-based system to classify non-small cell lung cancer (NSCLC) diagnostic whole slide images (WSIs) into two major histopathological subtypes: adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC). Classifying patients accurately is important for prognosis and therapy decisions. The proposed system was trained and tested on 876 subtyped NSCLC gigapixel-resolution diagnostic WSIs from 805 patients – 664 in the training set and 141 in the test set. The algorithm has modules for: 1) auto-generated tumor/non-tumor masking using a trained residual neural network (ResNet34), 2) cell-density map generation (based on color deconvolution, local drain segmentation, and watershed transformation), 3) patch-level feature extraction using a pre-trained ResNet34, 4) a tower of linear SVMs for different cell ranges, and 5) a majority voting module for aggregating subtype predictions in unseen testing WSIs. The proposed system was trained and tested on several WSI magnifications ranging from x4 to x40 with a best ROC AUC of 0.95 and an accuracy of 0.86 in test samples. This fully-automated histopathology subtyping method outperforms similar published state-of-the-art methods for diagnostic WSIs.

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.

scholarly journals The Potential of Artificial Intelligence to Detect Lymphovascular Invasion in Testicular Cancer

Cancers ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1325
Author(s):  
Abhisek Ghosh ◽  
Korsuk Sirinukunwattana ◽  
Nasullah Khalid Alham ◽  
Lisa Browning ◽  
Richard Colling ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Testicular Cancer ◽  
Germ Cell ◽  
Lymphovascular Invasion ◽  
Automatic Segmentation ◽  
Prognostic Significance ◽  
Germ Cell Tumours ◽  
Testicular Germ Cell ◽  
Testicular Germ Cell Tumours ◽  
Whole Slide Images

Testicular cancer is the most common cancer in men aged from 15 to 34 years. Lymphovascular invasion refers to the presence of tumours within endothelial-lined lymphatic or vascular channels, and has been shown to have prognostic significance in testicular germ cell tumours. In non-seminomatous tumours, lymphovascular invasion is the most powerful prognostic factor for stage 1 disease. For the pathologist, searching multiple slides for lymphovascular invasion can be highly time-consuming. The aim of this retrospective study was to develop and assess an artificial intelligence algorithm that can identify areas suspicious for lymphovascular invasion in histological digital whole slide images. Areas of possible lymphovascular invasion were annotated in a total of 184 whole slide images of haematoxylin and eosin (H&E) stained tissue from 19 patients with testicular germ cell tumours, including a mixture of seminoma and non-seminomatous cases. Following consensus review by specialist uropathologists, we trained a deep learning classifier for automatic segmentation of areas suspicious for lymphovascular invasion. The classifier identified 34 areas within a validation set of 118 whole slide images from 10 patients, each of which was reviewed by three expert pathologists to form a majority consensus. The precision was 0.68 for areas which were considered to be appropriate to flag, and 0.56 for areas considered to be definite lymphovascular invasion. An artificial intelligence tool which highlights areas of possible lymphovascular invasion to reporting pathologists, who then make a final judgement on its presence or absence, has been demonstrated as feasible in this proof-of-concept study. Further development is required before clinical deployment.

ReliableBox: Secure and Verifiable Cloud Storage with Location-aware Backup

2021 ◽  
pp. 1-1
Author(s):  
Tao Jiang ◽  
Wenjuan Meng ◽  
Xu Yuan ◽  
Liangmin Wang ◽  
Jian-Hua Ge ◽  
...  
Keyword(s):  
Cloud Storage ◽  
Location Aware

Proof of Concept: The Use of Whole-Slide Images (WSI) for Peer Review of Tissues on Routine Regulatory Toxicology Studies

2021 ◽  
pp. 019262332098325
Author(s):  
Alys E. Bradley ◽  
Maurice G. Cary ◽  
Kaori Isobe ◽  
Stuart Naylor ◽  
Stephen Drew
Keyword(s):  
Peer Review ◽  
Proof Of Concept ◽  
Regulatory Toxicology ◽  
Glass Slides ◽  
Microscopic Findings ◽  
Slight Discrepancy ◽  
Whole Slide Images ◽  
Diagnostic Discrepancies

This Proof of Concept (POC) study was to assess whether assessment of whole slide images (WSI) of the 2 target tissues for a contemporaneous peer review can elicit concordant results to the findings generated by the Study Pathologist from the glass slides. Well-focused WSI of liver and spleen from 4 groups of mice, that had previously been diagnosed to be the target tissues by an experienced veterinary toxicologic pathologist examining glass slides, were independently reviewed by 3 veterinary pathologists with varying experience in assessment of WSIs. Diagnostic discrepancies were then reviewed by an experienced adjudicating pathologist. Assessment of microscopic findings using WSI showed concordance with the glass slides, with only slight discrepancy in severity grades noted. None of the lesions recorded by the Study pathologist were “missed” and no lesions were added by the pathologists evaluating WSIs, thus demonstrating equivalence of the WSI to glass slides for this study.

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