Association of MRI-derived radiomic biomarker with disease-free survival in patients with early-stage cervical cancer

Jin, Fang; Zhang, Bin; Wang, Shuo; Jin, Yan; Wang, Fei; Ding, Yingying; Chen, Justin; Chen, Liting; Li, Yueyue; Li, Minmin; Chen, Zhuozhi; Liu, Lizhi; Liu, Zhenyu; Tian, Jie; Zhang, Shuixing

doi:10.7150/thno.37429

Cited by 65 publications

(53 citation statements)

References 34 publications

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“…To our knowledge, for predicting the survival of IPMN, a similar nomogram was established and demonstrated effectively compared with 7 th TNM stage (C‐indexes, 0.756 vs. 0.645) 29 . Compared with our model, the nomogram created earlier was unreasonable for the parameters selection, while our model was reasonable and logic by lasso regression analysis which can prevent over fitting of the model and multivariate cox regression analysis that ensure the accuracy of model 30 . In our model, we recruited age, pathology grade, and examined lymph nodes other than TNM stage, avoiding the insufficiency of TNM stage.…”

Section: Discussionmentioning

confidence: 91%

Analyzing and predicting the LNM rate and prognosis of patients with intraductal papillary mucinous neoplasm of the pancreas

et al. 2021

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Background Current the surveillance and management are controversial for patients with IPMN. We aimed to develop an alternative nomogram to individualize IPMN prognosis and LNM. Methods Based on the data from SEER database of patients diagnosed with IPMN between 2004 and 2015, a nomogram predicting the survival and LNM of IPMN based on univariate and multivariate and Lasso regression analysis was performed, internally and externally validated, and measured by C‐index, and decision curve analysis (DCA), and compared to the 7th TNM stage. Results A total of 941 patients were included. Age, T stage examined nodes, tumor size, and pathology grade were identified as an independent factor for predicting LNM. The nomogram we established to predict LNM had a high predicting value with a C‐index value of 0.735 and an AUC value of 0.753. Interestingly, including T1 stage, we found an inverse correlation was between age and LNM. In addition, nomogram for predicting CSS also performed better than TNM stage both in the internal validation group (1‐year AUC:0.753 vs. 0.693, 3‐year AUC: 0.801 vs. 0.731, 5‐year AUC: 0.803 vs. 0.733) and external validation group (1‐year AUC: 0.761 vs. 0.701, 3‐year AUC: 0.772 vs. 0.713, 5‐year AUC:0.811 vs. 0.735). DCA analysis showed the nomogram showed a greater benefit across the period of follow‐up compared to 7th TNM stage. Conclusion A nomogram based on multivariate and Lasso regression analysis showed great clinical usability compared with current criteria. Also, for LNM of IPMN, younger age patients with IPMN should be attached more importance.

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Section: Discussionmentioning

confidence: 91%

Analyzing and predicting the LNM rate and prognosis of patients with intraductal papillary mucinous neoplasm of the pancreas

et al. 2021

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“…One of the wavelet features, HLH_GLSZM_small_area_low_grade_level_emphasis, was reported in an earlier study conducted on T2WI for the prediction of Disease-free survival (DFS) in uterine cervical cancer [49], using 18 radiomic features and lymphovascular space invasion (LVI) with contrast MRI, to obtain a Rad score for the prediction of the DFS. Another wavelet feature, LLL_glrlm_Gray_Level_NonUniformity_Normalized, was also found to be significant in the prediction of recurrence in uterine cervical cancer [50] and 5-year survival [51].…”

Section: Discussionmentioning

confidence: 99%

Combination of Radiomics and Machine Learning with Diffusion-Weighted MR Imaging for Clinical Outcome Prognostication in Cervical Cancer

