Dataset Information

Magnetic resonance imaging-based radiomics nomogram for the evaluation of therapeutic responses to neoadjuvant chemohormonal therapy in high-risk non-metastatic prostate cancer.

ABSTRACT:

Purpose

The aim of this study was to assess the potential application of a radiomics features-based nomogram for predicting therapeutic responses to neoadjuvant chemohormonal therapy (NCHT) in patients with high-risk non-metastatic prostate cancer (PCa).

Methods

Clinicopathologic information was retrospectively collected from 162 patients with high-risk non-metastatic PCa receiving NCHT and radical prostatectomy at our center. The postoperative pathological findings were used as the gold standard for evaluating the efficacy of NCHT. The least absolute shrinkage and selection operator (LASSO) was conducted to develop radiomics signature. Multivariate logistic regression analyses were conducted to identify the predictors of a positive pathological response to NCHT, and a nomogram was constructed based on these predictors.

Results

Sixty-three patients (38.89%) experienced positive pathological response to NCHT. Receiver operating characteristic analyses showed that the area under the curve (AUC) of periprostatic fat (PPF) radiomics signature was 0.835 (95% CI, 0.754-0.898), while the AUC of intratumoral radiomics signature was 0.822 (95% CI, 0.739-0.888). Multivariate logistic regression analysis revealed that PSA level, PPF radiomics signature and intratumoral radiomics signature were independent predictors of positive pathological response. A nomogram based on these three predictors was constructed. The AUC was 0.908 (95% CI, 0.839-0.954). The Hosmer-Lemeshow goodness-of-fit test showed that the nomogram was well calibrated. Decision curve analysis revealed the favorable clinical practicability of the nomogram. The nomogram was successfully validated in the validation cohort. Kaplan-Meier analyses showed that nomogram and positive pathological response were significantly related with survival of PCa.

Conclusion

The radiomics-clinical nomogram based on mpMRI radiomics features exhibited superior predictive ability for positive pathological response to NCHT in high-risk non-metastatic PCa.

SUBMITTER: Wu XH

PROVIDER: S-EPMC11258568 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature

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Publications

Magnetic resonance imaging-based radiomics nomogram for the evaluation of therapeutic responses to neoadjuvant chemohormonal therapy in high-risk non-metastatic prostate cancer.

Wu Xiao-Hui XH Ruan Zhong-Tian ZT Ke Zhi-Bin ZB Lin Fei F Chen Jia-Yin JY Xue Yu-Ting YT Lin Bin B Chen Shao-Hao SH Chen Dong-Ning DN Zheng Qing-Shui QS Xue Xue-Yi XY Wei Yong Y Xu Ning N

Cancer medicine 20240701 14

<h4>Purpose</h4>The aim of this study was to assess the potential application of a radiomics features-based nomogram for predicting therapeutic responses to neoadjuvant chemohormonal therapy (NCHT) in patients with high-risk non-metastatic prostate cancer (PCa).<h4>Methods</h4>Clinicopathologic information was retrospectively collected from 162 patients with high-risk non-metastatic PCa receiving NCHT and radical prostatectomy at our center. The postoperative pathological findings were used as t ...[more]

