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Introduction: Whole-body MRI (WB-MRI) is increasingly used in clinical practice for detection of malignant bone disease. A high relative contrast ratio (RCR) of malignant bone lesions compared with normal bone can improve disease detection in breast, prostate and myeloma malignancies. However, the RCR of malignant bone lesions on T1w, DWI and relative Fat Fraction (rFF) maps derived from Dixon T1w have not been compared., Methods: 110 baseline WB-MRI of patients with suspected malignant bone lesions were reviewed retrospectively. On each scan, up to four active bone lesions were identified, one each at the cervicothoracic spine, lumbosacral spine, pelvis and extremity, and their ROI signal intensity measured on rFF, T1w and DWI b = 900. The signal intensity of background bone was measured by placing an ROI on the nearest normal-appearing bone to each lesion, for each sequence. The mean lesion signal-to-background ratio (taken as RCR) for each lesion was calculated. We compared the RCR of bone lesions on rFF, T1w and DWI (Mann-Whitney test)., Results: The median rFF RCR of malignant bone lesions was highest compared with normal bone marrow than that of T1w (p < 0.0001) and DWI (p < 0.0001). There was no significant difference in the median rFF RCR of malignant bone lesions from breast cancer, myeloma and prostate cancer (p > 0.017, Bonferroni correction) or according to their anatomical locations (p > 0.012, Bonferroni correction)., Conclusions: Malignant bone lesion RCR measured by lesion/background signal intensity was higher on rFF than on T1w and DWI b = 900 in patients with prostate, breast and myeloma malignancies, indicating its value for disease detection., (Copyright © 2025. Published by Elsevier Inc.)

Aim: To assess the diagnostic accuracy and inter-reader agreement of a simulated abbreviated gadoxetate liver magnetic resonance imaging (MRI) protocol together with contrast-enhanced computed tomography (CE-CT) against a standard gadoxetate MRI for the detection of colorectal liver metastases at baseline., Materials and Methods: Three readers independently evaluated two sets of images per patient, recording number and location of metastases and benign lesions. Set 1 comprised T1w, T2w, DWI, multiphase CE-T1w, and hepatobiliary phase (HBP) images (standard). Set 2 included T2w, HBP, DWI (from Set 1) and CE-CT (simulated abbreviated). Diagnostic performance was compared using McNemar's test. The level of agreement between sets 1 and 2 was determined with Cohen kappa. For agreement in the number of benign lesions and metastases, we applied intraclass correlation coefficient (ICC)., Results: Seventy-five patients (245 metastases, 122 benign lesions) were evaluated. There was no significant difference in diagnostic accuracy between set 1 and 2 for each reader (mean P = 0.74). The total number of metastases and benign lesions showed high agreement between reading set 1 and 2 (κ = 0.81, 0.78). The total number of metastases showed substantial agreement between readers for set 1 and 2 (ICC = 0.99, 0.99). Good agreement was seen for metastatic segmental involvement (κ = 0.84-0.99)., Conclusion: At baseline, using a simulated abbreviated liver MRI together with CE-CT showed excellent agreement with standard MRI protocol for liver metastasis detection., Competing Interests: Conflict of interest The authors declare no conflict of interest., (Copyright © 2024 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.)

