Computer extracted measurements of vessel tortuosity on baseline CT scans to predict response to nivolumab immunotherapy for non-small cell lung cancer.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. 11566-11566
Author(s):  
Monica Khunger ◽  
Mehdi Alilou ◽  
Rajat Thawani ◽  
Anant Madabhushi ◽  
Vamsidhar Velcheti
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Lung Nodule ◽  
Small Cell ◽  
Ct Scans ◽  
Support Vector ◽  
Svm Classifier ◽  
Small Cell Lung ◽  
Vessel Tortuosity

11566 Background: Immune-checkpoint blockade treatments, particularly drugs targeting the programmed death-1 (PD-1) receptor, demonstrate promising clinical efficacy in patients with non-small cell lung cancer (NSCLC). We sought to evaluate whether computer extracted measurements of tortuosity of vessels in lung nodules on baseline CT scans in NSCLC patients(pts) treated with a PD-1 inhibitor, nivolumab could distinguish responders and non-responders. Methods: A total of 61 NSCLC pts who underwent treatment with nivolumab were included in this study. Pts who did not receive nivolumab after 2 cycles due to lack of response or progression per RECIST were classified as ‘non-responders’, patients who had radiological response per RECIST or had clinical benefit (defined as stable disease >10 cycles) were classified as ‘responders’. A total of 35 quantitative tortuosity features of the vessels associated with lung nodule were investigated. In the training cohort (N=33), the features were ranked in their ability to identify responders to nivolumab using a support vector machine (SVM) classifier. The three most informative features were then used for training the SVM, which was then validated on a cohort of N=28 pts. Results: The maximum curvature ( f1), standard deviation of the torsion ( f2) and mean curvature ( f3) were identified as the most discriminating features. The area under Receiver operating characteristic (ROC) curve (AUC) of the SVM was 0.84 for the training and 0.72 for the validation cohort. Conclusions: Vessel tortuosity features were able to distinguish responders from non-responders for patients with NSCLC treated with nivolumab. Large scale multi-site validation will need to be done to establish vessel tortuosity as a predictive biomarker for immunotherapy. [Table: see text]

Changes in computer extracted features of vessel tortuosity on CT scans post-treatment in responders compared to non-responders for non-small cell lung cancer on immunotherapy.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. 11518-11518 ◽  
Author(s):  
Vamsidhar Velcheti ◽  
Mehdi Alilou ◽  
Monica Khunger ◽  
Rajat Thawani ◽  
Anant Madabhushi
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Post Treatment ◽  
Ct Scans ◽  
Svm Classifier ◽  
Small Cell Lung ◽  
Vessel Tortuosity ◽  
Nsclc Patients

11518 Background: Immune-checkpoint blockade treatments demonstrate promising clinical efficacy in patients with non-small cell lung cancer (NSCLC). Nivolumab is a PD-1 inhibitor that is FDA approved for treatment of patients with chemotherapy refractory advanced NSCLC. The current standard clinical approach to evaluating tumor response is sub-optimal in defining clinical benefit from immunotherapy drugs. We sought to evaluate whether computer extracted measurements of vessel tortuosity significantly and differentially change post treatment between NSCLC patients who do and do not respond to immunotherapy. Methods: A total of 50 NSCLC patients including pre- and post- treatment CT scans were included in this study. The patients were either responders or non-responders to Nivolumab. Patients who did not receive Nivolumab after 2 cycles due to lack of response or progression as per RECIST were classified as ‘non-responders’. A total of 35 tortuosity features of the vessels around the lung nodules were investigated. In the training cohort (N = 25), the features were ranked based on the degree of change between pre- and post- treatment CT. The top 4 features were used for training a Support Vector Machine (SVM) classifier to identify which patients did and did not respond to immunotherapy on a validation cohort of N = 25 patients. Results: The top features identified were the ones associated with the curvature of the vessel branches. The AUC for the SVM classifier was 0.75 for the training and 0.79 for the test set. Conclusions: Changes in specific vessel tortuosity features between baseline and post-treatment CT scans following nivolumab were different between NSCLC patients who did and did not respond. Multi-site validation of the vessel tortuosity features is needed to establish it as a predictive biomarker for NSCLC patients treated with immunotherapy.

scholarly journals Prediction of histological grade using preoperative multi-sequence MRI-based radiomics signature in patients with non-small-cell lung cancer

2020 ◽  
Author(s):  
Xing Tang ◽  
Guoyan Bai ◽  
Yuanqiang Zhu ◽  
Hong Wang ◽  
Jian Zhang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Clinical Features ◽  
Cell Lung Cancer ◽  
Discrimination Performance ◽  
Predictive Performance ◽  
Small Cell ◽  
Support Vector ◽  
Svm Classifier ◽  
Small Cell Lung

