scholarly journals Tumor proliferation assessment of whole slide images

2018 ◽  
Author(s):  
Oscar A. Jimenez-del-Toro ◽  
Mikael Rousson ◽  
Martin Hedlund ◽  
Mats Andersson ◽  
Ludwig Jacobsson ◽  
...  
Keyword(s):  
Tumor Proliferation ◽  
Whole Slide Images

scholarly journals Predicting breast tumor proliferation from whole-slide images: The TUPAC16 challenge

2019 ◽  
Vol 54 ◽  
pp. 111-121 ◽  
Author(s):  
Mitko Veta ◽  
Yujing J. Heng ◽  
Nikolas Stathonikos ◽  
Babak Ehteshami Bejnordi ◽  
Francisco Beca ◽  
...  
Keyword(s):  
Breast Tumor ◽  
Tumor Proliferation ◽  
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 TAMI-08. A TALE OF TWO TELOMERE MAINTENANCE MECHANISMS: TERT EXPRESSION AND THE ALT PATHWAY INDUCE UNIQUE MRS-DETECTABLE METABOLIC REPROGRAMMING IN LOW-GRADE GLIOMAS

2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii214-ii214
Author(s):  
Pavithra Viswanath ◽  
Georgios Batsios ◽  
Anne Marie Gillespie ◽  
Hema Artee Luchman ◽  
Joseph Costello ◽  
...  
Keyword(s):  
Treatment Response ◽  
Metabolic Reprogramming ◽  
Telomere Maintenance ◽  
Imaging Biomarker ◽  
Low Grade ◽  
Tumor Proliferation ◽  
Nicotinamide Phosphoribosyltransferase ◽  
Non Invasive ◽  
Key Enzyme ◽  
Tert Expression

Abstract Telomeres are nucleoprotein structures at chromosomal ends that shorten with cell division and constitute a natural barrier to proliferation. In order to proliferate indefinitely, all tumors require a telomere maintenance mechanism (TMM). Telomerase reverse transcriptase (TERT) expression is the TMM in most tumors, including low-grade oligodendrogliomas (LGOGs). In contrast, low-grade astrocytomas (LGAs) use the alternative lengthening of telomeres (ALT) pathway as their TMM. As molecular hallmarks of tumor proliferation, TMMs are attractive tumor biomarkers and therapeutic targets. Non-invasive imaging of TMM status will, therefore, allow assessment of tumor proliferation and treatment response. However, translational methods of imaging TMM status are lacking. Here, we show that TERT expression and the ALT pathway are associated with unique magnetic resonance spectroscopy (MRS)-detectable metabolic reprogramming in LGOGs and LGAs respectively. In genetically-engineered and patient-derived LGOG models, TERT expression is linked to elevated 1H-MRS-detectable NAD(P)/H, glutathione, aspartate and AXP. In contrast, the ALT pathway in LGAs is associated with higher α-ketoglutarate, glutamate, alanine and AXP. Importantly, elevated flux of hyperpolarized [1-13C]-alanine to pyruvate, which depends on α-ketoglutarate, is a non-invasive in vivo imaging biomarker of the ALT pathway in LGAs while elevated flux of hyperpolarized [1-13C]-alanine to lactate, which depends on NADH, is an imaging biomarker of TERT expression in LGOGs. Mechanistically, the ALT pathway in LGAs is linked to higher glutaminase (GLS), a key enzyme for α-ketoglutarate biosynthesis while TERT expression in LGOGs is associated with elevated nicotinamide phosphoribosyltransferase (NAMPT), a key enzyme for NADH biosynthesis. Notably, TERT expression and the ALT pathway are linked to MRS-detectable metabolic reprogramming in LGOG and LGA patient biopsies, emphasizing the clinical validity of our observations. Collectively, we have identified unique metabolic signatures of TMM status that integrate critical oncogenic information with noninvasive imaging modalities that can improve diagnosis and treatment response monitoring for LGOG and LGA patients.

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