scholarly journals Identification and Validation of Efficacy of Immunological Therapy for Lung Cancer From Histopathological Images Based on Deep Learning

2021 ◽  
Vol 12 ◽  
Author(s):  
Yachao Yang ◽  
Jialiang Yang ◽  
Yuebin Liang ◽  
Bo Liao ◽  
Wen Zhu ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Deep Learning ◽  
Cancer Metastasis ◽  
Target Therapy ◽  
Immune Therapy ◽  
Area Under The Curve ◽  
Predictive Marker ◽  
Immunological Therapy ◽  
Study Results ◽  
Model Training

Cancer immunotherapy, as a novel treatment against cancer metastasis and recurrence, has brought a significantly promising and effective therapy for cancer treatments. At present, programmed death 1 (PD-1) and programmed cell death-Ligand 1 (PD-L1) treatment for lung cancer is primarily recognized as an immune checkpoint inhibitor (ICI) to play an anti-tumor effect; however, it remains uncertain regarding of its efficacy though. Thereafter, tumor mutation burden (TMB) was recognized as a high-potential to be a predictive marker for the immune therapy, but it is invasive and costly. Therefore, discovering more immune-related biomarkers that have a guiding role in immunotherapy is a crucial step in the development of immunotherapy. In our study, we proposed a deep convolutional neural network (CNN)-based framework, DeepLRHE, which can efficiently analyze immunological stained pathological images of lung cancer tissues, as well as to identify and explore pathogenesis which can be used for immunological treatment in clinical field. In this study, we used 180 whole slice images (WSIs) of lung cancer downloaded from TCGA which was model training and validation. After two cross-validation used for this model, we compared with the area under the curve (AUC) of multiple mutant genes, TP53 had highest AUC, which reached 0.87, and EGFR, DNMT3A, PBRM1, STK11 also reached ranged from 0.71 to 0.84. The study results showed that the deep learning can used to assist health professionals for target-therapy as well as immunotherapies, therefore to improve the disease prognosis.

scholarly journals Extracting a biologically latent space of lung cancer epigenetics with variational autoencoders

2019 ◽  
Vol 20 (S18) ◽  
Author(s):  
Zhenxing Wang ◽  
Yadong Wang
Keyword(s):  
Lung Cancer ◽  
Dna Methylation ◽  
Deep Learning ◽  
Malignant Tumors ◽  
Methylation Data ◽  
Cancer Epigenetics ◽  
Latent Space ◽  
Unsupervised Deep Learning ◽  
Latent Representations ◽  
Model Training

Abstract Background Lung cancer is one of the most malignant tumors, causing over 1,000,000 deaths each year worldwide. Deep learning has brought success in many domains in recent years. DNA methylation, an epigenetic factor, is used for model training in many studies. There is an opportunity for deep learning methods to analyze the lung cancer epigenetic data to determine their subtypes for appropriate treatment. Results Here, we employ variational autoencoders (VAEs), an unsupervised deep learning framework, on 450K DNA methylation data of TCGA-LUAD and TCGA-LUSC to learn latent representations of the DNA methylation landscape. We extract a biologically relevant latent space of LUAD and LUSC samples. It is showed that the bivariate classifiers on the further compressed latent features could classify the subtypes accurately. Through clustering of methylation-based latent space features, we demonstrate that the VAEs can capture differential methylation patterns about subtypes of lung cancer. Conclusions VAEs can distinguish the original subtypes from manually mixed methylation data frame with the encoded features of latent space. Further applications about VAEs should focus on fine-grained subtypes identification for precision medicine.

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 Artificial Intelligence to Predict Risk of Lung Cancer with Electronic Medical Records: A Deep Learning and Big Data Approach (Preprint)

2020 ◽  
Author(s):  
Marvin Chia-Han Yeh ◽  
Yu-Chuan(Jack) Li ◽  
Yu-Hsiang Wang ◽  
Hsuan-Chia Yang ◽  
Kuan-Jen Bai ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Lung Cancer ◽  
Deep Learning ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Area Under The Curve ◽  
Lung Cancer Screening ◽  
Research Database ◽  
Cancer Group ◽  
And Gender

BACKGROUND Artificial intelligence can integrate complex features and may be used to predict the risk of developing lung cancer, thereby decreasing the need for unnecessary and expensive diagnostic interventions. OBJECTIVE Using electronic medical records to pre-screening patient’s risk for developing lung cancer. METHODS Two million participants were randomly selected from the Taiwan National Health Insurance Research Database from 1999 to 2013; We built a predictive lung cancer screening model with neural networks that were trained and validated using pre-2012 data and tested prospectively on post-2012 data. An age- and gender-matched subgroup that is 10 times larger than the original lung cancer group was used to assess the predictive power of EMR. Discrimination (area under the curve [AUC]) and calibration analyses were performed. RESULTS The analysis included 11,617 cases of lung cancer and 1,423,154 controls. The model achieved an AUC of 0.90 for the overall population and 0.87 in patients >55 years of age. The AUC in the matched subgroup was 0.82. The positive predictive value was highest (14.3%) among those >55-years-old with a preexisting history of lung disease. CONCLUSIONS Our model achieved excellent performance at predicting lung cancer within one year and may be deployed for digital patient screening. Deep learning facilitates the effective use of EMRs to identify individuals at high risk for developing lung cancer.

