Prediction of induction chemotherapy response in locoregionally advanced nasopharyngeal carcinoma based on three pretreatment non-Gaussian diffusion MRI models
Abstract Objectives To explore the value of continuous-time random walk (CTRW), fractional order calculus (FROC), and stretched exponential model (SEM) in predicting for response to induction chemotherapy (IC) in nasopharyngeal carcinoma (NPC). Methods This prospective study included the NPC partici...
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BMC
2025-07-01
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| Series: | BMC Medical Imaging |
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| Online Access: | https://doi.org/10.1186/s12880-025-01752-8 |
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| author | Huanhuan Ren Xinyu Chen Jing Yang Junhao Huang Jing Zhang Zhiqiang Peng Lisha Nie Daihong Liu Jiuquan Zhang |
| author_facet | Huanhuan Ren Xinyu Chen Jing Yang Junhao Huang Jing Zhang Zhiqiang Peng Lisha Nie Daihong Liu Jiuquan Zhang |
| author_sort | Huanhuan Ren |
| collection | DOAJ |
| description | Abstract Objectives To explore the value of continuous-time random walk (CTRW), fractional order calculus (FROC), and stretched exponential model (SEM) in predicting for response to induction chemotherapy (IC) in nasopharyngeal carcinoma (NPC). Methods This prospective study included the NPC participants (n = 79) who underwent non-Gaussian (CTRW, FROC, and SEM) model from December 2023 to October 2024. Eight diffusion parameters, namely αCTRW, βCTRW, DmCTRW, βFROC, µFROC, DFROC, αSEM, and DDCSEM of the primary tumor, were derived from three diffusion models before treatment. These diffusion metrics were compared between the response and non-response groups, as defined by the RECIST 1.1 criteria. Univariate and multivariate logistic analysis was used to determine the optimal diffusion metrics and clinicopathologic variables for classifying the IC response. Predictive models were established using logistic regression. Receiver operating characteristic (ROC) curves were used to evaluate their predictive ability. Results Participants enrolled in this study were classified into response group (n = 60) and non-response group (n = 19). Participants who responded well to IC had lower αCTRW and βCTRW values (p = 0.015, p = 0.011). αCTRW and βCTRW were independently associated with the response of chemotherapy in NPC (odds ratio [OR]: 0.444 [95% confidence interval [CI], 0.214–0.922], p = 0.029; 0.338 [95% CI, 0.139–0.822], p = 0.017). ROC analysis showed the predictive performance of αCTRW, βCTRW, and α+βCTRW values for response to IC (AUCs of 0.710, [95% CI, 0.597–0.806], 0.713 [95% CI, 0.600-0.809], and 0.829 [95% CI, 0.728–0.904], respectively) in NPC participants. Conclusions The developed model combining αCTRW and βCTRW showed good performance in predicting treatment response to IC in NPC. Relevance statement We developed a logistic regression model based on pre-treatment non-Gaussian diffusion MRI parameters to reliably predict early response to induction chemotherapy in locally advanced nasopharyngeal carcinoma. This model may aid in personalizing treatment and minimizing unnecessary toxicity for non-responders. |
| format | Article |
| id | doaj-art-d120ca8b1fb14ee2a033ae8d0e4d9322 |
| institution | Kabale University |
| issn | 1471-2342 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
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| series | BMC Medical Imaging |
| spelling | doaj-art-d120ca8b1fb14ee2a033ae8d0e4d93222025-08-20T03:45:41ZengBMCBMC Medical Imaging1471-23422025-07-0125111110.1186/s12880-025-01752-8Prediction of induction chemotherapy response in locoregionally advanced nasopharyngeal carcinoma based on three pretreatment non-Gaussian diffusion MRI modelsHuanhuan Ren0Xinyu Chen1Jing Yang2Junhao Huang3Jing Zhang4Zhiqiang Peng5Lisha Nie6Daihong Liu7Jiuquan Zhang8Department of Radiology, Chongqing University Cancer HospitalDepartment of Pathology, Chongqing University Cancer HospitalDepartment of Radiology, Chongqing University Cancer HospitalDepartment of Radiology, Chongqing University Cancer