scholarly journals Machine learning for prediction of immunotherapy efficacy in non-small cell lung cancer from simple clinical and biological data

2021 ◽  
Author(s):  
Sébastien Benzekry ◽  
Mathieu Grangeon ◽  
Mélanie Karlsen ◽  
Maria Alexa ◽  
Isabella Bicalho-Frazeto ◽  
...  
Keyword(s):  
Machine Learning ◽  
Lung Cancer ◽  
Random Forest ◽  
Predictive Value ◽  
Performance Status ◽  
Small Cell ◽  
Biological Data ◽  
Learning Models ◽  
Small Cell Lung ◽  
Machine Learning Models

ABSTRACTBackgroundImmune checkpoint inhibitors (ICIs) are now a therapeutic standard in advanced non-small cell lung cancer (NSCLC), but strong predictive markers for ICIs efficacy are still lacking. We evaluated machine learning models built on simple clinical and biological data to individually predict response to ICIs.MethodsPatients with metastatic NSCLC who received ICI in second line or later were included. We collected clinical and hematological data and studied the association of this data with disease control rate (DCR), progression free survival (PFS) and overall survival (OS). Multiple machine learning (ML) algorithms were assessed for their ability to predict response.ResultsOverall, 298 patients were enrolled. The overall response rate and DCR were 15.3 % and 53%, respectively. Median PFS and OS were 3.3 and 11.4 months, respectively. In multivariable analysis, DCR was significantly associated with performance status (PS) and hemoglobin level (OR 0.58, p<0.0001; OR 1.8, p<0.001). These variables were also associated with PFS and OS and ranked top in random forest-based feature importance. Neutrophils-to-lymphocytes ratio was also associated with DCR, PFS and OS. The best ML algorithm was a random forest. It could predict DCR with satisfactory efficacy based on these three variables. Ten-fold cross-validated performances were: accuracy 0.68 ± 0.04, sensitivity 0.58 ± 0.08; specificity 0.78 ± 0.06; positive predictive value 0.70 ± 0.08; negative predictive value 0.68 ± 0.06; AUC 0.74 ± 0.03.ConclusionCombination of simple clinical and biological data could accurately predict disease control rate at the individual level.Highlights-Machine learning applied to a large set of NSCLC patients could predict efficacy of immunotherapy with a 69% accuracy using simple routine data-Hemoglobin levels and performance status were the strongest predictors and significantly associated with DCR, PFS and OS-Neutrophils-to-lymphocyte ratio was also associated with outcome-Benchmark of 8 machine learning models

scholarly journals Machine Learning for Prediction of Immunotherapy Efficacy in Non-Small Cell Lung Cancer from Simple Clinical and Biological Data

Cancers ◽  
2021 ◽  
Vol 13 (24) ◽  
pp. 6210
Author(s):  
Sébastien Benzekry ◽  
Mathieu Grangeon ◽  
Mélanie Karlsen ◽  
Maria Alexa ◽  
Isabella Bicalho-Frazeto ◽  
...  
Keyword(s):  
Machine Learning ◽  
Lung Cancer ◽  
Random Forest ◽  
Small Cell Lung Cancer ◽  
Disease Control ◽  
Cell Lung Cancer ◽  
Predictive Value ◽  
Small Cell ◽  
Biological Data ◽  
Small Cell Lung

Background: Immune checkpoint inhibitors (ICIs) are now a therapeutic standard in advanced non-small cell lung cancer (NSCLC), but strong predictive markers for ICIs efficacy are still lacking. We evaluated machine learning models built on simple clinical and biological data to individually predict response to ICIs. Methods: Patients with metastatic NSCLC who received ICI in second line or later were included. We collected clinical and hematological data and studied the association of this data with disease control rate (DCR), progression free survival (PFS) and overall survival (OS). Multiple machine learning (ML) algorithms were assessed for their ability to predict response. Results: Overall, 298 patients were enrolled. The overall response rate and DCR were 15.3% and 53%, respectively. Median PFS and OS were 3.3 and 11.4 months, respectively. In multivariable analysis, DCR was significantly associated with performance status (PS) and hemoglobin level (OR 0.58, p < 0.0001; OR 1.8, p < 0.001). These variables were also associated with PFS and OS and ranked top in random forest-based feature importance. Neutrophil-to-lymphocyte ratio was also associated with DCR, PFS and OS. The best ML algorithm was a random forest. It could predict DCR with satisfactory efficacy based on these three variables. Ten-fold cross-validated performances were: accuracy 0.68 ± 0.04, sensitivity 0.58 ± 0.08; specificity 0.78 ± 0.06; positive predictive value 0.70 ± 0.08; negative predictive value 0.68 ± 0.06; AUC 0.74 ± 0.03. Conclusion: Combination of simple clinical and biological data could accurately predict disease control rate at the individual level.

scholarly journals Using Nomogram and Machine Learning Models to Predict Non-Small Cell Lung Cancer Prognosis

2021 ◽  
Author(s):  
Haike Lei ◽  
Chun Liu ◽  
Zheng Xu ◽  
Na Hong ◽  
Xiaosheng Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Lung Cancer ◽  
Cox Regression ◽  
Small Cell ◽  
Seer Database ◽  
Learning Models ◽  
Small Cell Lung ◽  
Vital Status ◽  
Nsclc Patients ◽  
Machine Learning Models

