Multi‐Institutional MR‐Derived Radiomics to Predict Post‐Exenteration Disease Recurrence in Patients With T4 Rectal Cancer
ABSTRACT Introduction Local recurrence and distant metastasis remain a concern in advanced rectal cancer, with up to 10% and 20%–30% of patients suffering local and distal progression, respectively. Radiomics refers to a novel technology that extracts and analyses quantitative imaging features from...
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Wiley
2025-02-01
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| Series: | Cancer Medicine |
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| Online Access: | https://doi.org/10.1002/cam4.70699 |
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| author | Niall J. O'Sullivan Fariba Tohidinezhad Hugo C. Temperley Mirac Ajredini Bedirye Koyuncu Sokmen Rumeysa Atabey Leyla Ozer Erman Aytac Alison Corr Alberto Traverso James F. Meaney Michael E. Kelly |
| author_facet | Niall J. O'Sullivan Fariba Tohidinezhad Hugo C. Temperley Mirac Ajredini Bedirye Koyuncu Sokmen Rumeysa Atabey Leyla Ozer Erman Aytac Alison Corr Alberto Traverso James F. Meaney Michael E. Kelly |
| author_sort | Niall J. O'Sullivan |
| collection | DOAJ |
| description | ABSTRACT Introduction Local recurrence and distant metastasis remain a concern in advanced rectal cancer, with up to 10% and 20%–30% of patients suffering local and distal progression, respectively. Radiomics refers to a novel technology that extracts and analyses quantitative imaging features from images, which can be subsequently used to develop and test clinical models predictive of outcomes. We aim to develop and test an MRI‐based radiomics nomogram predictive of disease recurrence in patients with T4 rectal cancer. Methods We conducted a multi‐institutional retrospective analysis of 55 patients with T4 rectal cancer treated with neoadjuvant chemoradiotherapy followed by exenterative surgery. Radiomic features were extracted from pre‐treatment T2‐weighted MRI scans and used to construct predictive models. The top‐performing radiomic signatures were identified, and internal validation with 1000 bootstrap samples was performed to calculate optimism‐corrected performance measures. Results Two radiomic signatures were identified as strong predictors of post‐operative disease recurrence. The best‐performing model achieved an optimism‐corrected AUC of 0.75, demonstrating good discriminative ability. Calibration plots showed a satisfactory fit of the predictions to the actual rates, and decision curve analyses confirmed the positive net benefit of the models. Conclusion The MRI‐based radiomics nomogram provides a promising tool for predicting disease recurrence in T4 rectal cancer patients post‐exenteration. This model could improve risk stratification and guide more personalized treatment strategies. Further studies with larger cohorts and external validation are needed to confirm these findings and enhance the model's generalizability. |
| format | Article |
| id | doaj-art-54d646f130d649bfaf1d0522cccb20db |
| institution | OA Journals |
| issn | 2045-7634 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Wiley |
| record_format | Article |
| series | Cancer Medicine |
| spelling | doaj-art-54d646f130d649bfaf1d0522cccb20db2025-08-20T02:13:02ZengWileyCancer Medicine2045-76342025-02-01144n/an/a10.1002/cam4.70699Multi‐Institutional MR‐Derived Radiomics to Predict Post‐Exenteration Disease Recurrence in Patients With T4 Rectal CancerNiall J. O'Sullivan0Fariba Tohidinezhad1Hugo C. Temperley2Mirac Ajredini3Bedirye Koyuncu Sokmen4Rumeysa Atabey5Leyla Ozer6Erman Aytac7Alison Corr8Alberto Traverso9James F. Meaney10Michael E. Kelly11Department of Radiology St. James's Hospital Dublin IrelandDepartment of Radiation Oncology (Maastro Clinic), School for Oncology and Reproduction (GROW) Maastricht University Medical Centre Maastricht the NetherlandsDepartment of Radiology St. James's