scholarly journals Using a convolutional neural network for classification of squamous and non-squamous non-small cell lung cancer based on diagnostic histopathology HES images

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Anne Laure Le Page ◽  
Elise Ballot ◽  
Caroline Truntzer ◽  
Valentin Derangère ◽  
Alis Ilie ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Validation Cohort ◽  
External Validation ◽  
Small Cell ◽  
Small Cell Lung ◽  
Tissue Micro Array ◽  
Micro Array ◽  
Nsclc Patients

AbstractHistological stratification in metastatic non-small cell lung cancer (NSCLC) is essential to properly guide therapy. Morphological evaluation remains the basis for subtyping and is completed by additional immunohistochemistry labelling to confirm the diagnosis, which delays molecular analysis and utilises precious sample. Therefore, we tested the capacity of convolutional neural networks (CNNs) to classify NSCLC based on pathologic HES diagnostic biopsies. The model was estimated with a learning cohort of 132 NSCLC patients and validated on an external validation cohort of 65 NSCLC patients. Based on image patches, a CNN using InceptionV3 architecture was trained and optimized to classify NSCLC between squamous and non-squamous subtypes. Accuracies of 0.99, 0.87, 0.85, 0.85 was reached in the training, validation and test sets and in the external validation cohort. At the patient level, the CNN model showed a capacity to predict the tumour histology with accuracy of 0.73 and 0.78 in the learning and external validation cohorts respectively. Selecting tumour area using virtual tissue micro-array improved prediction, with accuracy of 0.82 in the external validation cohort. This study underlines the capacity of CNN to predict NSCLC subtype with good accuracy and to be applied to small pathologic samples without annotation.

scholarly journals Using a Convolutional Neural Network for Classification of Squamous and Non-Squamous Non-Small Cell Lung Cancer Based on Diagnostic Histopathology HES Images

2021 ◽  
Author(s):  
Anne Laure Le Page ◽  
Elise Ballot ◽  
Caroline Truntzer ◽  
Valentin Derangère ◽  
Alis Ilie ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Validation Cohort ◽  
External Validation ◽  
Small Cell ◽  
Small Cell Lung ◽  
Tissue Micro Array ◽  
Micro Array ◽  
Nsclc Patients

Abstract Histological stratification in metastatic non-small cell lung cancer (NSCLC) is essential to properly guide therapy. Morphological evaluation remains the basis for subtyping and is completed by additional immunohistochemistry (IHC) labelling to confirm the diagnosis, which delays molecular analysis and utilises precious sample. Therefore, we tested the capacity of convolutional neural networks (CNNs) to classify NSCLC based on pathologic HES diagnostic biopsies. The model was estimated with a learning cohort of 132 NSCLC patients and validated on an external validation cohort of 65 NSCLC patients. Based on image patches, a CNN using InceptionV3 architecture was trained and optimized to classify NSCLC between squamous and non-squamous subtypes. Accuracies of 0.99, 0.85, 0.87, 0.85 was reached in the training, validation and test sets and in the external validation cohort. At the patient level, the CNN model showed a capacity to predict the tumour histology with accuracy of 0.73 and 0.78 in the learning and external validation cohorts respectively. Selecting tumour area using virtual Tissue Micro-Array (TMA) improved prediction, with accuracy of 0.79 in both learning and external validation cohorts. This study underlines the capacity of CNN to predict NSCLC subtype with good accuracy and to be applied to small pathologic samples without annotation.

scholarly journals Development and External Validation of a Nomogram for Predicting Cancer-Specific Survival of Non-Small Cell Lung Cancer Patients With Ipsilateral Pleural Dissemination

2021 ◽  
Vol 11 ◽  
Author(s):  
Zhenfan Wang ◽  
Hao Li ◽  
Taorui Liu ◽  
Zewen Sun ◽  
Fan Yang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Prognostic Factors ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
External Validation ◽  
Small Cell ◽  
Small Cell Lung ◽  
Cancer Specific Survival ◽  
Pleural Dissemination ◽  
Nsclc Patients

