scholarly journals A nomogram based on pretreatment CT radiomics features for predicting complete response to chemoradiotherapy in patients with esophageal squamous cell cancer

2020 ◽  
Author(s):  
Hesan Luo ◽  
Shao-Fu Huang ◽  
Hong-Yao Xu ◽  
Xu-Yuan Li ◽  
Sheng-Xi Wu ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Squamous Cell ◽  
Cell Cancer ◽  
Complete Response ◽  
Clinical Staging ◽  
Training Set ◽  
Simple Method ◽  
Validation Set ◽  
The Impact ◽  
Radiomics Signature

Abstract Purpose: To develop and validate a nomogram model to predict complete response (CR) after concurrent chemoradiotherapy (CCRT) in esophageal squamous cell carcinoma (ESCC) patients using pretreatment CT radiomic features. Methods: Data of patients diagnosed as ESCC and treated with CCRT in Shantou Central Hospital during the period from January 2013 to December 2015 were retrospectively collected. Eligible patients were included in this study and randomize divided into a training set and a validation set after successive screening. The least absolute shrinkage and selection operator (LASSO) with logistic regression to select radiomics features calculating Rad-score in the training set. The logistic regression analysis was performed to identify the predictive clinical factors for developing a nomogram model. The area under the receiver operating characteristic curves (AUC) was used to assess the performance of the predictive nomogram model and decision curve was used to analyze the impact of the nomogram model on clinical treatment decisions. Results: A total of 226 patients were included and randomly divided into two groups, 160 patients in training set and 66 patients in validation set. After LASSO analysis, seven radiomics features were screened out to develop a radiomics signature Rad-score. The AUC of Rad-score was 0.812 (95%CI: 0.742-0.869, p<0.001) in the training set and 0.744 (95%CI: 0.632-0.851, p=0.003) in the validation set. Multivariate analysis showed that Rad-score and clinical staging were independent predictors of CR status, with P values of 0.035 and 0.023, respectively. A nomogram model incorporating Rad-socre and clinical staging was developed and validated, with an AUC of 0.844 (95％CI: 0.779-0.897) in the training set and 0.807 (95％CI: 0.691-0.894) in the validation set．Delong test showed that the nomogram model was significantly superior to the clinical staging, with P<0.001 in the training set and P=0.026 in the validation set. The decision curve showed that the nomogram model was superior to the clinical staging when the risk threshold was greater than 25%. Conclusion: We developed and validated a nomogram model for predicting CR status of ESCC patients after CCRT. The nomogram model was combined radiomics signature Rad-score and clinical staging. This model provided us with an economical and simple method for evaluating the response of chemoradiotherapy for patients with ESCC.

scholarly journals A nomogram based on pretreatment CT radiomics features for predicting complete response to chemoradiotherapy in patients with esophageal squamous cell cancer

2020 ◽  
Author(s):  
Hesan Luo ◽  
Shao-Fu Huang ◽  
Hong-Yao Xu ◽  
Xu-Yuan Li ◽  
Sheng-Xi Wu ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Squamous Cell ◽  
Cell Cancer ◽  
Complete Response ◽  
Clinical Staging ◽  
Training Set ◽  
Simple Method ◽  
Validation Set ◽  
The Impact ◽  
Radiomics Signature