DESIGN AN “IN HOUSE” SOFTWARE FOR SCREENING PREECLAMPSIA BY MATERNAL FACTORS AND MEAN ARTERIAL PRESSURE AT 11 – 13 WEEKS IN COMMUNE HEALTH CENTERS.

2015 ◽  
pp. 115-126
Author(s):  
Viet Nhan Nguyen ◽  
Ngoc Thanh Cao ◽  
Thi Minh Thi Ha ◽  
Van Duc Vo ◽  
Quang Vinh Truong ◽  
...  
Keyword(s):  
Mean Arterial Pressure ◽  
Arterial Pressure ◽  
Gestational Hypertension ◽  
Area Under The Curve ◽  
Health Centers ◽  
Maternal Factors ◽  
Good Tool ◽  
Uterine Artery Doppler ◽  
Study Results ◽  
In Pregnancy

Objective: Design an “in house” software for screening preeclampsia by maternal factors and mean arterial pressure at 11 – 13 gestational weeks in commune health centers. Methods: Based on the algorithms for calculating the risk of preeclampsia (PE) by maternal factors and mean artirial pressure at 11 - 13 gestational weeks in the study results of the authors, an “in house” software was deigned in Excel. The results of prediction preeclampsia by The Fetal Medicine Foundation (FMF)(version 2.3) were compared with the results by “in house” software in 1110 singleton pregnant women. Results: The “in house” software met the requirements for calculating the risks of PE and save data. FMF risk for gestational hypertension disorder in pregnancy by maternal factors, mean arterial pressure,uterine artery Doppler and PAPP-A has an area under the curve of 0.68 (95%CI: 0.59 – 0.78). The “in house” software risk for gestational hypertension in pregnancy by maternal factors, mean arterial pressure has an area under the curve of 0.643 (0.55 – 0.73) There was no statistically significant different between two programs (p:0.52). The risk cut-off 1:50 in the prediction of gestational hypertension of the “in house” software was used to identify the group of high risk with detetion rate (DR) 28.6% (95%CI: 14.9-42.2) comparing to 40.5% (95%CI:25.6-55.3) of FMF. Conclusion: The FMF version 2.3 is better but in the absence of Doppler ultrasound and PAPP-A test in the commune health cares, the “in house” software for screening PE is a good tool for councelling, following up and early intervention for PE.

Computer-Aided Lung Cancer Diagnosis Approaches Based on Deep Learning

2018 ◽  
Vol 30 (1) ◽  
pp. 90 ◽  
Author(s):  
Peng Zhang ◽  
Xinnan Xu ◽  
Hongwei Wang ◽  
Yuanli Feng ◽  
Haozhe Feng ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Deep Learning ◽  
Cancer Diagnosis ◽  
Lung Cancer Diagnosis ◽  
Computer Aided

Exhaustive Review on Lung Cancers: Novel Technologies

2019 ◽  
Vol 15 (9) ◽  
pp. 873-883
Author(s):  
Sajad Khan ◽  
Shahid Ali ◽  
Muhammad
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Immune Therapy ◽  
Small Cell ◽  
X Rays ◽  
Small Cell Lung ◽  
Microscopic Appearance ◽  
Lung Cancers ◽  
Novel Technologies

Background:Lung cancers or (Bronchogenic-Carcinomas) are the disease in certain parts of the lungs in which irresistible multiplication of abnormal cells leads to the inception of a tumor. Lung cancers consisting of two substantial forms based on the microscopic appearance of tumor cells are: Non-Small-Cell-Lung-Cancer (NSCLC) (80 to 85%) and Small-Cell-Lung-Cancer (SCLC) (15 to 20%).Discussion:Lung cancers are existing luxuriantly across the globe and the most prominent cause of death in advanced countries (USA & UK). There are many causes of lung cancers in which the utmost imperative aspect is the cigarette smoking. During the early stage, there is no perspicuous sign/symptoms but later many symptoms emerge in the infected individual such as insomnia, headache, pain, loss of appetite, fatigue, coughing etc. Lung cancers can be diagnosed in many ways, such as history, physical examination, chest X-rays and biopsy. However, after the diagnosis and confirmation of lung carcinoma, various treatment approaches are existing for curing of cancer in different stages such as surgery, radiation therapy, chemotherapy, and immune therapy. Currently, novel techniques merged that revealed advancements in detection and curing of lung cancer in which mainly includes: microarray analysis, gene expression profiling.Conclusion:Consequently, the purpose of the current analysis is to specify and epitomize the novel literature pertaining to the development of cancerous cells in different parts of the lung, various preeminent approaches of prevention, efficient diagnostic procedure, and treatments along with novel technologies for inhibition of cancerous cell growth in advance stages.

