Development and Validation of a Radiomics-based Model to Predict Local Progression-free Survival After Chemo-radiotherapy in Patients With Esophageal Squamous Cell Cancer

Luo, Hui; Chen, Yingying; Wu, Sheng-Xi; Huang, Shao-Fu; Xu, Hanbing; Xue, Ren-Liang; Du, Ze-Sen; Li, Xu‐Yuan; Lin, Lian-Xin; Huang, Huaiyi

doi:10.21203/rs.3.rs-577680/v1

Cited by 3 publications

(5 citation statements)

References 19 publications

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“…Radiomics studies in EC most focused on the prediction of lymph node metastasis, radiation-induced diseases in the earlier [19][20][21]. What's more, there were still few researches in the prediction of both PFS and OS [16,[22][23][24]. The establishment of accurate prediction models of PFS and OS were conducive to clinical decision-making and are expected to improve the survival rate of advanced ESCC patients.…”

Section: Discussionmentioning

confidence: 99%

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Machine learning models predict overall survival and progression free survival of non-surgical esophageal cancer patients with chemoradiotherapy based on CT image radiomics signatures

Cui

Xiang

et al. 2022

Radiat Oncol

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Purpose To construct machine learning models for predicting progression free survival (PFS) and overall survival (OS) with esophageal squamous cell carcinoma (ESCC) patients. Methods 204 ESCC patients were randomly divided into training cohort (n = 143) and test cohort (n = 61) according to the ratio of 7:3. Two radiomics models were constructed by radiomics features, which were selected by LASSO Cox model to predict PFS and OS, respectively. Clinical features were selected by univariate and multivariate Cox proportional hazards model (p < 0.05). Combined radiomics and clinical model was developed by selected clinical and radiomics features. The receiver operating characteristic curve, Kaplan Meier curve and nomogram were used to display the capability of constructed models. Results There were 944 radiomics features extracted based on volume of interest in CT images. There were six radiomics features and seven clinical features for PFS prediction and three radiomics features and three clinical features for OS prediction; The radiomics models showed general performance in training cohort and test cohort for prediction for prediction PFS (AUC, 0.664, 0.676. C-index, 0.65, 0.64) and OS (AUC, 0.634, 0.646.C-index, 0.64, 0.65). The combined models displayed high performance in training cohort and test cohort for prediction PFS (AUC, 0.856, 0.833. C-index, 0.81, 0.79) and OS (AUC, 0.742, 0.768. C-index, 0.72, 0.71). Conclusion We developed combined radiomics and clinical machine learning models with better performance than radiomics or clinical alone, which were used to accurate predict 3 years PFS and OS of non-surgical ESCC patients. The prediction results could provide a reference for clinical decision.

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Section: Discussionmentioning

confidence: 99%

“…But, PET/ CT examination was highly expensive than CT or MRI and the sample was small in the study. Luo et al [23] also developed a nomogram model for predicting local PFS based on CT images and C-index was 0.723 in test cohort. This study included clinical response to develop model and obtained a fine result.…”

Section: Discussionmentioning

confidence: 99%

Machine learning models predict overall survival and progression free survival of non-surgical esophageal cancer patients with chemoradiotherapy based on CT image radiomics signatures

Cui

Xiang

et al. 2022

Radiat Oncol

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“…Although the AUC of this model reached more than 80% in the training and validation sets, it only evaluated the short-term treatment response and did not present the long-term evaluation like the survival analysis in our study (26). Luo et al also constructed a nomogram model for predicting local progress-free survival (LPFS) after CCRT based on radiomics and clinical features (27). Different from above study, we aimed to predict the overall survival rate, and the progression of the patient's condition did not be considered as the end point.…”

Section: Discussionmentioning

confidence: 99%

CT-based radiomics for predicting radio-chemotherapy response and overall survival in nonsurgical esophageal carcinoma