Abstract BackgroundPatients with non-small cell lung cancer (NSCLC) often have a poor prognosis. Overall survival (OS) prediction through the early diagnosis of cancer has many benefits, such as allowing providers to design the best treatment plan for patients. In this study, we aimed to evaluate the prognostic factors in NSCLC patients, construct a nomogram, and develop machine learning models to predict the OS. We also conducted feature importance analysis to understand how relevant factors of NSCLC patients impact their OS.ResultsMultiple machine learning models were adopted in a retrospective cohort of patients from 2010 to 2015 in the Surveillance, Epidemiology, and End Results (SEER) database. Independent prognostic factors for NSCLC were determined using Cox proportional hazards regression analysis. We modeled OS and vital status as the outcomes and constructed and validated a nomogram to predict the OS of NSCLC. Furthermore, we applied logistic regression, random forest, XGBoost, decision tree, multilayer perceptron, and LightGBM to predict the patients’ vital status. We tested the prediction ability of the models and evaluated their performances using accuracy, sensitivity, specificity, precision, and the area under the receiver operating characteristic curve. A total of 34,567 patients selected from the SEER database that met our criteria were included in this study. The nomogram visualized the OS prediction results of the Cox regression model. Among the classifiers, XGBoost had the best prediction performance, with an area under the curve of 0.733.ConclusionsThe results demonstrated that machine learning-based classifier models are capable of predicting the outcomes of patients with NSCLC. And Cox regression model-based nomogram interpreted the results well and supports potential medical applications.

Document Preprocessing with TF-IDF to Improve the Polarity Classification Performance of Unstructured Sentiment Analysis

2020 ◽  
pp. 235-242
Author(s):  
Farrikh Alzami ◽  
Erika Devi Udayanti ◽  
Dwi Puji Prabowo ◽  
Rama Aria Megantara
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Random Forest ◽  
Sentiment Analysis ◽  
Classification Performance ◽  
Document Preparation ◽  
Learning Models ◽  
Polarity Classification ◽  
Negative Sentiment ◽  
Machine Learning Models

Sentiment analysis in terms of polarity classification is very important in everyday life, with the existence of polarity, many people can find out whether the respected document has positive or negative sentiment so that it can help in choosing and making decisions. Sentiment analysis usually done manually. Therefore, an automatic sentiment analysis classification process is needed. However, it is rare to find studies that discuss extraction features and which learning models are suitable for unstructured sentiment analysis types with the Amazon food review case. This research explores some extraction features such as Word Bags, TF-IDF, Word2Vector, as well as a combination of TF-IDF and Word2Vector with several machine learning models such as Random Forest, SVM, KNN and Naïve Bayes to find out a combination of feature extraction and learning models that can help add variety to the analysis of polarity sentiments. By assisting with document preparation such as html tags and punctuation and special characters, using snowball stemming, TF-IDF results obtained with SVM are suitable for obtaining a polarity classification in unstructured sentiment analysis for the case of Amazon food review with a performance result of 87,3 percent.

Phase II study of docetaxel and cisplatin in advanced non-small-cell lung cancer.

1998 ◽  
Vol 16 (5) ◽  
pp. 1948-1953 ◽  
Author(s):  
J Zalcberg ◽  
M Millward ◽  
J Bishop ◽  
M McKeage ◽  
A Zimet ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Phase Ii ◽  
Advanced Nsclc ◽  
Response Rate ◽  
Phase Ii Study ◽  
Performance Status ◽  
Objective Response ◽  
Small Cell ◽  
Small Cell Lung ◽  
Grade 3

PURPOSE Docetaxel (Taxotere, Rhone-Poulenc Rorer, Antony, France) and cisplatin are two of the most active single agents used in the treatment of non-small-cell lung cancer (NSCLC). A recently reported phase I study of the combination of docetaxel and cisplatin recommended a dose of 75 mg/m2 of both drugs every 3 weeks for subsequent phase II study. PATIENTS AND METHODS Eligible patients were aged 18 to 75 years with a World Health Organization (WHO) performance status < or = 2 and life expectancy > or = 12 weeks, with metastatic and/or locally advanced NSCLC proven histologically or cytologically. Patients were not permitted to have received prior chemotherapy, extensive radiotherapy, or any radiotherapy to the target lesion and must have had measurable disease. Concurrent treatment with colony-stimulating factors (CSFs) or prophylactic antibiotics was not permitted. Docetaxel (75 mg/m2) in 250 mL 5% dextrose was given intravenously (i.v.) over 1 hour immediately before cisplatin (75 mg/m2) in 500 mL normal saline given i.v. over 1 hour in 3-week cycles. Premedication included ondansetron, dexamethasone, promethazine, and standard hyperhydration with magnesium supplementation. RESULTS A total of 47 patients, two thirds of whom had metastatic disease, were entered onto this phase II study. The majority of patients were male (72%) and of good (WHO 0 to 1) performance status (85%). All 47 patients were assessable for toxicity and 36 were for response. Three patients were ineligible and eight (17%) discontinued treatment because of significant toxicity. In assessable patients, the overall objective response rate was 38.9% (95% confidence limits [CL], 23.1% to 56.5%), 36.1% had stable disease, and 25% progressive disease. On an intention-to-treat analysis, the objective response rate was 29.8%. Median survival was 9.6 months and estimated 1-year survival was 33%. Significant (grade 3/4) toxicities included nausea (26%), hypotension (15%), diarrhea (13%), and dyspnea mainly related to chest infection (13%). One patient experienced National Cancer Institute (NCI) grade 3 neurosensory toxicity after eight cycles. Grade 3/4 neutropenia was common and occurred in 87% of patients, but thrombocytopenia > or = grade 3 was rare (one patient). Significant (grade 3/4) abnormalities of magnesium levels were common (24%). Febrile neutropenia occurred in 13% of patients and neutropenic infection in 11%, contributing to two treatment-related deaths. No neutropenic enterocolitis or severe fluid retention was reported. CONCLUSION Compared with other active regimens used in this setting, the combination of docetaxel and cisplatin in advanced NSCLC is an active regimen with a similar toxicity profile to other combination regimens.

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