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Radiomic phenotype features predict pathological response in non-small cell lung cancer.

ABSTRACT: Radiomics can quantify tumor phenotype characteristics non-invasively by applying advanced imaging feature algorithms. In this study we assessed if pre-treatment radiomics data are able to predict pathological response after neoadjuvant chemoradiation in patients with locally advanced non-small cell lung cancer (NSCLC).127 NSCLC patients were included in this study. Fifteen radiomic features selected based on stability and variance were evaluated for its power to predict pathological response. Predictive power was evaluated using area under the curve (AUC). Conventional imaging features (tumor volume and diameter) were used for comparison.Seven features were predictive for pathologic gross residual disease (AUC>0.6, p-value<0.05), and one for pathologic complete response (AUC=0.63, p-value=0.01). No conventional imaging features were predictive (range AUC=0.51-0.59, p-value>0.05). Tumors that did not respond well to neoadjuvant chemoradiation were more likely to present a rounder shape (spherical disproportionality, AUC=0.63, p-value=0.009) and heterogeneous texture (LoG 5mm 3D - GLCM entropy, AUC=0.61, p-value=0.03).We identified predictive radiomic features for pathological response, although no conventional features were significantly predictive. This study demonstrates that radiomics can provide valuable clinical information, and performed better than conventional imaging features.

SUBMITTER: Coroller TP

PROVIDER: S-EPMC4930885 | biostudies-literature | 2016 Jun

REPOSITORIES: biostudies-literature

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Radiomic phenotype features predict pathological response in non-small cell lung cancer.

Coroller Thibaud P TP Agrawal Vishesh V Narayan Vivek V Hou Ying Y Grossmann Patrick P Lee Stephanie W SW Mak Raymond H RH Aerts Hugo J W L HJ

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology 20160413 3

<h4>Background and purpose</h4>Radiomics can quantify tumor phenotype characteristics non-invasively by applying advanced imaging feature algorithms. In this study we assessed if pre-treatment radiomics data are able to predict pathological response after neoadjuvant chemoradiation in patients with locally advanced non-small cell lung cancer (NSCLC).<h4>Materials and methods</h4>127 NSCLC patients were included in this study. Fifteen radiomic features selected based on stability and variance wer ...[more]

PMID: 27085484

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Project description:IntroductionNon-small cell lung cancer (NSCLC) is the leading cause of cancer incidence and mortality worldwide. Neoadjuvant chemo-immunotherapy has led to clinical benefits in resectable NSCLC in comparison to chemo-therapy alone. Major pathological response (MPR) and pathological complete response (pCR) have been used as surrogates of neoadjuvant therapy response and clinical outcomes. However, the factors affecting the pathological response are still controversial. Therefore, in this study we retrospectively examined MPR and pCR in two different cohorts of NSCLC patients, 14 treated by chemotherapy and 12 by chemo-immunotherapy in the neoadjuvant setting.MethodsIn resected tumor specimens, different histological characteristics were evaluated: necrosis, fibrosis, inflammation, presence of organizing pneumonia, granuloma, cholesterol cleft, and reactive epithelial alterations. In addition, we evaluated how MPR impacts on event-free survival (EFS) and overall survival (OS). In a small group of patients treated by chemo-immunotherapy, a gene expression analysis of the Hippo pathway was performed both in preoperative biopsies and matched post-surgical specimens.ResultsWe observed a better pathological response in the chemo-immunotherapy treated cohort: 6/12 patients (50.0%) achieved a MPR ≤10% and 1/12 (8.3%) achieved pCR both on primary tumor and on lymph nodes. On the contrary, no patient treated with chemotherapy alone achieved pCR or MPR ≤10%. A higher amount of stroma in the neoplastic bed was observed in patients treated with immuno-chemotherapy. Moreover, patients achieving better MPR (including pCR) had significantly improved overall survival (OS) and event-free survival (EFS). After neoadjuvant chemo-immunotherapy, residual tumors showed a remarkable upregulation of genes consistent with the activation of YAP/TAZ. Also, alternative checkpoint, such as CTLA-4, were enhanced.DiscussionOur findings showed that neoadjuvant chemo-immunotherapy treatment improves MPR and pCR thus resulting in better EFS and OS. Moreover, a combined treatment could induce different morphological and molecular changes in comparison to chemotherapy alone, thus giving new insights in the assessment of pathological response.