et al. 2021

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Objectives: To explore the potential of Radiomics alone and in combination with a diffusion-weighted derived quantitative parameter, namely the apparent diffusion co-efficient (ADC), using supervised classification algorithms in the prediction of outcomes and prognosis. Materials and Methods: Retrospective evaluation of the imaging was conducted for a study cohort of uterine cervical cancer, candidates for radical treatment with chemo radiation. ADC values were calculated from the darkest part of the tumor, both before (labeled preADC) and post treatment (labeled postADC) with chemo radiation. Post extraction of 851 Radiomics features and feature selection analysis—by taking the union of the features that had Pearson correlation >0.35 for recurrence, >0.49 for lymph node and >0.40 for metastasis—was performed to predict clinical outcomes. Results: The study enrolled 52 patients who presented with variable FIGO stages in the age range of 28–79 (Median = 53 years) with a median follow-up of 26.5 months (range: 7–76 months). Disease recurrence occurred in 12 patients (23%). Metastasis occurred in 15 patients (28%). A model generated with 24 radiomics features and preADC using a monotone multi-layer perceptron neural network to predict the recurrence yields an AUC of 0.80 and a Kappa value of 0.55 and shows that the addition of radiomics features to ADC values improves the statistical metrics by approximately 40% for AUC and approximately 223% for Kappa. Similarly, the neural network model for prediction of metastasis returns an AUC value of 0.84 and a Kappa value of 0.65, thus exceeding performance expectations by approximately 25% for AUC and approximately 140% for Kappa. There was a significant input of GLSZM features (SALGLE and LGLZE) and GLDM features (SDLGLE and DE) in correlation with clinical outcomes of recurrence and metastasis. Conclusions: The study is an effort to bridge the unmet need of translational predictive biomarkers in the stratification of uterine cervical cancer patients based on prognosis.

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“…One of the wavelet features, HLH_GLSZM_small_area_low_grade_level_emphasis was reported in an earlier study done on T2WI, for prediction of Disease-free survival (DFS) in uterine cervical cancer [49], using 18 radiomic features and lymphovascular space invasion (LVI) with contrast MRI to obtain a Rad score for prediction of the DFS. Another wavelet feature LLL_glrlm_Gray_Level_NonUniformity_Normalized was also found to be significant in the prediction of recurrence in uterine cervical cancer [50] and 5-year survival [51].…”

Section: Discussionmentioning

confidence: 99%

Combination of Radiomics and Machine Learning with Diffusion-Weighted MR Imaging for Clinical Outcome Prognostication in Cervical Cancer

Jajodia¹,

Gupta²,

Prosch³

et al. 2021

Preprint

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Objectives: To explore the potential of Radiomics alone and in combination with diffusion-weighted derived quantitative parameter namely apparent diffusion co-efficient (ADC) using supervised classification algorithms in predicting outcomes and prognosis. Materials and Methods: Retrospective evaluation of the imaging was done for a study cohort of uterine cervical cancer, candidates for radical treatment with chemo radiation. ADC values were calculated from the darkest part of the tumor, both before (labeled preADC) and post treatment (labeled postADC) with chemo radiation. Post extraction of 851 Radiomics features and feature selection by taking the union of the features which had Pearson correlation >0.35 for recurrence, >0.49 for lymph node and >0.40 for metastasis, analysis was done to predict clinical outcomes. Results: The study enrolled 52 patients who presented with variable FIGO stages and age range 28–79 (Median = 53 years) with median follow-up of 26.5 months (range, 7–76 months). Disease recurrence occurred in 12 patients (23%). Metastasis occurred in 15 patients (28%). A model generated with 24 radiomics features and preADC using a monotone multi-layer perceptron neural network to predict the recurrence yields AUC of 0.80 and kappa value as 0.55 and shows that addition of radiomics features on ADC values improves the statistical metrics by 40% approximately for AUC and 223% approximately for Kappa. Similarly, neural network model for prediction of metastasis re-turns AUC of 0.84 and kappa value as 0.65 over performs by 25% for AUC and 140% for Kappa approximately. There was a significant input of GLSZM features (SALGLE and LGLZE) and GLDM features (SDLGLE and DE) correlation with clinical outcomes of recurrence and metastasis. Conclusions: The study is an effort to bridge the unmet need of translational predictive biomarkers in stratification of uterine cervical cancer patients based on prognosis.

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Association of MRI-derived radiomic biomarker with disease-free survival in patients with early-stage cervical cancer

Cited by 65 publications

References 34 publications

Analyzing and predicting the LNM rate and prognosis of patients with intraductal papillary mucinous neoplasm of the pancreas

Analyzing and predicting the LNM rate and prognosis of patients with intraductal papillary mucinous neoplasm of the pancreas

Combination of Radiomics and Machine Learning with Diffusion-Weighted MR Imaging for Clinical Outcome Prognostication in Cervical Cancer

Combination of Radiomics and Machine Learning with Diffusion-Weighted MR Imaging for Clinical Outcome Prognostication in Cervical Cancer

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