scholarly journals Application of contrast-enhanced CT radiomics in prediction of early recurrence of locally advanced oesophageal squamous cell carcinoma after trimodal therapy

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sun Tang ◽  
Jing Ou ◽  
Jun Liu ◽  
Yu-ping Wu ◽  
Chang-qiang Wu ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Clinical Features ◽  
Medical Records ◽  
Locally Advanced ◽  
Early Recurrence ◽  
Oesophageal Squamous Cell Carcinoma ◽  
Training Cohort ◽  
Clinical Model ◽  
Contrast Enhanced ◽  
Trimodal Therapy

Abstract Background Early recurrence of oesophageal squamous cell carcinoma (SCC) is defined as recurrence after surgery within 1 year, and appears as local recurrence, distant recurrence, and lymph node positive and disseminated recurrence. Contrast-enhanced computed tomography (CECT) is recommended for diagnosis of primary tumor and initial staging of oesophageal SCC, but it cannot be used to predict early recurrence. It is reported that radiomics can help predict preoperative stages of oesophageal SCC, lymph node metastasis before operation, and 3-year overall survival of oesophageal SCC patients following chemoradiotherapy by extracting high-throughput quantitative features from CT images. This study aimed to develop models based on CT radiomics and clinical features of oesophageal SCC to predict early recurrence of locally advanced cancer. Methods We collected electronic medical records and image data of 197 patients with confirmed locally advanced oesophageal SCC. These patients were randomly allocated to 137 patients in the training cohort and 60 in the test cohort. 352 radiomics features were extracted by delineating region-of-interest (ROI) around the lesion on CECT images and clinical signature was generated by medical records. The radiomics model, clinical model, the combined model of radiomics and clinical features were developed by radiomics features and/or clinical characteristics. Predicting performance of the three models was assessed with area under receiver operating characteristic curve (AUC), accuracy and F-1 score. Results Eleven radiomics features and/or six clinical signatures were selected to build prediction models related to recurrence of locally advanced oesophageal SCC after trimodal therapy. The AUC of integration of radiomics and clinical models was better than that of radiomics or clinical model for the training cohort (0.821 versus 0.754 or 0.679, respectively) and for the validation cohort (0.809 versus 0.646 or 0.658, respectively). Integrated model of radiomics and clinical features showed good performance in predicting early recurrence of locally advanced oesophageal SCC for both the training and validation cohorts (accuracy = 0.730 and 0.733, and F-1score = 0.730 and 0.778, respectively). Conclusions The integrated model of CECT radiomics and clinical features may be a potential imaging biomarker to predict early recurrence of locally advanced oesophageal SCC after trimodal therapy.

scholarly journals Contrast-Enhanced CT-Based Radiomics Analysis in Predicting Lymphovascular Invasion in Esophageal Squamous Cell Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Yang Li ◽  
Meng Yu ◽  
Guangda Wang ◽  
Li Yang ◽  
Chongfei Ma ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Logistic Regression ◽  
Esophageal Squamous Cell Carcinoma ◽  
Lymphovascular Invasion ◽  
Combined Model ◽  
Training Cohort ◽  
Clinical Model ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct

ObjectivesTo develop a radiomics model based on contrast-enhanced CT (CECT) to predict the lymphovascular invasion (LVI) in esophageal squamous cell carcinoma (ESCC) and provide decision-making support for clinicians.Patients and MethodsThis retrospective study enrolled 334 patients with surgically resected and pathologically confirmed ESCC, including 96 patients with LVI and 238 patients without LVI. All enrolled patients were randomly divided into a training cohort and a testing cohort at a ratio of 7:3, with the training cohort containing 234 patients (68 patients with LVI and 166 without LVI) and the testing cohort containing 100 patients (28 patients with LVI and 72 without LVI). All patients underwent preoperative CECT scans within 2 weeks before operation. Quantitative radiomics features were extracted from CECT images, and the least absolute shrinkage and selection operator (LASSO) method was applied to select radiomics features. Logistic regression (Logistic), support vector machine (SVM), and decision tree (Tree) methods were separately used to establish radiomics models to predict the LVI status in ESCC, and the best model was selected to calculate Radscore, which combined with two clinical CT predictors to build a combined model. The clinical model was also developed by using logistic regression. The receiver characteristic curve (ROC) and decision curve (DCA) analysis were used to evaluate the model performance in predicting the LVI status in ESCC.ResultsIn the radiomics model, Sphericity and gray-level non-uniformity (GLNU) were the most significant radiomics features for predicting LVI. In the clinical model, the maximum tumor thickness based on CECT (cThick) in patients with LVI was significantly greater than that in patients without LVI (P<0.001). Patients with LVI had higher clinical N stage based on CECT (cN stage) than patients without LVI (P<0.001). The ROC analysis showed that both the radiomics model (AUC values were 0.847 and 0.826 in the training and testing cohort, respectively) and the combined model (0.876 and 0.867, respectively) performed better than the clinical model (0.775 and 0.798, respectively), with the combined model exhibiting the best performance.ConclusionsThe combined model incorporating radiomics features and clinical CT predictors may potentially predict the LVI status in ESCC and provide support for clinical treatment decisions.