Wound Healing after Trauma May Predispose to Lung Cancer Metastasis

2011 ◽  
Vol 44 (5) ◽  
pp. 591-596 ◽  
Author(s):  
Nicholas D. Walter ◽  
Pamela L. Rice ◽  
Elizabeth F. Redente ◽  
Emily F. Kauvar ◽  
Lisa Lemond ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Wound Healing ◽  
Cancer Metastasis ◽  
Lung Cancer Metastasis

scholarly journals Erianthridin suppresses non-small-cell lung cancer cell metastasis through inhibition of Akt/mTOR/p70S6K signaling pathway

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sutthaorn Pothongsrisit ◽  
Kuntarat Arunrungvichian ◽  
Yoshihiro Hayakawa ◽  
Boonchoo Sritularak ◽  
Supachoke Mangmool ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cell Migration ◽  
Protein Kinase ◽  
Small Cell Lung Cancer ◽  
Cancer Metastasis ◽  
Small Cell ◽  
Migration And Invasion ◽  
Lung Cancer Cell ◽  
Small Cell Lung ◽  
Cell Metastasis

AbstractCancer metastasis is a major cause of the high mortality rate in lung cancer patients. The cytoskeletal rearrangement and degradation of extracellular matrix are required to facilitate cell migration and invasion and the suppression of these behaviors is an intriguing approach to minimize cancer metastasis. Even though Erianthridin (ETD), a phenolic compound isolated from the Thai orchid Dendrobium formosum exhibits various biological activities, the molecular mechanism of ETD for anti-cancer activity is unclear. In this study, we found that noncytotoxic concentrations of ETD (≤ 50 μM) were able to significantly inhibit cell migration and invasion via disruption of actin stress fibers and lamellipodia formation. The expression of matrix metalloproteinase-2 (MMP-2) and MMP-9 was markedly downregulated in a dose-dependent manner after ETD treatment. Mechanistic studies revealed that protein kinase B (Akt) and its downstream effectors mammalian target of rapamycin (mTOR) and p70 S6 kinase (p70S6K) were strongly attenuated. An in silico study further demonstrated that ETD binds to the protein kinase domain of Akt with both hydrogen bonding and van der Waals interactions. In addition, an in vivo tail vein injection metastasis study demonstrated a significant effect of ETD on the suppression of lung cancer cell metastasis. This study provides preclinical information regarding ETD, which exhibits promising antimetastatic activity against non-small-cell lung cancer through Akt/mTOR/p70S6K-induced actin reorganization and MMPs expression.

scholarly journals FlowCell-enriched circulating tumor cells as a predictor of lung cancer metastasis

Human Cell ◽  
2021 ◽  
Author(s):  
Yan Lu ◽  
Yushuang Zheng ◽  
Yuhong Wang ◽  
Dongmei Gu ◽  
Jun Zhang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Tumor Cells ◽  
Circulating Tumor Cells ◽  
Cancer Metastasis ◽  
Lung Tumor ◽  
Early Stage ◽  
High Rate ◽  
Early Stage Lung Cancer ◽  
Lung Cancer Metastasis ◽  
Number Of Patients

AbstractLung cancer is the most fetal malignancy due to the high rate of metastasis and recurrence after treatment. A considerable number of patients with early-stage lung cancer relapse due to overlooked distant metastasis. Circulating tumor cells (CTCs) are tumor cells in blood circulation that originated from primary or metastatic sites, and it has been shown that CTCs are critical for metastasis and prognosis in various type of cancers. Here, we employed novel method to capture, isolate and classify CTC with FlowCell system and analyzed the CTCs from a cohort of 302 individuals. Our results illustrated that FlowCell-enriched CTCs effectively differentiated benign and malignant lung tumor and the total CTC counts increased as the tumor developed. More importantly, we showed that CTCs displayed superior sensitivity and specificity to predict lung cancer metastasis in comparison to conventional circulating biomarkers. Taken together, our data suggested CTCs can be used to assist the diagnosis of lung cancer as well as predict lung cancer metastasis. These findings provide an alternative means to screen early-stage metastasis.

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