BackgroundNon-small-cell lung cancer (NSCLC) patients with ipsilateral pleural dissemination are defined as M1a in the eighth of American Joint Committee on Cancer (AJCC) TNM staging. We aimed to build a nomogram to predict lung cancer specific survival (LCSS) of NSCLC patients with ipsilateral pleural dissemination and to compare the impact of primary tumor resection (PTR) on LCSS among patients with different features.MethodsA total of 3,918 NSCLC patients with ipsilateral pleural dissemination were identified from the Surveillance, Epidemiology, and End Results (SEER) database. We selected and integrated significant prognostic factors based on competing risk regression to build a nomogram. The model was subjected to internal validation within SEER cohort and external validation with the cohort of 97 patients from Peking University People’s Hospital.ResultsAge (P < 0.001), gender (P = 0.037), T stage (P = 0.002), N stage (P < 0.001), metastasis pattern (P = 0.005), chemotherapy (P < 0.001), and PTR (P < 0.001) were independent prognostic factors. The calibration curves presented a good consistency and the Harrell’s C-index of nomogram were 0.682 (95%CI: 0.673–0.691), 0.687 (95%CI: 0.670–0.704) and 0.667 (95%CI: 0.584–0.750) in training, internal, and external validation cohort, respectively. Interaction tests suggested a greater LCSS difference caused by PTR in patients without chemotherapy (P < 0.001).ConclusionsWe developed a nomogram based on competing risk regression to reliably predict prognosis of NSCLC patients with ipsilateral pleural dissemination and validated this nomogram in an external Chinese cohort. This novel nomogram might be a practical tool for clinicians to anticipate the 1-, 3- and 5-year LCSS for NSCLC patients with pleural dissemination. Subgroup analysis indicated that patients without chemotherapy could get more benefit from PTR. In order to assess the role of PTR in the management of M1a patients more accurately, further prospective study would be urgently required.

scholarly journals Biomarkers and Gene Signatures to Predict Durable Response to Pembrolizumab in Non-Small Cell Lung Cancer

Cancers ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 3828
Author(s):  
Anello Marcello Poma ◽  
Rossella Bruno ◽  
Iacopo Pietrini ◽  
Greta Alì ◽  
Giulia Pasquini ◽  
...  
Keyword(s):  
Lung Cancer ◽  
T Cells ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Immune Cell ◽  
Small Cell ◽  
Small Cell Lung ◽  
First Line ◽  
Durable Response ◽  
Nsclc Patients

Pembrolizumab has been approved as first-line treatment for advanced Non-small cell lung cancer (NSCLC) patients with tumors expressing PD-L1 and in the absence of other targetable alterations. However, not all patients that meet these criteria have a durable benefit. In this monocentric study, we aimed at refining the selection of patients based on the expression of immune genes. Forty-six consecutive advanced NSCLC patients treated with pembrolizumab in first-line setting were enrolled. The expression levels of 770 genes involved in the regulation of the immune system was analysed by the nanoString system. PD-L1 expression was evaluated by immunohistochemistry. Patients with durable clinical benefit had a greater infiltration of cytotoxic cells, exhausted CD8, B-cells, CD45, T-cells, CD8 T-cells and NK cells. Immune cell scores such as CD8 T-cell and NK cell were good predictors of durable response with an AUC of 0.82. Among the immune cell markers, XCL1/2 showed the better performance in predicting durable benefit to pembrolizumab, with an AUC of 0.85. Additionally, CD8A, CD8B and EOMES showed a high specificity (>0.86) in identifying patients with a good response to treatment. In the same series, PD-L1 expression levels had an AUC of 0.61. The characterization of tumor microenvironment, even with the use of single markers, can improve patients’ selection for pembrolizumab treatment.

scholarly journals Circulating Biomarkers of Response and Toxicity of Immunotherapy in Advanced Non-Small Cell Lung Cancer (NSCLC): A Comprehensive Review

Cancers ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1794
Author(s):  
Alice Indini ◽  
Erika Rijavec ◽  
Francesco Grossi
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Liquid Biopsy ◽  
Cytotoxic T Lymphocyte ◽  
Checkpoint Inhibitors ◽  
Small Cell ◽  
Circulating Biomarkers ◽  
Small Cell Lung ◽  
Nsclc Patients

