Faculty Opinions recommendation of Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning.

Earlier, Lung cancer is the primary cause of cancer deaths worldwide among both men and women, with more than 1 million deaths annually. Lung Cancer have been widest difficulty faced by humans over recent couple of decades. When a person has lung cancer, they have abnormal cells that cluster together to form a tumor. A cancerous tumor is a group of cancer cells that can grow into and destroy nearby tissue. It can also spread to other parts of the body. There are two main types of lung cancer:1. Non-small cell lung cancer, 2. Small cell lung cancer. Non- small cell lung cancer has four main stages. In this research we are classifying four stages of lung cancer. Lung cancer detection at early stage has become very important. Currently many techniques are used based on image processing and deep learning techniques for lung cancer classification. For that lung patient Computer Tomography (CT) scan images are used to detect and lung nodules and classify lung cancer stage of that nodules. In this re- search we compare different Machine learning (SVM, KNN, RF etc.) techniques with deep learning (CNN, CDNN) techniques using different parameters accuracy, precision and recall. In this Research paper we com- pare all existing approach and find our better result for future application.

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Classifying non-small cell lung cancer types and transcriptomic subtypes using convolutional neural networks

Journal of the American Medical Informatics Association ◽

10.1093/jamia/ocz230 ◽

2020 ◽

Vol 27 (5) ◽

pp. 757-769 ◽

Cited By ~ 3

Author(s):

Kun-Hsing Yu ◽

Feiran Wang ◽

Gerald J Berry ◽

Christopher Ré ◽

Russ B Altman ◽

...

Keyword(s):

Lung Cancer ◽

Neural Networks ◽

Squamous Cell Carcinoma ◽

Small Cell Lung Cancer ◽

Cell Carcinoma ◽

Cell Lung Cancer ◽

Small Cell ◽

Small Cell Lung ◽

Histopathology Images ◽

Independent Cohort

Abstract Objective Non-small cell lung cancer is a leading cause of cancer death worldwide, and histopathological evaluation plays the primary role in its diagnosis. However, the morphological patterns associated with the molecular subtypes have not been systematically studied. To bridge this gap, we developed a quantitative histopathology analytic framework to identify the types and gene expression subtypes of non-small cell lung cancer objectively. Materials and Methods We processed whole-slide histopathology images of lung adenocarcinoma (n = 427) and lung squamous cell carcinoma patients (n = 457) in the Cancer Genome Atlas. We built convolutional neural networks to classify histopathology images, evaluated their performance by the areas under the receiver-operating characteristic curves (AUCs), and validated the results in an independent cohort (n = 125). Results To establish neural networks for quantitative image analyses, we first built convolutional neural network models to identify tumor regions from adjacent dense benign tissues (AUCs > 0.935) and recapitulated expert pathologists’ diagnosis (AUCs > 0.877), with the results validated in an independent cohort (AUCs = 0.726-0.864). We further demonstrated that quantitative histopathology morphology features identified the major transcriptomic subtypes of both adenocarcinoma and squamous cell carcinoma (P < .01). Discussion Our study is the first to classify the transcriptomic subtypes of non-small cell lung cancer using fully automated machine learning methods. Our approach does not rely on prior pathology knowledge and can discover novel clinically relevant histopathology patterns objectively. The developed procedure is generalizable to other tumor types or diseases.

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3 / Automatic classification of non-small cell lung cancer histologic subtypes by deep learning

10.26226/morressier.5b4709886f4cb300109520da ◽

2018 ◽

Author(s):

Ruben Casanova

Keyword(s):

Lung Cancer ◽

Deep Learning ◽

Small Cell Lung Cancer ◽

Cell Lung Cancer ◽

Automatic Classification ◽

Small Cell ◽

Small Cell Lung ◽

Histologic Subtypes

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Deep learning-based predictive biomarker for immune checkpoint inhibitor response in metastatic non-small cell lung cancer.

Journal of Clinical Oncology ◽

10.1200/jco.2019.37.15_suppl.9094 ◽

2019 ◽

Vol 37 (15_suppl) ◽

pp. 9094-9094

Author(s):

Sehhoon Park ◽

Chang Ho Ahn ◽

Geunyoung Jung ◽

Sarah Lee ◽

Kyunghyun Paeng ◽

...

Keyword(s):

Lung Cancer ◽

Deep Learning ◽

Small Cell Lung Cancer ◽

Cell Lung Cancer ◽

Immune Checkpoint ◽

Immune Checkpoint Inhibitor ◽

Checkpoint Inhibitor ◽

Predictive Biomarker ◽

Small Cell ◽

Small Cell Lung

9094 Background: In the era of immunotherapy, immune checkpoint inhibitor (ICI) has changed the treatment paradigm in metastatic non-small cell lung cancer (NSCLC). Along with clinical trials, there is an ongoing investigation to discover the predictive biomarker of ICI which so far has unsatisfactory reliability. As an effort to enhance the predictive value of ICI treatment, we applied deep learning and developed artificial intelligent (AI) score (range from 0 to 1) to analyze the specific context of immune-tumor microenvironment (TME) extracted by scanned images from H&E slides. Methods: As a ground work, deep learning-based H&E image analyzer, Lunit SCOPE, has been trained with H&E images (n = 1824) from ICI naive NSCLC samples. For the calculation of AI score, training was conducted using responder/non-responder labeled ICI treated samples from the exploratory cohort. The ICI responder was defined as the patient with a best overall response of partial or complete response and stable disease for more than 6 months. The positivity of PD-L1 immunohistochemistry (IHC) was assessed manually by pathologists. Results: The exploratory cohort is composed of NSCLC patients treated with ICI (n = 189) in Samsung Medical Center, and response to ICI was observed in 72 (38.1%) patients. Median follow-up duration was 6.8 months (6.6~8.2). Samples with PD-L1 IHC positive, defined by ≥ 1%, was observed in 138 (73.0%) patients. AI score was significant higher in the responder group (median: 0.391 vs 0.205, P = 6.14e-5), and the patients with AI score above the cut-off (0.337) showed a better response to ICI (odds ratio [OR] 3.47 P = 7.34e-5) which is higher than patients with PD-L1 ≥ 1% (OR 1.92, P = 0.069). High AI score group (n = 83) showed significantly favorable PFS compared to low AI score group (n = 106, median PFS: 5.1m vs 1.9m, hazard ratio [HR] 0.51, P = 9.6e-5) and this outcome was independent with PD-L1 status (P = 6.0e-5). In subgroup analysis, PFS of PD-L1 high / AI score high group (n = 63) had longer median PFS (6.7m) compared to both PD-L1 high / AI score low group (n = 70, 4.0m, P = 0.001) and PD-L1 low/AI score low group (n = 35, 1.9m, P = 4.0e-6). Tumor infiltrating lymphocyte (TIL) density of cancer epithelium was significantly correlated with AI score (Pearson’s r = 0.310, P = 1.43e-5), which suggests that AI score may partly reflect TME represented by TIL. Conclusions: The AI score by machine-learned information, extracted from H&E images without additional IHC stain, could predict responsiveness and PFS of ICI treatment independent of PD-L1 IHC positivity.

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