Abstract Background: Non-small cell lung cancer (NSCLC) is treatable when caught early, yet limited non-invasive methods exist for grading NSCLC patients. In the present study, we aimed to examine the diagnostic utility of multi-sequence magnetic resonance imaging (MRI) radiomics and clinical features for grading NSCLC. Methods: In this retrospective study, 148 patients with postoperative pathologically-confirmed NSCLC were recruited. Both preoperative T2-weighted imaging (T2WI) and multi-b-value diffusion-weighted imaging (DWI) were performed on a 1.5 T MRI scanner. A total of 2775 radiomics features were extracted from the T2WI, DWI, and the corresponding apparent diffusion coefficient (ADC) maps of patients. The least absolute shrinkage and selection operator (LASSO) and stepwise regression method were used for feature selection using the training cohort (n=110). Next, these features were further evaluated assessed in the two cohorts using a non-linear support vector machine (SVM) classifier. Lastly, a Radscore model was used to develop the radiomics-clinical nomogram.Results: Favorable discrimination performance was obtained for five of the optimal features using both cohorts, as demonstrated by the area under the curves (AUC) of 0.761 and 0.753. In addition, the radiomics-clinical nomogram, which integrated the Radscore with four independent clinical predictors, showed higher discriminative power, with AUCs of 0.814 and 0.767 for the X.T cohort and H.Y cohort, respectively. The nomogram showed excellent predictive performance and potential clinical utility for grading NSCLC.Conclusions: Multi-sequence MRI radiomics features can stratify NSCLC tumor grades noninvasively. The radiomics features can be integrated with the clinical features to improve its predictive performance.

Dosimetric consequences of tumor mobility in radiotherapy of stage I non-small cell lung cancer – an analysis of data generated using ‘slow’ CT scans

2001 ◽  
Vol 61 (1) ◽  
pp. 93-99 ◽  
Author(s):  
John R. van Sörnsen de Koste ◽  
Frank J. Lagerwaard ◽  
Regine H. Schuchhard-Schipper ◽  
Margriet R.J. Nijssen-Visser ◽  
Peter W.J. Voet ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Stage I ◽  
Ct Scans ◽  
Small Cell Lung

A systems pathology model for predicting overall survival in patients with refractory, advanced non-small cell lung cancer (NSCLC) treated with gefitinib

2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 18065-18065
Author(s):  
M. J. Donovan ◽  
A. Kotsianti ◽  
V. Bayer-Zubek ◽  
D. Verbel ◽  
M. Clayton ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Overall Survival ◽  
Tyrosine Kinase ◽  
Small Cell Lung Cancer ◽  
Cyclin D1 ◽  
Cell Lung Cancer ◽  
Caspase 3 ◽  
Small Cell ◽  
Support Vector ◽  
Small Cell Lung

18065 Background: The abundant expression of the epidermal growth factor receptor (EGFR) in a variety of solid tumors including non-small cell lung cancer (NSCLC), head and neck, breast, colon and brain has made it an attractive target for various selective molecular therapeutics, including the tyrosine kinase inhibitor gefitinib. The recent evidence of activating mutations in EGFR combined with clinical - demographic features has suggested that subgroups of patients with NSCLC are most likely to respond to selective therapies. We sought to determine whether the integration of clinical variables, tumor morphometry and quantitative protein profiles using support vector machines could identify a set of features which predicts overall survival in patients with NSCLC treated with gefitinib. Methods: We analyzed tumor samples from 109 patients with advanced refractory NSCLC treated with gefitinib. Formalin fixed, paraffin embedded tissue samples were evaluated with the following assays: Hematoxylin and Eosin image morphometry, EGFR DNA mutation analysis, EGFR protein immunohistochemistry and quantitative immunofluorescence with the following antibodies: CK18, Ki67, Caspase 3 activated, cd34, EGFR, phosphorylated-EGFR, phosphorylated-ERK, phosphorylated-AKT, PTEN, Cyclin D1, phosphorylated-m-TOR, PI3-K, VEGF, KDR (VEGFR2) and phosphorylated KDR. A predictive model was developed using support vector regression for censored data. Results: 4 of 87 patients had tyrosine kinase domain mutations in exons 19, 20 or 21. Utilizing 51 patients with complete data profiles (i.e. clinical, image morphometry and immunofluorescence), a model predicting overall survival was developed with a concordance index of 0.74. Poor performance status, poorly differentiated histology by morphometry and increased levels of activated caspase 3, phosphorylated KDR (VEGFR2) and cyclin D1 were associated with reduced survival. Conclusion: The integration of clinical, imaging and biomarker data identified a set of features which were associated with a more aggressive disease phenotype and resulted in overall poor survival. [Table: see text]

Stereotactic, high single-dose irradiation of stage I non-small cell lung cancer (NSCLC) using four-dimensional CT scans for treatment planning

Lung Cancer ◽  
2008 ◽  
Vol 60 (2) ◽  
pp. 193-199 ◽  
Author(s):  
Peter Fritz ◽  
Hans-Jörg Kraus ◽  
Thomas Blaschke ◽  
Werner Mühlnickel ◽  
Konstantin Strauch ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Single Dose ◽  
Small Cell Lung Cancer ◽  
Treatment Planning ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Ct Scans ◽  
Small Cell Lung ◽  
Dose Irradiation ◽  
High Single Dose

scholarly journals Identifying transcription patterns of histology and radiomics features in NSCLC with neural networks