HospitalDepartment of Radiology, Chongqing University Cancer HospitalDepartment of Radiology, GanZhou Cancer HospitalGE HealthCare MR ResearchDepartment of Radiology, Chongqing University Cancer HospitalDepartment of Radiology, Chongqing University Cancer HospitalAbstract Objectives To explore the value of continuous-time random walk (CTRW), fractional order calculus (FROC), and stretched exponential model (SEM) in predicting for response to induction chemotherapy (IC) in nasopharyngeal carcinoma (NPC). Methods This prospective study included the NPC participants (n = 79) who underwent non-Gaussian (CTRW, FROC, and SEM) model from December 2023 to October 2024. Eight diffusion parameters, namely αCTRW, βCTRW, DmCTRW, βFROC, µFROC, DFROC, αSEM, and DDCSEM of the primary tumor, were derived from three diffusion models before treatment. These diffusion metrics were compared between the response and non-response groups, as defined by the RECIST 1.1 criteria. Univariate and multivariate logistic analysis was used to determine the optimal diffusion metrics and clinicopathologic variables for classifying the IC response. Predictive models were established using logistic regression. Receiver operating characteristic (ROC) curves were used to evaluate their predictive ability. Results Participants enrolled in this study were classified into response group (n = 60) and non-response group (n = 19). Participants who responded well to IC had lower αCTRW and βCTRW values (p = 0.015, p = 0.011). αCTRW and βCTRW were independently associated with the response of chemotherapy in NPC (odds ratio [OR]: 0.444 [95% confidence interval [CI], 0.214–0.922], p = 0.029; 0.338 [95% CI, 0.139–0.822], p = 0.017). ROC analysis showed the predictive performance of αCTRW, βCTRW, and α+βCTRW values for response to IC (AUCs of 0.710, [95% CI, 0.597–0.806], 0.713 [95% CI, 0.600-0.809], and 0.829 [95% CI, 0.728–0.904], respectively) in NPC participants. Conclusions The developed model combining αCTRW and βCTRW showed good performance in predicting treatment response to IC in NPC. Relevance statement We developed a logistic regression model based on pre-treatment non-Gaussian diffusion MRI parameters to reliably predict early response to induction chemotherapy in locally advanced nasopharyngeal carcinoma. This model may aid in personalizing treatment and minimizing unnecessary toxicity for non-responders.https://doi.org/10.1186/s12880-025-01752-8Diffusion-weighted imagingNasopharyngeal carcinomaInduction chemotherapy |
| spellingShingle | Huanhuan Ren Xinyu Chen Jing Yang Junhao Huang Jing Zhang Zhiqiang Peng Lisha Nie Daihong Liu Jiuquan Zhang Prediction of induction chemotherapy response in locoregionally advanced nasopharyngeal carcinoma based on three pretreatment non-Gaussian diffusion MRI models BMC Medical Imaging Diffusion-weighted imaging Nasopharyngeal carcinoma Induction chemotherapy |
| title | Prediction of induction chemotherapy response in locoregionally advanced nasopharyngeal carcinoma based on three pretreatment non-Gaussian diffusion MRI models |
| title_full | Prediction of induction chemotherapy response in locoregionally advanced nasopharyngeal carcinoma based on three pretreatment non-Gaussian diffusion MRI models |
| title_fullStr | Prediction of induction chemotherapy response in locoregionally advanced nasopharyngeal carcinoma based on three pretreatment non-Gaussian diffusion MRI models |
| title_full_unstemmed | Prediction of induction chemotherapy response in locoregionally advanced nasopharyngeal carcinoma based on three pretreatment non-Gaussian diffusion MRI models |
| title_short | Prediction of induction chemotherapy response in locoregionally advanced nasopharyngeal carcinoma based on three pretreatment non-Gaussian diffusion MRI models |
| title_sort | prediction of induction chemotherapy response in locoregionally advanced nasopharyngeal carcinoma based on three pretreatment non gaussian diffusion mri models |
| topic | Diffusion-weighted imaging Nasopharyngeal carcinoma Induction chemotherapy |
| url | https://doi.org/10.1186/s12880-025-01752-8 |
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