Background: Microsatellite instability (MSI) status is a strong predictor of response to immunotherapy of colorectal cancer. Radiogenomic approaches promise the ability to gain insight into the underlying tumor biology using non-invasive routine clinical images. This study investigates the association between tumor morphology and the status of MSI versus microsatellite stability (MSS), validating a novel radiomic signature on an external multicenter cohort., Methods: Preoperative computed tomography scans with matched MSI status were retrospectively collected for 243 colorectal cancer patients from three hospitals: Seoul National University Hospital (SNUH); Netherlands Cancer Institute (NKI); and Fondazione IRCCS Istituto Nazionale dei Tumori, Milan Italy (INT). Radiologists delineated primary tumors in each scan, from which radiomic features were extracted. Machine learning models trained on SNUH data to identify MSI tumors underwent external validation using NKI and INT images. Performances were compared in terms of area under the receiving operating curve (AUROC)., Results: We identified a radiomic signature comprising seven radiomic features that were predictive of tumors with MSS or MSI (AUROC 0.69, 95% confidence interval [CI] 0.54-0.84, p = 0.018). Integrating radiomic and clinical data into an algorithm improved predictive performance to an AUROC of 0.78 (95% CI 0.60-0.91, p = 0.002) and enhanced the reliability of the predictions., Conclusion: Differences in the radiomic morphological phenotype between tumors MSS or MSI could be detected using radiogenomic approaches. Future research involving large-scale multicenter prospective studies that combine various diagnostic data is necessary to refine and validate more robust, potentially tumor-agnostic MSI radiogenomic models., Relevance Statement: Noninvasive radiomic signatures derived from computed tomography scans can predict MSI in colorectal cancer, potentially augmenting traditional biopsy-based methods and enhancing personalized treatment strategies., Key Points: Noninvasive CT-based radiomics predicted MSI in colorectal cancer, enhancing stratification. A seven-feature radiomic signature differentiated tumors with MSI from those with MSS in multicenter cohorts. Integrating radiomic and clinical data improved the algorithm's predictive performance., (© 2024. The Author(s).)

Purpose: Pleural plaques (PPs) are morphologic manifestations of long-term asbestos exposure. The relationship between PP and lung function is not well understood, whereas the time-consuming nature of PP delineation to obtain volume impedes research. To automate the laborious task of delineation, we aimed to develop automatic artificial intelligence (AI)-driven segmentation of PP. Moreover, we aimed to explore the relationship between pleural plaque volume (PPV) and pulmonary function tests., Materials and Methods: Radiologists manually delineated PPs retrospectively in computed tomography (CT) images of patients with occupational exposure to asbestos (May 2014 to November 2019). We trained an AI model with a no-new-UNet architecture. The Dice Similarity Coefficient quantified the overlap between AI and radiologists. The Spearman correlation coefficient ( r ) was used for the correlation between PPV and pulmonary function test metrics. When recorded, these were vital capacity (VC), forced vital capacity (FVC), and diffusing capacity for carbon monoxide (DLCO)., Results: We trained the AI system on 422 CT scans in 5 folds, each time with a different fold (n = 84 to 85) as a test set. On these independent test sets combined, the correlation between the predicted volumes and the ground truth was r = 0.90, and the median overlap was 0.71 Dice Similarity Coefficient. We found weak to moderate correlations with PPV for VC (n = 80, r = -0.40) and FVC (n = 82, r = -0.38), but no correlation for DLCO (n = 84, r = -0.09). When the cohort was split on the median PPV, we observed statistically significantly lower VC ( P = 0.001) and FVC ( P = 0.04) values for the higher PPV patients, but not for DLCO ( P = 0.19)., Conclusion: We successfully developed an AI algorithm to automatically segment PP in CT images to enable fast volume extraction. Moreover, we have observed that PPV is associated with loss in VC and FVC., Competing Interests: J.A.B. is on the advisory board of Roche International (payment to institution) and received financial support and free drugs for an investigator-initiated study by MSD. The remaining authors declare no conflict of interest., (Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc.)