Project description:Background: The aim of the study was to evaluate the role of different immunohistochemical and radiomics features in patients with small cell lung cancer (SCLC). Methods: Consecutive patients with histologically proven SCLC with limited (n = 47, 48%) or extensive disease (n = 51, 52%) treated with radiotherapy and chemotherapy at our department were included in the analysis. The expression of different immunohistochemical markers from the initial tissue biopsy, such as CD56, CD44, chromogranin A, synaptophysin, TTF-1, GLUT-1, Hif-1 a, PD-1, and PD-L1, and MIB-1/KI-67 as well as LDH und NSE from the initial blood sample were evaluated. H-scores were additionally generated for CD44, Hif-1a, and GLUT-1. A total of 72 computer tomography (CT) radiomics texture features from a homogenous subgroup (n = 31) of patients were correlated with the immunohistochemistry, the survival (OS), and the progression-free survival (PFS). Results: The median OS, calculated from diagnosis, was 21 months for patients with limited disease and 13 months for patients with extensive disease. The expression of synaptophysin correlated with a better OS (HR 0.546 95% CI 0.308-0.966, p = 0.03). The expression of TTF-1 (HR 0.286, 95% CI: 0.117-0.698, p = 0.006) and a lower GLUT-1 H-score (median = 50, HR: 0.511, 95% CI: 0.260-1.003, p = 0.05) correlated with a better PFS. Patients without chromogranin A expression had a higher risk for developing cerebral metastases (p = 0.02) and patients with PD 1 expression were at risk for developing metastases (p = 0.02). Our radiomics analysis did not reveal a single texture feature that correlated highly with OS or PFS. Correlation coefficients ranged between -0.48 and 0.39 for OS and between -0.46 and 0.38 for PFS. Conclusions: The role of synaptophysin should be further evaluated as synaptophysin-negative patients might profit from treatment intensification. We report an, at most, moderate correlation of radiomics features with overall and progression free survival and no correlation with the expression of different immunohistochemical markers.

Project description:Lung cancer is the second most fatal disease worldwide. In the last few years, radiomics is being explored to develop prediction models for various clinical endpoints in lung cancer. However, the robustness of radiomic features is under question and has been identified as one of the roadblocks in the implementation of a radiomic-based prediction model in the clinic. Many past studies have suggested identifying the robust radiomic feature to develop a prediction model. In our earlier study, we identified robust radiomic features for prediction model development. The objective of this study was to develop and validate the robust radiomic signatures for predicting 2-year overall survival in non-small cell lung cancer (NSCLC). This retrospective study included a cohort of 300 stage I-IV NSCLC patients. Institutional 200 patients' data were included for training and internal validation and 100 patients' data from The Cancer Image Archive (TCIA) open-source image repository for external validation. Radiomic features were extracted from the CT images of both cohorts. The feature selection was performed using hierarchical clustering, a Chi-squared test, and recursive feature elimination (RFE). In total, six prediction models were developed using random forest (RF-Model-O, RF-Model-B), gradient boosting (GB-Model-O, GB-Model-B), and support vector(SV-Model-O, SV-Model-B) classifiers to predict 2-year overall survival (OS) on original data as well as balanced data. Model validation was performed using 10-fold cross-validation, internal validation, and external validation. Using a multistep feature selection method, the overall top 10 features were chosen. On internal validation, the two random forest models (RF-Model-O, RF-Model-B) displayed the highest accuracy; their scores on the original and balanced datasets were 0.81 and 0.77 respectively. During external validation, both the random forest models' accuracy was 0.68. In our study, robust radiomic features showed promising predictive performance to predict 2-year overall survival in NSCLC.

Project description:Lung cancer can be classified into two main categories: small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC), which are different in treatment strategy and survival probability. The lung CT images of SCLC and NSCLC are similar such that their subtle differences are hardly visually discernible by the human eye through conventional imaging evaluation. We hypothesize that SCLC/NSCLC differentiation could be achieved via computerized image feature analysis and classification in feature space, as termed a radiomic model. The purpose of this study was to use CT radiomics to differentiate SCLC from NSCLC adenocarcinoma. Patients with primary lung cancer, either SCLC or NSCLC adenocarcinoma, were retrospectively identified. The post-diagnosis pre-treatment lung CT images were used to segment the lung cancers. Radiomic features were extracted from histogram-based statistics, textural analysis of tumor images and their wavelet transforms. A minimal-redundancy-maximal-relevance method was used for feature selection. The predictive model was constructed with a multilayer artificial neural network. The performance of the SCLC/NSCLC adenocarcinoma classifier was evaluated by the area under the receiver operating characteristic curve (AUC). Our study cohort consisted of 69 primary lung cancer patients with SCLC (n = 35; age mean ± SD = 66.91± 9.75 years), and NSCLC adenocarcinoma (n = 34; age mean ± SD = 58.55 ± 11.94 years). The SCLC group had more male patients and smokers than the NSCLC group (P < 0.05). Our SCLC/NSCLC classifier achieved an overall performance of AUC of 0.93 (95% confidence interval = [0.85, 0.97]), sensitivity = 0.85, and specificity = 0.85). Adding clinical data such as smoking history could improve the performance slightly. The top ranking radiomic features were mostly textural features. Our results showed that CT radiomics could quantitatively represent tumor heterogeneity and therefore could be used to differentiate primary lung cancer subtypes with satisfying results. CT image processing with the wavelet transformation technique enhanced the radiomic features for SCLC/NSCLC classification. Our pilot study should motivate further investigation of radiomics as a non-invasive approach for early diagnosis and treatment of lung cancer.

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Radiomic phenotype features predict pathological response in non-small cell lung cancer.

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Radiomic phenotype features predict pathological response in non-small cell lung cancer.

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