Purpose: We hypothesized that online adaptive radiation therapy (oART) could eliminate errors associated with interfractional deformation in gastric mucosa-associated lymphoid tissue (MALT) lymphoma, but errors in intrafractional deformation remained in 6 directions (anterior, posterior, superior, inferior, left, and right). This study aimed to quantify the anisotropic deformation errors of the clinical target volume (CTV) for MALT lymphoma using oART to determine deformations in the planning target volume (PTV) margins. Methods and Materials: Thirty fractional scans from 4 consecutive patients (a total of 120 cone beam computed tomography scans) with gastric MALT lymphoma treated with oART were chosen for this retrospective study. The CTV contours were manually delineated on the pretreatment and posttreatment cone beam computed tomography images. The center-of-mass matching of the CTVs was performed following the bone anatomy matching. Systematic and random errors of intrafractional deformations of the CTV were quantified using displacement vectors between the pretreatment and posttreatment CTV surfaces for each fraction. The PTV margins for oART were anisotropically calculated using the van Herk formula: 2.5S+ 0.7s, accounting for intrafractional errors. Results: For intrafractional deformation, the means of standard deviations of systematic errors ranged from 1.2 mm to 2.2 mm, whereas those of random errors ranged from 2.9 mm to 3.6 mm. The PTV margins were up to 13.1 mm in the inferior direction, whereas in other directions, they ranged from 9.7 mm to 12.8 mm. The PTV margin in integer achieved posttreatment CTV coverage for 90% of the fractions, with undercoverage volumes remaining below 0.6 cm3 in all fractions. Conclusions: This study suggests that the impact of intrafractional CTV deformation can not be eliminated even with oART. This highlights the need to set the appropriate anisotropic PTV margins.
(c) 2025 The Author(s). Published by Elsevier Inc. on behalf of American Society for Radiation Oncology. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

We hypothesized that persistent lifetime (PLT) images could represent tumor imaging traits, locations, and persistent contrasts of topological components (connected and hole components) corresponding to gene mutations such as epidermal growth factor receptor (EGFR) mutant signs. We aimed to develop a topological radiogenomic approach using PLT images to identify EGFR mutation-positive patients with non-small cell lung cancer (NSCLC). The PLT image was newly proposed to visualize the locations and persistent contrasts of the topological components for a sequence of binary images with consecutive thresholding of an original computed tomography (CT) image. This study employed 226 NSCLC patients (94 mutant and 132 wildtype patients) with pretreatment contrast-enhanced CT images obtained from four datasets from different countries for training and testing prediction models. Two-dimensional (2D) and three-dimensional (3D) PLT images were assumed to characterize specific imaging traits (e.g., air bronchogram sign, cavitation, and ground glass nodule) of EGFR-mutant tumors. Seven types of machine learning classification models were constructed to predict EGFR mutations with significant features selected from 2D-PLT, 3D-PLT, and conventional radiogenomic features. Among the means and standard deviations of the test areas under the receiver operating characteristic curves (AUCs) of all radiogenomic approaches in a four-fold cross-validation test, the 2D-PLT features showed the highest AUC with the lowest standard deviation of 0.927 ± 0.08. The best radiogenomic approaches with the highest AUC were the random forest model trained with the Betti number (BN) map features (AUC = 0.984) in the internal test and the adapting boosting model trained with the BN map features (AUC = 0.717) in the external test. PLT features can be used as radiogenomic imaging biomarkers for the identification of EGFR mutation status in patients with NSCLC. © 2024

OBJECTIVE: This study aimed to develop an explainable radiomics model for stratifying prostate cancer (PCa) patients with high-risk disease via investigation of the association between cell density (CD) in the PCa region on histopathological images and multiparametric MR (mpMR) radiomics features.; MATERIALS AND METHODS: 137,970 radiomic features were calculated from mpMR images (101 PCa regions of 44 patients), and joint histograms (JHs) were derived from dynamic contrast-enhanced (DCE) images for each PCa region. The association between CD on histopathological images and its corresponding mpMR radiomic features in PCa regions for various grade groups and the three risk groups was evaluated using Spearman's correlation coefficient. To validate the potential of the radiomic-feature-CD association, we developed the radiomics model for stratifying patients into low/intermediate-risk and high-risk groups.; RESULTS: There were moderate correlations of the CD with a DCE-based texture feature (WV_HH_1st_GLSZM_ZP) (rho=0.609, p=0.024) and DCE-JH feature (JH_WV_HL_1st versus 5th-1st_Hist_STD) (rho=0.609, p=0.024) in the high-risk group. The radiomics model had an accuracy of 0.920 for stratifying the patients of a test dataset into the low/intermediate-risk and high-risk groups.; CONCLUSION: The association between CD and mpMR features can be leveraged to develop the explainable radiomics for the high-risk stratification of patients with PCa. © 2025. The Author(s), under exclusive licence to European Society for Magnetic Resonance in Medicine and Biology (ESMRMB).