PMID: 39031016

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Project description:ObjectiveTo build and validate a periprostatic fat magnetic resonance imaging (MRI) based radiomics nomogram for prediction of biochemical recurrence-free survival (bRFS) of patients with non-metastatic prostate cancer (PCa) receiving radical prostatectomy (RP).MethodsA retrospective study was conducted on 356 patients with non-metastatic PCa who underwent preoperative mpMRI followed by RP treatment at our institution. Radiomic features were extracted from both intratumoral region and the periprostatic fat region, which were segmented on images obtained through T2-weighted imaging (T2WI) and apparent-diffusion coefficient (ADC) imaging. Three radiomics models were developed by applying the Least absolute shrinkage and selection operator (LASSO) Cox regression, followed by Cox risk regression to construct a combined radiomics-clinical model by integrating the optimal radiomics score and clinicopathological risk factors to draw a nomogram. The predictive performance was evaluated using receiver operating characteristic (ROC) curves, Kaplan-Meier analysis and calibration curves.ResultsOne hundred and twenty-one patients (33.98%) experienced biochemical recurrence. ROC analyses showed that the Area Under the Curve (AUC) of the periprostatic fat-intratumoral radiomics model demonstrated the highest AUC at 0.921 (95%CI, 0.857-0.981), 0.875 (95%CI, 0.763-0.950), 0.854 (95%CI, 0.706-0.923) for 1-year, 3-years and 5-years bRFS. Multivariate Cox regression analysis revealed that Pathological T stage, ISUP grading group and Positive surgical margin were independent prognostic factors for predicting bRFS. A radiomics-clinical nomogram based on these clinical predictors and periprostatic fat-intratumoral radiomics score was constructed. Kaplan-Meier analyses showed that radiomics-clinical nomogram was significantly related with survival of PCa (P < 0.001); and calibration curves revealed the predicted and observed survival probability of 1-year, 3-year and 5-year bRFS had high degree of consistency in the training and validation group. The radiomics-clinical nomogram showed a significant improvement than the clinical model for 1-year (AUC, 0.944; 95%CI, 0.912-0.990 vs. AUC, 0.839; 95%CI, 0.661-0.928; P = 0.009), 3-year (AUC, 0.864; 95%CI, 0.772-0.969 vs. AUC, 0.776; 95%CI, 0.602-0.872; P = 0.008), and 5-year bRFS (AUC, 0.907; 95%CI, 0.836-0.982 vs. AUC, 0.819; 95%CI, 0.687-0.915; P = 0.027).ConclusionsThis study developed and validated the radiomics-clinical nomogram for the prediction of bRFS in non-metastatic PCa patients underwent RP.

Project description:Purpose: The construction and validation of a radiomics nomogram based on machine learning using magnetic resonance image (MRI) for predicting the efficacy of neoadjuvant chemotherapy (NACT) in patients with breast cancer (BCa). Methods: This retrospective investigation consisted of 158 patients who were diagnosed with BCa and underwent MRI before NACT, of which 33 patients experienced pathological complete response (pCR) by the postoperative pathological examination. The patients with BCa were divided into the training set (n = 110) and test set (n = 48) randomly. The features were selected by the maximum relevance minimum redundancy (mRMR) and absolute shrinkage and selection operator (LASSO) algorithm in the training set. In return, the radiomics signature was established using machine learning. The predictive score of each patient was calculated using the radiomics signature formula. Finally, the predictive scores and clinical factors were used to perform the multivariate logistic regression and construct the nomogram. Receiver operating characteristics (ROC) analyses were used to assess and validate the diagnostic accuracy of the nomogram in the test set. Lastly, the usefulness of the nomogram was confirmed via decision curve analysis (DCA). Results: The radiomics signature was well-discriminated in the training set [AUC 0.835, specificity 71.32%, and sensitivity 82.61%], and test set (AUC 0.834, specificity 73.21%, and sensitivity 80%). Containing the radiomics signature and hormone status, the radiomics nomogram showed good calibration and discrimination in the training set [AUC 0.888, specificity 79.31%, and sensitivity 86.96%] and test set (AUC 0.879, specificity 82.19%, and sensitivity 83.57%). The decision curve indicated the clinical usefulness of our nomogram. Conclusion: Our radiomics nomogram showed good discrimination in patients with BCa who experience pCR after NACT. The model may aid physicians in predicting how specific patients may respond to BCa treatments in the future.