2020 ◽  
Author(s):  
Nova F. Smedley ◽  
Denise R. Aberle ◽  
William Hsu
Keyword(s):  
Lung Cancer ◽  
Neural Networks ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Support Vector ◽  
Small Cell Lung ◽  
Gene Expressions ◽  
Gene Sets ◽  
Tumor Gene

AbstractPurposeTo investigate the use of deep neural networks to learn associations between gene expression and radiomics or histology in non-small cell lung cancer (NSCLC).Materials and MethodsDeep feedforward neural networks were used for radio-genomic mapping, where 21,766 gene expressions were inputs to individually predict histology and 101 CT radiomic features. Models were compared against logistic regression, support vector machines, random forests, and gradient boosted trees on 262 training and 89 testing patients. Neural networks were interpreted using gene masking to derive the learned associations between subsets of gene expressions to a radiomic feature or histology type.ResultsNeural networks outperformed other classifiers except in five radiomic features, where training differences were <0.026 AUC. In testing, neural networks classified histology with AUCs of 0.86 (adenocarcinoma), 0.91 (squamous), and 0.71 (other); and 14 radiomics features with >= 0.70 AUC. Gene masking of the models showed new and previously reported histology-gene or radiogenomic associations. For example, hypoxia genes could predict histology with >0.90 test AUC and published gene signatures for histology prediction were also predictive in our models (>0.80 test AUC). Gene sets related to the immune or cardiac systems and cell development processes were predictive (>0.70 test AUC) of several different radiomic features while AKT signaling, TNF, and Rho gene sets were each predictive of tumor textures.ConclusionWe demonstrate the ability of neural networks to map gene expressions to radiomic features and histology in NSCLC and interpret the models to identify predictive genes associated with each feature or type.Author SummaryNon-small-cell lung cancer (NSCLC) patients can have different presentations as seen in the CT scans, tumor gene expressions, or histology types. To improve the understanding of these complementary data types, this study attempts to map tumor gene expressions associated with a patient’s CT radiomic features or a histology type. We explore a deep neural network approach to learn gene-radiomic associations (i.e., the subsets of co-expressed genes that are predictive of a value of an individual radiomic feature) and gene-histology associations in two separate public cohorts. Our modeling approach is capable of learning relevant information by showing the model can predict histology and that the learned relationships are consistent with prior works. The study provides evidence for coherent patterns between gene expressions and radiomic features and suggests such integrated associations could improve patient stratification.

scholarly journals Weighted-Support Vector Machine Learning Classifier of Circulating Cytokine Biomarkers to Predict Radiation-Induced Lung Fibrosis in Non-Small-Cell Lung Cancer Patients

2021 ◽  
Vol 10 ◽  
Author(s):  
Hao Yu ◽  
Ka-On Lam ◽  
Huanmei Wu ◽  
Michael Green ◽  
Weili Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Lung Cancer ◽  
Support Vector Machine ◽  
Small Cell Lung Cancer ◽  
Lung Fibrosis ◽  
Small Cell ◽  
Support Vector ◽  
Svm Classifier ◽  
Small Cell Lung ◽  
Radiation Induced

BackgroundRadiation-induced lung fibrosis (RILF) is an important late toxicity in patients with non-small-cell lung cancer (NSCLC) after radiotherapy (RT). Clinically significant RILF can impact quality of life and/or cause non-cancer related death. This study aimed to determine whether pre-treatment plasma cytokine levels have a significant effect on the risk of RILF and investigate the abilities of machine learning algorithms for risk prediction.MethodsThis is a secondary analysis of prospective studies from two academic cancer centers. The primary endpoint was grade≥2 (RILF2), classified according to a system consistent with the consensus recommendation of an expert panel of the AAPM task for normal tissue toxicity. Eligible patients must have at least 6 months’ follow-up after radiotherapy commencement. Baseline levels of 30 cytokines, dosimetric, and clinical characteristics were analyzed. Support vector machine (SVM) algorithm was applied for model development. Data from one center was used for model training and development; and data of another center was applied as an independent external validation.ResultsThere were 57 and 37 eligible patients in training and validation datasets, with 14 and 16.2% RILF2, respectively. Of the 30 plasma cytokines evaluated, SVM identified baseline circulating CCL4 as the most significant cytokine associated with RILF2 risk in both datasets (P = 0.003 and 0.07, for training and test sets, respectively). An SVM classifier predictive of RILF2 was generated in Cohort 1 with CCL4, mean lung dose (MLD) and chemotherapy as key model features. This classifier was validated in Cohort 2 with accuracy of 0.757 and area under the curve (AUC) of 0.855.ConclusionsUsing machine learning, this study constructed and validated a weighted-SVM classifier incorporating circulating CCL4 levels with significant dosimetric and clinical parameters which predicts RILF2 risk with a reasonable accuracy. Further study with larger sample size is needed to validate the role of CCL4, and this SVM classifier in RILF2.

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