scholarly journals Early Recurrence and Cancer Death After Trimodal Therapy for Esophageal Squamous Cell Carcinoma

2019 ◽  
Vol 39 (3) ◽  
pp. 1433-1440 ◽  
Author(s):  
YOICHI HAMAI ◽  
MANABU EMI ◽  
YUTA IBUKI ◽  
YUJI MURAKAMI ◽  
IKUNO NISHIBUCHI ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Esophageal Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Early Recurrence ◽  
Cancer Death ◽  
Trimodal Therapy

Predictive score for recurrence after complete resection in locally advanced oral cavity squamous cell carcinoma.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e17536-e17536
Author(s):  
Hsueh-Ju Lu ◽  
Szu-Wen Tseng ◽  
Chih-Yu Peng ◽  
Hsien-Chun Tseng ◽  
Chung-Han Hsin ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Oral Cavity ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Cox Regression ◽  
Locally Advanced ◽  
Predictive Score ◽  
Depth Of Invasion ◽  
Training Cohort ◽  
Early Progression

e17536 Background: Early progression, usually defined disease-free interval (DFI) less than six months after completing adjuvant platinum-based chemoradiotherapy (CRT), has very poor outcome for oral cavity squamous cell carcinoma (OCSCC). But there are no biomarkers to predict such early progression. Methods: Locally advanced OCSCC patients, after complete surgical resection and followed-up platinum-based adjuvant CRT, were retrospectively enrolled from Chung Shan Medical University Hospital (CSMUH, training cohort) and Taipei Veterans General Hospital (TPE-VGH, validating cohort) in Taiwan. Clinicopathologic variables of patients with DFI < or ≥ 6 months were compared by using the χ2 test. The Cox proportional hazards model was applied to identify independent factors for DFI. Survival was estimated using the Kaplan-Meier method and log-rank test. Results: A total of 350 high-risk OCSCC patients were enrolled, including 146 patients in training cohort and 204 in validating cohort. In multivariate Cox regression, pN > 0, extracapular spread, and depth of invasion ( ≥ 1cm) were independent factors for DFI in training cohort. If each factor scored one point, the scoring system could effectively predict early progression that sensitivity/specificity/area under curve (AUC) of training and validating cohort were 57.7%/91.2%/0.771 and 58.1%/83.9%/0.730, respectively (the cutoff level ≤ 2 or > 2). DFI between lower- (score 0–2) and high- (score 3) risk groups were also significantly different in both training (median DFI, 59.6 vs. 4.5 months, P < 0.001) and validating cohorts (NA vs. 9.3 months, P < 0.001). Conclusions: The established score system was effective to predict early progression after adjuvant CRT for locally advanced OCSCC.

scholarly journals MRI Radiomic Features: A Potential Biomarker for Progression-Free Survival Prediction of Patients With Locally Advanced Cervical Cancer Undergoing Surgery

2021 ◽  
Vol 11 ◽  
Author(s):  
Mengting Cai ◽  
Fei Yao ◽  
Jie Ding ◽  
Ruru Zheng ◽  
Xiaowan Huang ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Validation Cohort ◽  
Locally Advanced ◽  
Progression Free Survival ◽  
Risk Groups ◽  
Advanced Cervical Cancer ◽  
Free Survival ◽  
Training Cohort ◽  
Clinical Model ◽  
Locally Advanced Cervical Cancer

ObjectivesTo investigate the prognostic role of radiomic features based on pretreatment MRI in predicting progression-free survival (PFS) of locally advanced cervical cancer (LACC).MethodsAll 181 women with histologically confirmed LACC were randomly divided into the training cohort (n = 126) and the validation cohort (n = 55). For each patient, we extracted radiomic features from whole tumors on sagittal T2WI and axial DWI. The least absolute shrinkage and selection operator (LASSO) algorithm combined with the Cox survival analysis was applied to select features and construct a radiomic score (Rad-score) model. The cutoff value of the Rad-score was used to divide the patients into high- and low-risk groups by the X-tile. Kaplan–Meier analysis and log-rank test were used to assess the prognostic value of the Rad-score. In addition, we totally developed three models, the clinical model, the Rad-score, and the combined nomogram.ResultsThe Rad-score demonstrated good performance in stratifying patients into high- and low-risk groups of progression in the training (HR = 3.279, 95% CI: 2.865–3.693, p &lt; 0.0001) and validation cohorts (HR = 2.247, 95% CI: 1.735–2.759, p &lt; 0.0001). Otherwise, the combined nomogram, integrating the Rad-score and patient’s age, hemoglobin, white blood cell, and lymph vascular space invasion, demonstrated prominent discrimination, yielding an AUC of 0.879 (95% CI, 0.811–0.947) in the training cohort and 0.820 (95% CI, 0.668–0.971) in the validation cohort. The Delong test verified that the combined nomogram showed better performance in estimating PFS than the clinical model and Rad-score in the training cohort (p = 0.038, p = 0.043).ConclusionThe radiomics nomogram performed well in individualized PFS estimation for the patients with LACC, which might guide individual treatment decisions.

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