Immune checkpoint inhibitors (ICIs) targeting the programmed cell death (PD)-1 protein and its ligand, PD-L1, and cytotoxic T-lymphocyte-associated antigen (CTLA)-4, have revolutionized the management of patients with advanced non-small cell lung cancer (NSCLC). Unfortunately, only a small portion of NSCLC patients respond to these agents. Furthermore, although immunotherapy is usually well tolerated, some patients experience severe immune-related adverse events (irAEs). Liquid biopsy is a non-invasive diagnostic procedure involving the isolation of circulating biomarkers, such as circulating tumor cells (CTC), cell-free DNA (cfDNA), and microRNAs (miRNAs). Thanks to recent advances in technologies, such as next-generation sequencing (NGS) and digital polymerase chain reaction (dPCR), liquid biopsy has become a useful tool to provide baseline information on the tumor, and to monitor response to treatments. This review highlights the potential role of liquid biomarkers in the selection of NSCLC patients who could respond to immunotherapy, and in the identification of patients who are most likely to experience irAEs, in order to guide improvements in care.

scholarly journals 1318P Association between soluble PD-L1 and prognosis of non-small cell lung cancer (NSCLC) patients treated with immunotherapy

2020 ◽  
Vol 31 ◽  
pp. S851
Author(s):  
C. Dellepiane ◽  
S. Coco ◽  
M.G. Dal Bello ◽  
G. Rossi ◽  
E. Rijavec ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Small Cell Lung ◽  
Nsclc Patients

scholarly journals Molecular profile of KRAS G12C-mutant colorectal and non-small-cell lung cancer

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Luiz Henrique Araujo ◽  
Bianca Mendes Souza ◽  
Laura Rabelo Leite ◽  
Sabrina A. F. Parma ◽  
Natália P. Lopes ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Driver Mutations ◽  
Molecular Profile ◽  
Common Mutation ◽  
Small Cell Lung ◽  
The South ◽  
Nsclc Patients

Abstract Background KRAS is the most frequently mutated oncogene in cancer, however efforts to develop targeted therapies have been largely unsuccessful. Recently, two small-molecule inhibitors, AMG 510 and MRTX849, have shown promising activity in KRAS G12C-mutant solid tumors. The current study aims to assess the molecular profile of KRAS G12C in colorectal (CRC) and non-small-cell lung cancer (NSCLC) tested in a clinical certified laboratory. Methods CRC and NSCLC samples submitted for KRAS testing between 2017 and 2019 were reviewed. CRC samples were tested for KRAS and NRAS by pyrosequencing, while NSCLC samples were submitted to next generation sequencing of KRAS, NRAS, EGFR, and BRAF. Results The dataset comprised 4897 CRC and 4686 NSCLC samples. Among CRC samples, KRAS was mutated in 2354 (48.1%). Most frequent codon 12 mutations were G12D in 731 samples (14.9%) and G12V in 522 (10.7%), followed by G12C in 167 (3.4%). KRAS mutations were more frequent in females than males (p = 0.003), however this difference was exclusive of non-G12C mutants (p < 0.001). KRAS mutation frequency was lower in the South and North regions (p = 0.003), but again KRAS G12C did not differ significantly (p = 0.80). In NSCLC, KRAS mutations were found in 1004 samples (21.4%). As opposed to CRC samples, G12C was the most common mutation in KRAS, in 346 cases (7.4%). The frequency of KRAS G12C was higher in the South and Southeast regions (p = 0.012), and lower in patients younger than 50 years (p < 0.001). KRAS G12C mutations were largely mutually exclusive with other driver mutations; only 11 NSCLC (3.2%) and 1 CRC (0.6%) cases had relevant co-mutations. Conclusions KRAS G12C presents in frequencies higher than several other driver mutations, and may represent a large volume of patients in absolute numbers. KRAS testing should be considered in all CRC and NSCLC patients, independently of clinical or demographic characteristics.

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