Abstract Purpose To develop and validate a nomogram model to predict complete response (CR) after concurrent chemoradiotherapy (CCRT) in esophageal squamous cell carcinoma (ESCC) patients using pretreatment CT radiomic features. Methods Data of patients diagnosed as ESCC and treated with CCRT in Shantou Central Hospital during the period from January 2013 to December 2015 were retrospectively collected. Eligible patients were included in this study and randomize divided into a training set and a validation set after successive screening. The least absolute shrinkage and selection operator (LASSO) with logistic regression to select radiomics features calculating Rad-score in the training set. The logistic regression analysis was performed to identify the predictive clinical factors for developing a nomogram model. The area under the receiver operating characteristic curves (AUC) was used to assess the performance of the predictive nomogram model and decision curve was used to analyze the impact of the nomogram model on clinical treatment decisions. Results A total of 226 patients were included and randomly divided into two groups, 160 patients in training set and 66 patients in validation set. After LASSO analysis, seven radiomics features were screened out to develop a radiomics signature Rad-score. The AUCs of Rad-score was 0.812 (95%CI: 0.742–0.869) in the training set and 0.744 (95%CI: 0.632–0.851) in the validation set. Multivariate analysis showed that Rad-score and clinical staging were independent predictors of CR status, with P values of 0.035 and 0.023, respectively. A nomogram model incorporating Rad-socre and clinical staging was developed and validated, with an AUC of 0.844 (95%CI: 0.779–0.897) in the training set and 0.807 (95༅CI: 0.691–0.894) in the validation set༎Delong test showed that the nomogram model was significantly superior to the clinical staging, with P < 0.001 in the training set and P = 0.026 in the validation set. The decision curve showed that the nomogram model was superior to the clinical staging when the risk threshold was greater than 25%. Conclusion We developed and validated a nomogram model for predicting CR status of ESCC patients after CCRT. The nomogram model was combined radiomics signature Rad-score and clinical staging. This model provided us with an economical and simple method for evaluating the response of chemoradiotherapy for patients with ESCC.

scholarly journals A nomogram based on pretreatment CT radiomics features for predicting complete response to chemoradiotherapy in patients with esophageal squamous cell cancer

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
He-San Luo ◽  
Shao-Fu Huang ◽  
Hong-Yao Xu ◽  
Xu-Yuan Li ◽  
Sheng-Xi Wu ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Squamous Cell ◽  
Cell Cancer ◽  
Complete Response ◽  
Clinical Staging ◽  
Training Set ◽  
Simple Method ◽  
Validation Set ◽  
The Impact ◽  
Radiomics Signature

Abstract Purpose To develop and validate a nomogram model to predict complete response (CR) after concurrent chemoradiotherapy (CCRT) in esophageal squamous cell carcinoma (ESCC) patients using pretreatment CT radiomic features. Methods Data of patients diagnosed as ESCC and treated with CCRT in Shantou Central Hospital during the period from January 2013 to December 2015 were retrospectively collected. Eligible patients were included in this study and randomize divided into a training set and a validation set after successive screening. The least absolute shrinkage and selection operator (LASSO) with logistic regression to select radiomics features calculating Rad-score in the training set. The logistic regression analysis was performed to identify the predictive clinical factors for developing a nomogram model. The area under the receiver operating characteristic curves (AUC) was used to assess the performance of the predictive nomogram model and decision curve was used to analyze the impact of the nomogram model on clinical treatment decisions. Results A total of 226 patients were included and randomly divided into two groups, 160 patients in training set and 66 patients in validation set. After LASSO analysis, seven radiomics features were screened out to develop a radiomics signature Rad-score. The AUC of Rad-score was 0.812 (95% CI 0.742–0.869, p < 0.001) in the training set and 0.744 (95% CI 0.632–0.851, p = 0.003) in the validation set. Multivariate analysis showed that Rad-score and clinical staging were independent predictors of CR status, with p values of 0.035 and 0.023, respectively. A nomogram model incorporating Rad-socre and clinical staging was developed and validated, with an AUC of 0.844 (95% CI 0.779–0.897) in the training set and 0.807 (95% CI 0.691–0.894) in the validation set. Delong test showed that the nomogram model was significantly superior to the clinical staging, with p < 0.001 in the training set and p = 0.026 in the validation set. The decision curve showed that the nomogram model was superior to the clinical staging when the risk threshold was greater than 25%. Conclusion We developed and validated a nomogram model for predicting CR status of ESCC patients after CCRT. The nomogram model was combined radiomics signature Rad-score and clinical staging. This model provided us with an economical and simple method for evaluating the response of chemoradiotherapy for patients with ESCC.