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Project description:BackgroundMicrosatellite instability (MSI) status is an important indicator for screening patients with endometrial cancer (EC) who have potential Lynch syndrome (LS) and may benefit from immunotherapy. This study aimed to develop a magnetic resonance imaging (MRI)-based radiomics nomogram for the prediction of MSI status in EC.MethodsA total of 296 patients with histopathologically diagnosed EC were enrolled, and their MSI status was determined using immunohistochemical (IHC) analysis. Patients were randomly divided into the training cohort (n=236) and the validation cohort (n=60) at a ratio of 8:2. To predict the MSI status in EC, the tumor radiomics features were extracted from T2-weighted images and contrast-enhanced T1-weighted images, which in turn were selected using one-way analysis of variance (ANOVA) and the least absolute shrinkage and selection operator (LASSO) algorithm to build the radiomics signature (radiomics score; radscore) model. Five clinicopathologic characteristics were used to construct a clinicopathologic model. Finally, the nomogram model combining radscore and clinicopathologic characteristics was constructed. The performance of the three models was evaluated using receiver operating characteristic (ROC), calibration, and decision curve analyses (DCA).ResultsTotals of 21 radiomics features and five clinicopathologic characteristics were selected to develop the radscore and clinicopathological models. The radscore and clinicopathologic models achieved an area under the curve (AUC) of 0.752 and 0.600, respectively, in the training cohort; and of 0.723 and 0.615, respectively, in the validation cohort. The radiomics nomogram model showed improved discrimination efficiency compared with the radscore and clinicopathologic models, with an AUC of 0.773 and 0.740 in the training and validation cohorts, respectively. The calibration curve analysis and DCA showed favorable calibration and clinical utility of the nomogram model.ConclusionsThe nomogram incorporating MRI-based radiomics features and clinicopathologic characteristics could be a potential tool for the prediction of MSI status in EC.

Project description:Background Tumor grade is associated with the treatment and prognosis of endometrial cancer (EC). The accurate preoperative prediction of the tumor grade is essential for EC risk stratification. Herein, we aimed to assess the performance of a multiparametric magnetic resonance imaging (MRI)-based radiomics nomogram for predicting high-grade EC. Methods One hundred and forty-three patients with EC who had undergone preoperative pelvic MRI were retrospectively enrolled and divided into a training set (n =100) and a validation set (n =43). Radiomic features were extracted based on T2-weighted, diffusion-weighted, and dynamic contrast-enhanced T1-weighted images. The minimum absolute contraction selection operator (LASSO) was implemented to obtain optimal radiomics features and build the rad-score. Multivariate logistic regression analysis was used to determine the clinical MRI features and build a clinical model. We developed a radiomics nomogram by combining important clinical MRI features and rad-score. A receiver operating characteristic (ROC) curve was used to evaluate the performance of the three models. The clinical net benefit of the nomogram was assessed using decision curve analysis (DCA), net reclassification index (NRI), and integrated discrimination index (IDI). Results In total, 35/143 patients had high-grade EC and 108 had low-grade EC. The areas under the ROC curves of the clinical model, rad-score, and radiomics nomogram were 0.837 (95% confidence interval [CI]: 0.754–0.920), 0.875 (95% CI: 0.797–0.952), and 0.923 (95% CI: 0.869–0.977) for the training set; 0.857 (95% CI: 0.741–0.973), 0.785 (95% CI: 0.592–0.979), and 0.914 (95% CI: 0.827–0.996) for the validation set, respectively. The radiomics nomogram showed a good net benefit according to the DCA. NRIs were 0.637 (0.214–1.061) and 0.657 (0.079–1.394), and IDIs were 0.115 (0.077–0.306) and 0.053 (0.027–0.357) in the training set and validation set, respectively. Conclusion The radiomics nomogram based on multiparametric MRI can predict the tumor grade of EC before surgery and yield a higher performance than that of dilation and curettage.

Dataset Information

Magnetic resonance imaging-based radiomics nomogram for the evaluation of therapeutic responses to neoadjuvant chemohormonal therapy in high-risk non-metastatic prostate cancer.

Purpose

Methods

Results

Conclusion

Publications

Magnetic resonance imaging-based radiomics nomogram for the evaluation of therapeutic responses to neoadjuvant chemohormonal therapy in high-risk non-metastatic prostate cancer.

Similar Datasets

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