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Radiomic-Based Pathological Response Prediction from Primary Tumors and Lymph Nodes in NSCLC.

ABSTRACT: Noninvasive biomarkers that capture the total tumor burden could provide important complementary information for precision medicine to aid clinical decision making. We investigated the value of radiomic data extracted from pretreatment computed tomography images of the primary tumor and lymph nodes in predicting pathological response after neoadjuvant chemoradiation before surgery.A total of 85 patients with resectable locally advanced (stage II-III) NSCLC (median age 60.3 years, 65% female) treated from 2003 to 2013 were included in this institutional review board-approved study. Radiomics analysis was performed on 85 primary tumors and 178 lymph nodes to discriminate between pathological complete response (pCR) and gross residual disease (GRD). Twenty nonredundant and stable features (10 from each site) were evaluated by using the area under the curve (AUC) (all p values were corrected for multiple hypothesis testing). Classification performance of each feature set was evaluated by random forest and nested cross validation.Three radiomic features (describing primary tumor sphericity and lymph node homogeneity) were significantly predictive of pCR with similar performances (all AUC = 0.67, p < 0.05). Two features (quantifying lymph node homogeneity) were predictive of GRD (AUC range 0.72-0.75, p < 0.05) and performed significantly better than the primary features (AUC = 0.62). Multivariate analysis showed that for pCR, the radiomic features set alone had the best-performing classification (median AUC = 0.68). Furthermore, for GRD classification, the combination of radiomic and clinical data significantly outperformed all other feature sets (median AUC = 0.73).Lymph node phenotypic information was significantly predictive for pathological response and showed higher classification performance than radiomic features obtained from the primary tumor.

SUBMITTER: Coroller TP

PROVIDER: S-EPMC5318226 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature

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Radiomic-Based Pathological Response Prediction from Primary Tumors and Lymph Nodes in NSCLC.

Coroller Thibaud P TP Agrawal Vishesh V Huynh Elizabeth E Narayan Vivek V Lee Stephanie W SW Mak Raymond H RH Aerts Hugo J W L HJWL

Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer 20161127 3

<h4>Introduction</h4>Noninvasive biomarkers that capture the total tumor burden could provide important complementary information for precision medicine to aid clinical decision making. We investigated the value of radiomic data extracted from pretreatment computed tomography images of the primary tumor and lymph nodes in predicting pathological response after neoadjuvant chemoradiation before surgery.<h4>Methods</h4>A total of 85 patients with resectable locally advanced (stage II-III) NSCLC (m ...[more]

PMID: 27903462

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Project description:BACKGROUND:We aimed to develop radiomic models based on different phases of computed tomography (CT) imaging and to investigate the efficacy of models for diagnosing mediastinal metastatic lymph nodes (LNs) in non-small cell lung cancer (NSCLC). METHODS:Eighty-six NSCLC patients were enrolled in this study, and we selected 231 mediastinal LNs confirmed by pathology results as the subjects which were divided into training (n?=?163) and validation cohorts (n?=?68). The regions of interest (ROIs) were delineated on CT scans in the plain phase, arterial phase and venous phase, respectively. Radiomic features were extracted from the CT images in each phase. A least absolute shrinkage and selection operator (LASSO) algorithm was used to select features, and multivariate logistic regression analysis was used to build models. We constructed six models (orders 1-6) based on the radiomic features of the single- and dual-phase CT images. The performance of the radiomic model was evaluated by the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value (NPV). RESULTS:A total of 846 features were extracted from each ROI, and 10, 9, 5, 2, 2, and 9 features were chosen to develop models 1-6, respectively. All of the models showed excellent discrimination, with AUCs greater than 0.8. The plain CT radiomic model, model 1, yielded the highest AUC, specificity, accuracy and PPV, which were 0.926 and 0.925; 0.860 and 0.769; 0.871 and 0.882; and 0.906 and 0.870 in the training and validation sets, respectively. When the plain and venous phase CT radiomic features were combined with the arterial phase CT images, the sensitivity increased from 0.879 and 0.919 to 0.949 and 0979 and the NPV increased from 0.821 and 0.789 to 0.878 and 0.900 in the training group, respectively. CONCLUSIONS:All of the CT radiomic models based on different phases all showed high accuracy and precision for the diagnosis of LN metastasis (LNM) in NSCLC patients. When combined with arterial phase CT, the sensitivity and NPV of the model was be further improved.

Project description:Background The current study attempted to describe the specific patterns of pathological tumor response and locoregional node metastases from surgically resected esophageal squamous cell carcinoma after neoadjuvant immunochemotherapy (NAIC), as well as to explore the association between clinicopathological characteristics and such oncological patterns. Methods Fifty-one patients with cT3 or deeper esophageal squamous cell cancer underwent subtotal esophagectomy after NAIC. The NAIC regimen included intravenous administration of platinum-based and docetaxel- and taxane-based chemotherapeutics along with a 200 mg fixed dose of one programmed death 1 (PD-1) inhibitor, given every 3 weeks. We divided patients into tumor/nodal good-responders and poor-responders based on the pathological observation of the tumor or nodal responses. We also examined the association between clinicopathological factors and tumor/nodal responses. Further, significant baseline predictors for tumor and nodal good-responders were identified using multivariate binary logistic regression. Results Of the 51 patients, 68.6% achieved marked primary tumor response. Notably, 21.6% of patients achieved complete pathological response. Significant differences in treatment cycles between tumor good-responders and tumor poor-responders (P = 0.019) were observed. For locoregional nodal responses, only 33.3% of patients achieved down-staged nodal disease. Of the investigated variables, neoadjuvant cycles (odds ratio (OR): 5.271, 95% confidence interval (CI): 1.278 - 21.740, P = 0.022) and pretreatment platelets (OR: 0.979, 95% CI: 0.962 - 0.996, P = 0.017) were identified as independent predictors for good tumor and nodal responses. Conclusions We conclusively noted that most patients receiving NAIC were tumor good-responders, whereas only one-third of patients were nodal good-responders. Furthermore, we identified that treatment cycle number and baseline platelet counts were independent predictors of combined tumor and nodal responses.

Dataset Information

Radiomic-Based Pathological Response Prediction from Primary Tumors and Lymph Nodes in NSCLC.

Publications

Radiomic-Based Pathological Response Prediction from Primary Tumors and Lymph Nodes in NSCLC.

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