A prospective study investigating the impact of definitive chemoradiation in locoregionally advanced squamous cell carcinoma of the skin.

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. 8538-8538 ◽  
Author(s):  
Michelle K. Nottage ◽  
Alessandra Bastian Francesconi ◽  
Kathleen Emilie Mary Houston ◽  
Charles Lin ◽  
Lizbeth M. Kenny ◽  
...  
Keyword(s):  
Squamous Cell ◽  
Response Rate ◽  
Complete Response Rate ◽  
Squamous Cell Cancer ◽  
Performance Status ◽  
Cell Cancer ◽  
Prospective Trial ◽  
Complete Response ◽  
Platinum Based Chemotherapy ◽  
The Impact

8538 Background: Our state, Queensland, Australia, has the highest rate of cutaneous squamous cell cancer (SCC) in the world. Spread to regional lymph nodes or more distant sites occurs in 5-10%. A proportion of patients can not undergo surgical resection but complete response rates with radiotherapy alone are low. This led to the hypothesis that combined chemoradiation (CRT) may be of benefit. We decided to document the outcomes of concurrent chemoradiation by means of a prospective trial. Methods: This was a single arm, phase II study with planned sample size 30 patients. The primary endpoint was complete response rate (CRR), estimate 60%. Patients with locally/regionally advanced (non-metastatic) cutaneous SCC deemed unresectable or unsuitable for surgery by consensus of the multidisciplinary Head and Neck Cancer Clinic, with measurable disease, aged over 18, performance status 0-2, received definitive radiotherapy (XRT) (70Gy in 35#) and concurrent weekly platinum based chemotherapy (CT) (cisplatin 40mg/m2 or carboplatin AUC 2). Results: 14 patients were enrolled (Feb 2008-June 2011), median age 66 (48-84), 64% ECOG PS 0, 64% stage IV, 57% nodal disease only. Cisplatin/carboplatin was administered in 64%/36% respectively. 42% received all planned CT while 58% had 1 or 2 weeks omitted. 2 patients had dose reductions. XRT was completed as planned in 93%. The CRR was 57% (8/14) at analysis in December 2011 (median follow-up 13.5m). 2 further patients with partial response (PR) achieved CR after undergoing salvage surgery. Six (43%) patients had a PR; 4(29%) did not receive surgery and later progressed. Median overall survival was not reached, with 3 year survival 54%. The most frequent toxicities were dermatitis, mucositis, thrombocytopenia, nausea, anaemia, dysphagia. 28% had grade 3/4 toxicity, mainly cytopenias, infection, dehydration and nausea. Conclusions: This is the only prospective series of CRT for cutaneous squamous cell cancer. A high complete response rate was documented in patients with loco-regionally advanced disease and multiple co-morbidities, with acceptable toxicity, making this a reasonable alternative for patients unable to undergo surgery.

Improved lymph node metastasis prediction from preoperative esophageal squamous cell cancer CT by graph attention convolutional neural network (GACNN).

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e16093-e16093
Author(s):  
Mingjun Ding ◽  
Hui Cui ◽  
Butuo Li ◽  
Bing Zou ◽  
Yiyue Xu ◽  
...  
Keyword(s):  
Lymph Node ◽  
Prediction Model ◽  
Squamous Cell ◽  
Data Augmentation ◽  
Cell Cancer ◽  
Morphological Features ◽  
Training Set ◽  
Contrast Enhanced Ct ◽  
Area Under Roc Curve ◽  
Validation Set

