Structural Magnetic Resonance Imaging (MRI) in Differentiating Radionecrosis from Metastatic Recurrence in Post-Surgical Patients: A Tertiary Center Experience
6 affiliations
- 1Department of Neuroradiology, Fondazione IRCCS San Gerardo dei Tintori, Via Pergolesi 33, 20900 Monza, Italy
- 2School of Medicine, University of Milano-Bicocca, Piazza dell’Ateneo Nuovo 1, 20126 Milano, Italy
- 3Neuroradiology Section, Department of Radiology, Diagnostic Image Center, Hospital Clinic of Barcelona, University of Barcelona, 08036 Barcelona, Spain
- 4Fundació de Recerca Clínic Barcelona, Institut d’Investigacions Biomèdiques August Pi I Sunyer, 08036 Barcelona, Spain
- 5Department of Radiation Oncology, Hospital Clínic de Barcelona, University of Barcelona, 08036 Barcelona, Spain
- 6Department of Pathology, Biomedical Diagnostic Center (CDB), Hospital Clínic de Barcelona, University of Barcelona, 08036 Barcelona, Spain
Abstract
Purpose: Distinguishing radionecrosis (RN) from recurrent brain metastasis (rM1) remains a major challenge in the follow-up of patients treated with surgery and radiotherapy. Although advanced imaging techniques have been proposed to improve diagnostic accuracy, they are not always available in routine clinical practice and may be limited by postoperative changes and artifacts. Histopathologically validated studies in this setting remain scarce. Methods: We retrospectively evaluated 16 patients with 17 histologically confirmed lesions who underwent repeat surgery for suspected progression after the resection of brain metastases and adjuvant radiotherapy. Preoperative MRI examinations were reviewed by two neuroradiologists using a standard MRI protocol. Qualitative features included signal characteristics, T1/T2 mismatch, enhancement patterns, and the presence of the “inflammatory cloud” and “incomplete ring enhancement” signs. Quantitative analysis included lesion, edema and necrosis volumes, apparent diffusion coefficient (ADC), intratumoral susceptibility signal (ITSS) grading, and volumetric lesion quotient (vLQ). A LASSO logistic regression model with leave-one-out cross-validation and bootstrap stability analysis was used to identify the most relevant predictors. Results: Histopathology demonstrated 11 RN lesions and six recurrent metastases. The LASSO model achieved an area under the curve of 0.71 and an overall accuracy of 76% (13/17 lesions) with a sensitivity of 82% for RN and a specificity of 67% for rM1. RN lesions were characterized by significantly higher ADC values and ITSS grades and by the exclusive presence of the inflammatory cloud and incomplete ring enhancement signs. Stability analysis identified incomplete ring enhancement (49%), ITSS grade (45%), ADC values (45%), and inflammatory cloud (44%) as the most robust predictors of RN, whereas volumetric measurements showed limited independent predictive value. Conclusions: In patients with histologically confirmed post-treatment lesions, conventional MRI provided several reproducible features that aided differentiation between radionecrosis and recurrent brain metastasis. The inflammatory cloud, incomplete ring enhancement, elevated ADC values, and higher ITSS grades emerged as the most robust imaging markers of RN. While predictive multivariable models remain exploratory, these readily available MRI features may improve diagnostic confidence in routine clinical practice and warrant validation in larger multicenter cohorts.
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BibTeX
@article{bino2026structural,
title = {{Structural Magnetic Resonance Imaging (MRI) in Differentiating Radionecrosis from Metastatic Recurrence in Post-Surgical Patients: A Tertiary Center Experience}},
author = {Flavia Bino and Camilo Pineda and José Pariente and Solanche Jasmin Santillan-Zorrilla and Juan Andrés Mora and Izaskun Valduvieco Ruiz and Yensa Rodríguez and Iban Aldecoa and Guillem Martín Vidal and Albert Calvo Porcel and Núria Bargalló and Sofía González-Ortiz},
journal = {Cancers},
year = {2026},
month = oct,
doi = {10.3390/cancers18213439},
url = {https://www.mdpi.com/2072-6694/18/21/3439},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Structural Magnetic Resonance Imaging (MRI) in Differentiating Radionecrosis from Metastatic Recurrence in Post-Surgical Patients: A Tertiary Center Experience
AU - Flavia Bino
AU - Camilo Pineda
AU - José Pariente
AU - Solanche Jasmin Santillan-Zorrilla
AU - Juan Andrés Mora
AU - Izaskun Valduvieco Ruiz
AU - Yensa Rodríguez
AU - Iban Aldecoa
AU - Guillem Martín Vidal
AU - Albert Calvo Porcel
AU - Núria Bargalló
AU - Sofía González-Ortiz
PY - 2026
DA - 2026/10/24
JO - Cancers
DO - 10.3390/cancers18213439
UR - https://www.mdpi.com/2072-6694/18/21/3439
AB - Purpose: Distinguishing radionecrosis (RN) from recurrent brain metastasis (rM1) remains a major challenge in the follow-up of patients treated with surgery and radiotherapy. Although advanced imaging techniques have been proposed to improve diagnostic accuracy, they are not always available in routine clinical practice and may be limited by postoperative changes and artifacts. Histopathologically validated studies in this setting remain scarce. Methods: We retrospectively evaluated 16 patients with 17 histologically confirmed lesions who underwent repeat surgery for suspected progression after the resection of brain metastases and adjuvant radiotherapy. Preoperative MRI examinations were reviewed by two neuroradiologists using a standard MRI protocol. Qualitative features included signal characteristics, T1/T2 mismatch, enhancement patterns, and the presence of the “inflammatory cloud” and “incomplete ring enhancement” signs. Quantitative analysis included lesion, edema and necrosis volumes, apparent diffusion coefficient (ADC), intratumoral susceptibility signal (ITSS) grading, and volumetric lesion quotient (vLQ). A LASSO logistic regression model with leave-one-out cross-validation and bootstrap stability analysis was used to identify the most relevant predictors. Results: Histopathology demonstrated 11 RN lesions and six recurrent metastases. The LASSO model achieved an area under the curve of 0.71 and an overall accuracy of 76% (13/17 lesions) with a sensitivity of 82% for RN and a specificity of 67% for rM1. RN lesions were characterized by significantly higher ADC values and ITSS grades and by the exclusive presence of the inflammatory cloud and incomplete ring enhancement signs. Stability analysis identified incomplete ring enhancement (49%), ITSS grade (45%), ADC values (45%), and inflammatory cloud (44%) as the most robust predictors of RN, whereas volumetric measurements showed limited independent predictive value. Conclusions: In patients with histologically confirmed post-treatment lesions, conventional MRI provided several reproducible features that aided differentiation between radionecrosis and recurrent brain metastasis. The inflammatory cloud, incomplete ring enhancement, elevated ADC values, and higher ITSS grades emerged as the most robust imaging markers of RN. While predictive multivariable models remain exploratory, these readily available MRI features may improve diagnostic confidence in routine clinical practice and warrant validation in larger multicenter cohorts.
SN - 2072-6694
ER -