Emanuela Parri

Background: This exploratory study assessed whether qualitative dynamic contrast-enhanced MRI (DCE-MRI) time–intensity curves (TICs) differentiate non-progressive treatment-related changes (NPTRC) from tumor progression after stereotactic radiotherapy for brain metastases. Methods: Thirty-two patients with 51 treated lesions underwent DCE-MRI. Five neuroradiologists independently delineated a volume of interest and classified the resulting TIC as Type A–D. Longitudinal clinical and MRI follow-up provided the reference outcome, which was recorded before the TIC framework was finalized. An A/B versus C/D grouping was evaluated as a secondary exploratory analysis. Results: Thirty-five lesions were classified as NPTRC and 16 as tumor progression. All 17 Type A lesions were NPTRC, whereas all 13 Type C/D lesions showed progression. Type B was observed in 21 of 51 lesions (41.2%), including 18 NPTRC and 3 progressive lesions, representing a substantial indeterminate subgroup. The exploratory A/B versus C/D grouping yielded 81.3% sensitivity and 100% specificity. The ordinal TIC analysis yielded an AUC of 0.952. Interobserver agreement was high within the present reader group (Fleiss’ κ = 0.889). Conclusions: Type A was associated with NPTRC, Types C/D were associated with progression, and Type B represented an indeterminate pattern. These descriptive findings require independent validation before clinical use.

Background: This exploratory study assessed whether qualitative dynamic contrast-enhanced MRI (DCE-MRI) time–intensity curves (TICs) differentiate non-progressive treatment-related changes (NPTRC) from tumor recurrence after stereotactic radiotherapy for brain metastases. Methods: Thirty-two patients with 51 treated lesions underwent DCE-MRI. Five neuroradiologists inde-pendently delineated a volume of interest and classified the resulting TIC as Type A–D. Longitudinal clinical and MRI follow-up provided the reference outcome, which was recorded before the TIC framework was finalized. An A/B versus C/D grouping was evaluated as a secondary exploratory analysis. Results: Thirty-five lesions were classified as NPTRC and 16 as tumor recurrence. All 17 Type A lesions were NPTRC, and all 13 Type C/D lesions showed recurrence. Type B occurred in both groups (18 NPTRC; 3 recurrences). The A/B versus C/D grouping yielded 81.3% sensitivity and 100% specificity. The ordinal TIC analysis yielded an area under the curve of 0.952. Interobserver agree-ment was high for the four TIC categories (Fleiss’ κ = 0.889). Conclusions: Type A was associated with NPTRC, Types C/D with recurrence, and Type B represented an indeterminate pattern. These descriptive findings require independent validation before clinical use.

The study was aimed to develop a radiomic model able to identify high-risk colon cancer by analyzing pre-operative CT scans. The study population comprised 148 patients: 108 with non-metastatic colon cancer were retrospectively enrolled from January 2015 to June 2020, and 40 patients were used as the external validation cohort. The population was divided into two groups—High-risk and No-risk—following the presence of at least one high-risk clinical factor. All patients had baseline CT scans, and 3D cancer segmentation was performed on the portal phase by two expert radiologists using open-source software (3DSlicer v4.10.2). Among the 107 radiomic features extracted, stable features were selected to evaluate the inter-class correlation (ICC) (cut-off ICC > 0.8). Stable features were compared between the two groups (T-test or Mann–Whitney), and the significant features were selected for univariate and multivariate logistic regression to build a predictive radiomic model. The radiomic model was then validated with an external cohort. In total, 58/108 were classified as High-risk and 50/108 as No-risk. A total of 35 radiomic features were stable (0.81 ≤ ICC < 0.92). Among these, 28 features were significantly different between the two groups (p < 0.05), and only 9 features were selected to build the radiomic model. The radiomic model yielded an AUC of 0.73 in the internal cohort and 0.75 in the external cohort. In conclusion, the radiomic model could be seen as a performant, non-invasive imaging tool to properly stratify colon cancers with high-risk disease.