e16093 Background: Lymph node (LN) metastasis is the most important factor for decision making in esophageal squamous cell carcinoma (ESCC). A more accurate prediction model for LN metastatic status in ESCC patients is needed. Methods: In this retrospective study, 397 ESCC patients who took Contrast-Enhanced CT (CECT) within 15 days before surgery between October 2013 and November 2018 were collected. There are 924 (798 negative and 126 positive) LNs with pathologically confirmed status after surgery. All LNs were randomly divided into a training set (n = 663) and validation set (n = 185). Data augmentation including shifting and rotation was performed in the training set, resulting in 1326 negative and 1140 positive LN samples. The GACNN model was trained over CT volumetric patches centred at manually segmented LN samples. GACNN was composed of a 3D UNet encoder to extract deep features, and a graph attention layer to integrate morphological features extracted from segmented LN. The model was validated using the validation set (135 negative and 50 positive) and measured by area under ROC curve (auc), sensitivity (sen), and specificity (spe). Results: GACNN achieved better auc, sen, and spe of 0.802, 0.765, and 0.826, when compared to 3 other models including CT radiomics model (auc 0.733, sen 0.689, spe 0.765), 3D UNet encoder (auc 0.778, sen 0.722, spe 0.767), and our model without morphological features (auc 0.796, sen 0.754, spe 0.803). The improvement was statistically significant (p < 0.001). Conclusions: Our prediction model improved the prediction of LN metastasis, which has the potential to assist LN metastasis risk evaluation and personalized treatment planning in ESCC patients for surgery or radiotherapy.

scholarly journals A CT-Based Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Periampullary Carcinomas

2021 ◽  
Vol 11 ◽  
Author(s):  
Lei Bi ◽  
Yubo Liu ◽  
Jingxu Xu ◽  
Ximing Wang ◽  
Tong Zhang ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Lymph Node ◽  
Clinical Utility ◽  
Clinical Decision Making ◽  
Multivariate Logistic Regression Model ◽  
Training Set ◽  
Preoperative Prediction ◽  
Validation Set ◽  
Periampullary Carcinomas ◽  
Radiomics Signature

PurposeTo establish and validate a radiomics nomogram for preoperatively predicting lymph node (LN) metastasis in periampullary carcinomas.Materials and MethodsA total of 122 patients with periampullary carcinoma were assigned into a training set (n = 85) and a validation set (n = 37). The preoperative CT radiomics of all patients were retrospectively assessed and the radiomic features were extracted from portal venous-phase images. The one-way analysis of variance test and the least absolute shrinkage and selection operator regression were used for feature selection. A radiomics signature was constructed with logistic regression algorithm, and the radiomics score was calculated. Multivariate logistic regression model integrating independent risk factors was adopted to develop a radiomics nomogram. The performance of the radiomics nomogram was assessed by its calibration, discrimination, and clinical utility with independent validation.ResultsThe radiomics signature, constructed by seven selected features, was closely related to LN metastasis in the training set (p &lt; 0.001) and validation set (p = 0.017). The radiomics nomogram that incorporated radiomics signature and CT-reported LN status demonstrated favorable calibration and discrimination in the training set [area under the curve (AUC), 0.853] and validation set (AUC, 0.853). The decision curve indicated the clinical utility of our nomogram.ConclusionOur CT-based radiomics nomogram, incorporating radiomics signature and CT-reported LN status, could be an individualized and non-invasive tool for preoperative prediction of LN metastasis in periampullary carcinomas, which might assist clinical decision making.

Definitive Chemoradiation with Concurrent Weekly Cisplatin in the Treatment of Esophageal Squamous Cell Carcinoma – A Simplistic Approach

2021 ◽  
Vol 6 (2) ◽  
pp. 181-187
Author(s):  
Imtiaz Ahmed ◽  
Sapna Krishnamurthy ◽  
Rohan Bhise
Keyword(s):  
Squamous Cell ◽  
Treatment Time ◽  
Univariate Analysis ◽  
Cell Cancer ◽  
Locally Advanced ◽  
Complete Response ◽  
Response To Treatment ◽  
Prolonged Treatment ◽  
Weekly Chemotherapy ◽  
Weekly Cisplatin

