Radiology. Imaging cancer最新文献

Purpose To determine the influence of location, extent of tissue invasion, and tumor morphology at MRI on the resectability of locally recurrent rectal cancer (LRRC) and postresection oncologic outcomes of LRRC. Materials and Methods This retrospective observational study included consecutive patients diagnosed with LRRC who underwent surgery with curative intent at the Catharina Hospital Eindhoven and Karolinska University Hospital Stockholm between January 2003 and December 2017. Two expert radiologists reviewed available MR images while adhering to a standardized reviewing checklist. The effect of pelvic structure involvement, tumor morphology on the primary outcome of resection margin status, and secondary outcomes of overall survival and disease-free survival were assessed using univariable and multivariable logistic regression and Cox proportional hazard analyses. Results The final analysis included 328 patients with LRRC (mean age ± SD, 64.9 years ± 9.6; 126 female, 202 male). Resection margins were negative in 217 (66.2%) patients and positive in 111 patients (33.8%). Tumor size, tumor type, and border type on MR images were all associated with resectability. Central recurrences were associated with the lowest likelihood of positive resection margins (odds ratio [OR], 0.45; 95% CI: 0.28, 0.71; P < .001), whereas lateral recurrences were associated with the highest likelihood (OR, 2.00; 95% CI: 1.25, 3.19: P = .004). Similarly, central recurrences were associated with better disease-free survival compared with lateral recurrences (hazard ratio [HR], 0.69; 95% CI: 0.53, 0.90; P = .006 vs HR, 1.49; 95% CI: 1.14, 1.94; P = .003, respectively). Similar findings were observed after correcting for resection margin status. Conclusion Standardized MRI assessment of tumor characteristics in patients with LRRC resulted in the identification of specific prognostic factors. Central compartment involvement and well-defined tumors were associated with improved prognosis, whereas lateral compartment involvement and fibrotic spiculated tumors were associated with a worse prognosis after surgical resection. Keywords: Rectum, MR-Imaging, Abdomen/GI, Oncology, Surgery, Locally Recurrent Rectal Cancer, Tumor Biology Supplemental material is available for this article. © RSNA, 2025.

Purpose To compare the performance of volumetric radiomic parenchymal pattern analysis from three-dimensional (3D) digital breast tomosynthesis (DBT) images with that of two-dimensional (2D) digital mammography (DM) and 2D sections from DBT in assessing breast cancer risk relative to breast density measurements. Materials and Methods This was a retrospective matched case-control study among individuals who underwent concurrent DM and DBT screening from March 2011 through December 2014. The Cancer Phenomics Toolkit was used to calculate radiomic features from craniocaudal and mediolateral oblique views in all study patients, matched on race and age, for various experimental settings, including image resolution and window size. For each image type, conditional logistic regression evaluated the association of radiomic features, along with age, body mass index (BMI), and area percent density (PD) (from the Laboratory for Individualized Breast Radiodensity Assessment software), with breast cancer, using the C statistic as the measure of model predictive ability. Model fit was compared via likelihood ratio tests. Results The study included 924 female patients (median age, 61 years [IQR: 51-69 years]), with 187 cases and 737 controls. Volumetric features from 3D reconstructed DBT scans had, on average, higher C statistics across all experimental conditions. Among models using only radiomic features, C statistics were highest for models using features from 3D images (mean C statistic: 0.68, P < .001); models using features from 2D image types resulted in lower mean C statistics (0.60 to 0.65). A baseline model using age, BMI, and area PD had a C statistic of 0.60. The effect of higher image resolution and smaller window size were not substantial, supporting the use of less computationally intensive processing. Conclusion Fully automated 3D parenchymal analysis from DBT improved breast cancer risk estimation beyond markers derived from area breast density and 2D images. Keywords: Mammography, Tomosynthesis, Breast, Volume Analysis Supplemental material is available for this article. © RSNA, 2025.

Purpose To evaluate the performance of a machine learning model developed using radiomics data derived from physiologically decomposed diffusion-weighted MRI data for predicting pathologic complete response (pCR) following neoadjuvant chemotherapy for breast cancer compared with baseline and benchmark models. Materials and Methods This retrospective study included data from the Breast Multiparametric MRI for prediction of neoadjuvant chemotherapy Response (BMMR2) challenge dataset, comprising longitudinal multiparametric breast MRI studies (diffusion-weighted imaging [DWI] and dynamic contrast-enhanced MRI) from participants enrolled in the I-SPY 2/ACRIN 6698 trial (ClinicalTrials.gov: NCT01042379). Piecewise linear physiologic decomposition was applied to DWI data (PD DWI) to isolate pseudo-diffusion, pure-diffusion, and pseudo-diffusion fraction components for radiomics feature extraction. These features were used to develop a boosted decision tree model to predict pCR following neoadjuvant chemotherapy. Model performance was compared with performance of baseline models, including data on tumor size and mean apparent diffusion coefficient, and the BMMR2 challenge benchmark model using area under the receiver operating characteristic curve, F1 score, and positive and negative predictive values. Model calibration was assessed via the Brier score, and a decision curve analysis was performed to estimate the potential reduction in unnecessary interventions when using the proposed model. Results The study included multiparametric MRI scans from 190 female participants (mean age ± SD, 48.4 years ± 10.5). PD DWI achieved the highest area under the receiver operating characteristic curve (0.89, 95% CI: 0.81, 0.96) among all evaluated models, demonstrating statistically significant improvements over baseline approaches (all P < .04). Decision curve analysis showed that the PD DWI model provided a greater net benefit compared with the BMMR2 challenge benchmark model (0.17, 95% CI: 0.13, 0.21 vs 0.09, 95% CI: 0.05, 0.13; P < .001). Conclusion A machine learning model using radiomics data derived from PD DWI achieved higher performance than baseline and benchmark models in predicting pCR following neoadjuvant chemotherapy for breast cancer. Keywords: Image Postprocessing, MR-Diffusion Weighted Imaging, Breast, Tumor Response, Experimental Investigations ClinicalTrials.gov: NCT01042379 © RSNA, 2025.