Pan

Yang

et al. 2022

Preprint

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Background To predict treatment response and 2 years overall survival (OS) of radio-chemotherapy in patients with esophageal cancer (EC) by radiomics based on the computed tomography (CT) images. Methods This study retrospectively collected 171 nonsurgical EC patients treated with radiochemotherapy from Jan 2010 to Jan 2019. 80 patients were randomly divided into training (n = 64) and validation (n = 16) cohorts to predict the radiochemotherapy response. The models predicting treatment response were established by Lasso and logistic regression. A total of 156 patients were allocated into the training cohort (n = 110), validation cohort (n = 23) and test set (n = 23) to predict 2-year OS. The models predicting 2-year OS were established by the Lasso Cox model and Cox proportional hazards model. Results To predict the radiochemotherapy response, WFK as a radiomics feature, and clinical stages and clinical M stages (cM) as clinical features were selected to construct the clinical-radiomics model, achieving 0.78 and 0.75 AUC (area under the curve) in the training and validation sets, respectively. Furthermore, radiomics features called WFI and WGI combined with clinical features (smoking index, pathological types, cM) were the optimal predictors to predict 2-year OS. The AUC values of the clinical-radiomics model were 0.71 and 0.70 in the training set and validation set, respectively. Conclusions This study demonstrated that planning CT-based radiomics showed the predictability of the radiochemotherapy response and 2-year OS in nonsurgical esophageal carcinoma. The predictive results prior to treatment have the potential to assist physicians in choosing the optimal therapeutic strategy to prolong overall survival.

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“…It can predict patient viability based on imaging features and determine the level of treatment needed to achieve optimal survival. The prediction of recurrence, metastasis, surgical margins and therapeutic responses can be used to formulate an optimal therapeutic strategy for individual patients (21,26).…”

Section: Ai Assists Pm For Tumors Diagnosed Via Medical Imagingmentioning

confidence: 99%

“…Patients with poor surgical results may want to consider changing the surgical method or choosing non-surgical treatment. Patients with a higher risk of tumor recurrence and metastasis should continue to receive neoadjuvant radiotherapy and chemotherapy (21,26,(178)(179)(180).…”

Section: Ai Assists Pm For Tumors Diagnosed Via Medical Imagingmentioning

confidence: 99%

Artificial intelligence assists precision medicine in cancer treatment

Liao

et al. 2023

Front. Oncol.

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Cancer is a major medical problem worldwide. Due to its high heterogeneity, the use of the same drugs or surgical methods in patients with the same tumor may have different curative effects, leading to the need for more accurate treatment methods for tumors and personalized treatments for patients. The precise treatment of tumors is essential, which renders obtaining an in-depth understanding of the changes that tumors undergo urgent, including changes in their genes, proteins and cancer cell phenotypes, in order to develop targeted treatment strategies for patients. Artificial intelligence (AI) based on big data can extract the hidden patterns, important information, and corresponding knowledge behind the enormous amount of data. For example, the ML and deep learning of subsets of AI can be used to mine the deep-level information in genomics, transcriptomics, proteomics, radiomics, digital pathological images, and other data, which can make clinicians synthetically and comprehensively understand tumors. In addition, AI can find new biomarkers from data to assist tumor screening, detection, diagnosis, treatment and prognosis prediction, so as to providing the best treatment for individual patients and improving their clinical outcomes.

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Development and Validation of a Radiomics-based Model to Predict Local Progression-free Survival After Chemo-radiotherapy in Patients With Esophageal Squamous Cell Cancer

Cited by 3 publications

References 19 publications

Machine learning models predict overall survival and progression free survival of non-surgical esophageal cancer patients with chemoradiotherapy based on CT image radiomics signatures

Machine learning models predict overall survival and progression free survival of non-surgical esophageal cancer patients with chemoradiotherapy based on CT image radiomics signatures

CT-based radiomics for predicting radio-chemotherapy response and overall survival in nonsurgical esophageal carcinoma

Artificial intelligence assists precision medicine in cancer treatment

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