Purpose: Chemoradiation is the standard of care in locally advanced/ inoperable esophageal squamous cell cancer (ESCC). Though combination chemotherapy with Cisplatin and 5-Fluorouracil is the standard, it has low compliance due to toxicities and prolonged treatment time. Hence there is a window of opportunity to explore a safer chemotherapy regimen without compromising the treatment outcome. Methods: 55 patients of ESCC who were treated with definitive External Beam Radiotherapy (EBRT) to a dose of 50.4 – 59.4 Gray and concurrent weekly Cisplatin (or Carboplatin) were retrospectively evaluated for treatment efficacy and outcomes. 2 year Overall Survival (OS) and Progression Free Survival (PFS) were evaluated. Prognostic variables were assessed with respect to OS in Univariate analysis. Results: Median age at presentation was 58 years. 29 (53%) had lesion in the upper third of esophagus. 40 (72%) had T3 disease and 31 (56%) were node positive. All patients (100%) completed planned radiotherapy dose. 54 (98%) received 4 or more cycles of weekly chemotherapy. Mean overall treatment time was 43 days. Only 7 patients (12.7%) had grade 3 or more acute toxicity. 36 (65.5%) had complete response. At median follow-up of 13.7 months, the median OS was 15.2 months and 2 year OS was 42.6%. On univariate analysis, patients with comorbidities and lower third lesion had poor OS (p=0.016 and p=0.002). Stage II disease and complete response to treatment showed better OS (p=0.02 and p=0.00). Conclusion: Radical chemoradiation with weekly Cisplatin in ESCC is a simple and effective regimen which needs to be explored in larger trials.

Development and validation of a multiparametric MRI-based radiomics signature for distinguishing between indolent and aggressive prostate cancer

2021 ◽  
pp. 20210191
Author(s):  
Liuhui Zhang ◽  
Donggen Jiang ◽  
Chujie Chen ◽  
Xiangwei Yang ◽  
Hanqi Lei ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Multiparametric Mri ◽  
Curve Analysis ◽  
Training Set ◽  
Threshold Probability ◽  
Roc Curve Analysis ◽  
Aggressive Prostate Cancer ◽  
Therapeutic Decisions ◽  
Validation Set ◽  
Radiomics Signature

Objectives: To develop and validate a noninvasive MRI-based radiomics signature for distinguishing between indolent and aggressive prostate cancer (PCa) prior to therapy. Methods: In all, 139 qualified and pathology-confirmed PCa patients were divided into a training set (n = 93) and a validation set (n = 46). A total of 1576 radiomics features were extracted from the T2WI (n = 788) and DWI (n = 788) for each patient. The Select K Best and the least absolute shrinkage and selection operator (LASSO) regression algorithm were used to construct a radiomics signature in the training set. The predictive performance of the radiomics signature was assessed in the training set and then validated in the validation set by receiver operating characteristic (ROC) curve analysis. We computed the calibration curve and the decision curve to evaluate the calibration and clinical usefulness of the signature. Results: nine radiomics features were identified to form the radiomics signature. The radiomics score (Rad-score) was significantly different between indolent and aggressive PCa (p < 0.001). The radiomics signature exhibited favorable discrimination between the indolent and aggressive PCa groups in the training set (AUC: 0.853, 95% CI: 0.766 to 0.941) and validation set (AUC: 0.901, 95% CI: 0.793 to 1.000). The decision curve analysis showed that a greater net benefit would be obtained when the threshold probability ranged from 20 to 90%. Conclusions: The multiparametric MRI-based radiomics signature can potentially serve as a noninvasive tool for distinguishing between indolent and aggressive PCa prior to therapy. Advances in knowledge: The multiparametric MRI-based radiomics signature has the potential to noninvasively distinguish between the indolent and aggressive PCa, which might aid clinicians in making personalized therapeutic decisions.

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