Hospital Dublin IrelandAcibadem University Atakent Hospital Gastrointestinal Oncology Unit Istanbul TurkeyAcibadem University Atakent Hospital Gastrointestinal Oncology Unit Istanbul TurkeyAcibadem University Atakent Hospital Gastrointestinal Oncology Unit Istanbul TurkeyAcibadem University Atakent Hospital Gastrointestinal Oncology Unit Istanbul TurkeyAcibadem University Atakent Hospital Gastrointestinal Oncology Unit Istanbul TurkeyDepartment of Radiology St. James's Hospital Dublin IrelandSchool of Medicine Libera Università Vita‐Salute San Raffaele Milan ItalyDepartment of Radiology St. James's Hospital Dublin IrelandSchool of Medicine Trinity College Dublin Dublin IrelandABSTRACT Introduction Local recurrence and distant metastasis remain a concern in advanced rectal cancer, with up to 10% and 20%–30% of patients suffering local and distal progression, respectively. Radiomics refers to a novel technology that extracts and analyses quantitative imaging features from images, which can be subsequently used to develop and test clinical models predictive of outcomes. We aim to develop and test an MRI‐based radiomics nomogram predictive of disease recurrence in patients with T4 rectal cancer. Methods We conducted a multi‐institutional retrospective analysis of 55 patients with T4 rectal cancer treated with neoadjuvant chemoradiotherapy followed by exenterative surgery. Radiomic features were extracted from pre‐treatment T2‐weighted MRI scans and used to construct predictive models. The top‐performing radiomic signatures were identified, and internal validation with 1000 bootstrap samples was performed to calculate optimism‐corrected performance measures. Results Two radiomic signatures were identified as strong predictors of post‐operative disease recurrence. The best‐performing model achieved an optimism‐corrected AUC of 0.75, demonstrating good discriminative ability. Calibration plots showed a satisfactory fit of the predictions to the actual rates, and decision curve analyses confirmed the positive net benefit of the models. Conclusion The MRI‐based radiomics nomogram provides a promising tool for predicting disease recurrence in T4 rectal cancer patients post‐exenteration. This model could improve risk stratification and guide more personalized treatment strategies. Further studies with larger cohorts and external validation are needed to confirm these findings and enhance the model's generalizability.https://doi.org/10.1002/cam4.70699advanced rectal cancerMRIoncologyRadiomicsrecurrence |
| spellingShingle | Niall J. O'Sullivan Fariba Tohidinezhad Hugo C. Temperley Mirac Ajredini Bedirye Koyuncu Sokmen Rumeysa Atabey Leyla Ozer Erman Aytac Alison Corr Alberto Traverso James F. Meaney Michael E. Kelly Multi‐Institutional MR‐Derived Radiomics to Predict Post‐Exenteration Disease Recurrence in Patients With T4 Rectal Cancer Cancer Medicine advanced rectal cancer MRI oncology Radiomics recurrence |
| title | Multi‐Institutional MR‐Derived Radiomics to Predict Post‐Exenteration Disease Recurrence in Patients With T4 Rectal Cancer |
| title_full | Multi‐Institutional MR‐Derived Radiomics to Predict Post‐Exenteration Disease Recurrence in Patients With T4 Rectal Cancer |
| title_fullStr | Multi‐Institutional MR‐Derived Radiomics to Predict Post‐Exenteration Disease Recurrence in Patients With T4 Rectal Cancer |
| title_full_unstemmed | Multi‐Institutional MR‐Derived Radiomics to Predict Post‐Exenteration Disease Recurrence in Patients With T4 Rectal Cancer |
| title_short | Multi‐Institutional MR‐Derived Radiomics to Predict Post‐Exenteration Disease Recurrence in Patients With T4 Rectal Cancer |
| title_sort | multi institutional mr derived radiomics to predict post exenteration disease recurrence in patients with t4 rectal cancer |
| topic | advanced rectal cancer MRI oncology Radiomics recurrence |
| url | https://doi.org/10.1002/cam4.70699 |
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