Radiomic prediction for durable response to high‐dose methotrexate‐based chemotherapy in primary central nervous system lymphoma
Abstract Background The rarity of primary central nervous system lymphoma (PCNSL) and treatment heterogeneity contributes to a lack of prognostic models for evaluating posttreatment remission. This study aimed to develop and validate radiomic‐based models to predict the durable response (DR) to high...
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Wiley
2024-09-01
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| Series: | Cancer Medicine |
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| Online Access: | https://doi.org/10.1002/cam4.70182 |
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| author | Haoyi Li Mingming Xiong Ming Li Caixia Sun Dao Zheng Leilei Yuan Qian Chen Song Lin Zhenyu Liu Xiaohui Ren |
| author_facet | Haoyi Li Mingming Xiong Ming Li Caixia Sun Dao Zheng Leilei Yuan Qian Chen Song Lin Zhenyu Liu Xiaohui Ren |
| author_sort | Haoyi Li |
| collection | DOAJ |
| description | Abstract Background The rarity of primary central nervous system lymphoma (PCNSL) and treatment heterogeneity contributes to a lack of prognostic models for evaluating posttreatment remission. This study aimed to develop and validate radiomic‐based models to predict the durable response (DR) to high‐dose methotrexate (HD‐MTX)‐based chemotherapy in PCNSL patients. Methods A total of 159 patients pathologically diagnosed with PCNSL between 2011 and 2021 across two institutions were enrolled. According to the NCCN guidelines, the DR was defined as the remission lasting ≥1 year after receiving HD‐MTX‐based chemotherapy. For each patient, a total of 1218 radiomic features were extracted from prebiopsy T1 contrast‐enhanced MR images. Multiple machine‐learning algorithms were utilized for feature selection and classification to build a radiomic signature. The radiomic‐clinical integrated models were developed using the random forest method. Model performance was externally validated to verify its clinical utility. Results A total of 105 PCNSL patients were enrolled after excluding 54 cases with ineligibility. The training and validation cohorts comprised 76 and 29 individuals, respectively. Among them, 65 patients achieved DR. The radiomic signature, consisting of 8 selected features, demonstrated strong predictive performance, with area under the curves of 0.994 in training cohort and 0.913 in validation cohort. This signature was independently associated with the DR in both cohorts. Both the radiomic signature and integrated models significantly outperformed the clinical models in two cohorts. Decision curve analysis underscored the clinical utility of the established models. Conclusions This radiomic signature and integrated models have the potential to accurately predict the DR to HD‐MTX‐based chemotherapy in PCNSL patients, providing valuable therapeutic insights. |
| format | Article |
| id | doaj-art-320b58aaae834a64a63e02f261dfe90d |
| institution | Kabale University |
| issn | 2045-7634 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | Cancer Medicine |
| spelling | doaj-art-320b58aaae834a64a63e02f261dfe90d2025-02-07T09:08:08ZengWileyCancer Medicine2045-76342024-09-011317n/an/a10.1002/cam4.70182Radiomic prediction for durable response to high‐dose methotrexate‐based chemotherapy in primary central nervous system lymphomaHaoyi Li0Mingming Xiong1Ming Li2Caixia Sun3Dao Zheng4Leilei Yuan5Qian Chen6Song Lin7Zhenyu Liu8Xiaohui Ren9Department of Neurosurgery Beijing Tiantan Hospital, Capital Medical University Beijing ChinaNational Genomics Data Center Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation Beijing ChinaDepartment of Neurosurgery Beijing Tiantan Hospital, Capital Medical University Beijing ChinaCAS Key Laboratory of Molecular Imaging Beijing Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences Beijing ChinaDepartment of Neurosurgery Beijing Tiantan Hospital, Capital Medical University Beijing ChinaDepartment of Nuclear Medicine Beijing Tiantan Hospital, Capital Medical University Beijing ChinaDepartment of Nuclear Medicine Beijing Tiantan Hospital, Capital Medical University Beijing ChinaDepartment of Neurosurgery Beijing Tiantan Hospital, Capital Medical University Beijing ChinaSchool of Artificial Intelligence, University of Chinese Academy of Sciences Beijing ChinaDepartment of Neurosurgery Beijing Tiantan Hospital, Capital Medical University Beijing ChinaAbstract Background The rarity of primary central nervous system lymphoma (PCNSL) and treatment heterogeneity contributes to a lack of prognostic models for evaluating posttreatment remission. This study aimed to develop and validate radiomic‐based models to predict the durable response (DR) to high‐dose methotrexate (HD‐MTX)‐based chemotherapy in PCNSL patients. Methods A total of 159 patients pathologically diagnosed with PCNSL between 2011 and 2021 across two institutions were enrolled. According to the NCCN guidelines, the DR was defined as the remission lasting ≥1 year after receiving HD‐MTX‐based chemotherapy. For each patient, a total of 1218 radiomic features were extracted from prebiopsy T1 contrast‐enhanced MR images. Multiple machine‐learning algorithms were utilized for feature selection and classification to build a radiomic signature. The radiomic‐clinical integrated models were developed using the random forest method. Model performance was externally validated to verify its clinical utility. Results A total of 105 PCNSL patients were enrolled after excluding 54 cases with ineligibility. The training and validation cohorts comprised 76 and 29 individuals, respectively. Among them, 65 patients achieved DR. The radiomic signature, consisting of 8 selected features, demonstrated strong predictive performance, with area under the curves of 0.994 in training cohort and 0.913 in validation cohort. This signature was independently associated with the DR in both cohorts. Both the radiomic signature and integrated models significantly outperformed the clinical models in two cohorts. Decision curve analysis underscored the clinical utility of the established models. Conclusions This radiomic signature and integrated models have the potential to accurately predict the DR to HD‐MTX‐based chemotherapy in PCNSL patients, providing valuable therapeutic insights.https://doi.org/10.1002/cam4.70182methotrexateprimary central nervous system lymphomaprognosisradiomicsresponse |
| spellingShingle | Haoyi Li Mingming Xiong Ming Li Caixia Sun Dao Zheng Leilei Yuan Qian Chen Song Lin Zhenyu Liu Xiaohui Ren Radiomic prediction for durable response to high‐dose methotrexate‐based chemotherapy in primary central nervous system lymphoma Cancer Medicine methotrexate primary central nervous system lymphoma prognosis radiomics response |
| title | Radiomic prediction for durable response to high‐dose methotrexate‐based chemotherapy in primary central nervous system lymphoma |
| title_full | Radiomic prediction for durable response to high‐dose methotrexate‐based chemotherapy in primary central nervous system lymphoma |
| title_fullStr | Radiomic prediction for durable response to high‐dose methotrexate‐based chemotherapy in primary central nervous system lymphoma |
| title_full_unstemmed | Radiomic prediction for durable response to high‐dose methotrexate‐based chemotherapy in primary central nervous system lymphoma |
| title_short | Radiomic prediction for durable response to high‐dose methotrexate‐based chemotherapy in primary central nervous system lymphoma |
| title_sort | radiomic prediction for durable response to high dose methotrexate based chemotherapy in primary central nervous system lymphoma |
| topic | methotrexate primary central nervous system lymphoma prognosis radiomics response |
| url | https://doi.org/10.1002/cam4.70182 |
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