Purpose: To evaluate the potential of synthetic radiomic data generation in addressing data scarcity in radiomics/radiogenomics models., Methods: This study was conducted on a retrospectively collected cohort of 386 colorectal cancer patients (n = 2570 lesions) for whom matched contrast-enhanced CT images and gene TP53 mutational status were available. The full cohort data was divided into a training cohort (n = 2055 lesions) and an independent and fixed test set (n = 515 lesions). Differently sized training sets were subsampled from the training cohort to measure the impact of sample size on model performance and assess the added value of synthetic radiomic augmentation at different sizes. Five different tabular synthetic data generation models were used to generate synthetic radiomic data based on "real-world" radiomics data extracted from this cohort. The quality and reproducibility of the generated synthetic radiomic data were assessed. Synthetic radiomics were then combined with "real-world" radiomic training data to evaluate their impact on the predictive model's performance., Results: A prediction model was generated using only "real-world" radiomic data, revealing the impact of data scarcity in this particular data set through a lack of predictive performance at low training sample numbers (n = 200, 400, 1000 lesions with average AUC = 0.52, 0.53, and 0.56 respectively, compared to 0.64 when using 2055 training lesions). Synthetic tabular data generation models created reproducible synthetic radiomic data with properties highly similar to "real-world" data (for n = 1000 lesions, average Chi-square = 0.932, average basic statistical correlation = 0.844). The integration of synthetic radiomic data consistently enhanced the performance of predictive models trained with small sample size sets (AUC enhanced by 9.6%, 11.3%, and 16.7% for models trained on n&#95;samples = 200, 400, and 1000 lesions, respectively). In contrast, synthetic data generated from randomised/noisy radiomic data failed to enhance predictive performance underlining the requirement of true signal data to do so., Conclusion: Synthetic radiomic data, when combined with real radiomics, could enhance the performance of predictive models. Tabular synthetic data generation might help to overcome limitations in medical AI stemming from data scarcity., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

As the management of gastrointestinal malignancy has evolved, tumor response assessment has expanded from size-based assessments to those that include tumor enhancement, in addition to functional data such as those derived from PET and diffusion-weighted imaging. Accurate interpretation of tumor response therefore requires knowledge of imaging modalities used in gastrointestinal malignancy, anticancer therapies, and tumor biology. Targeted therapies such as immunotherapy pose additional considerations due to unique imaging response patterns and drug toxicity; as a consequence, immunotherapy response criteria have been developed. Some gastrointestinal malignancies require assessment with tumor-specific criteria when assessing response, often to guide clinical management (such as watchful waiting in rectal cancer or suitability for surgery in pancreatic cancer). Moreover, anatomic measurements can underestimate therapeutic response when applied to molecular-targeted therapies or locoregional therapies in hypervascular malignancies such as hepatocellular carcinoma. In these cases, responding tumors may exhibit morphologic changes including cystic degeneration, necrosis, and hemorrhage, often without significant reduction in size. Awareness of pitfalls when interpreting gastrointestinal tumor response is required to correctly interpret response assessment imaging and guide appropriate oncologic management. Data-driven image analyses such as radiomics have been investigated in a variety of gastrointestinal tumors, such as identifying those more likely to respond to therapy or recur, with the aim of delivering precision medicine. Multimedia-enhanced radiology reports can facilitate communication of gastrointestinal tumor response by automatically embedding response categories, key data, and representative images. © RSNA, 2024 Test Your Knowledge questions for this article are available in the supplemental material.

Objectives: Diffusion-weighted imaging (DWI) with simultaneous multi-slice (SMS) acquisition and advanced processing can accelerate acquisition time and improve MR image quality. This study evaluated the image quality and apparent diffusion coefficient (ADC) measurements of free-breathing DWI acquired from patients with liver metastases using a prototype SMS-DWI acquisition (with/without an advanced processing option) and conventional DWI., Methods: Four DWI schemes were compared in a pilot 5-patient cohort; three DWI schemes were further assessed in a 24-patient cohort. Two readers scored image quality of all b-value images and ADC maps across the three methods. ADC measurements were performed, for all three methods, in left and right liver parenchyma, spleen, and liver metastases. The Friedman non-parametric test (post-hoc Wilcoxon test with Bonferroni correction) was used to compare image quality scoring; t-test was used for ADC comparisons., Results: SMS-DWI was faster (by 24%) than conventional DWI. Both readers scored the SMS-DWI with advanced processing as having the best image quality for highest b-value images (b750) and ADC maps; Cohen's kappa inter-reader agreement was 0.6 for b750 image and 0.56 for ADC maps. The prototype SMS-DWI sequence with advanced processing allowed a better visualization of the left lobe of the liver. ADC measured in liver parenchyma, spleen, and liver metastases using the SMS-DWI with advanced processing option showed lower values than those derived from the SMS-DWI method alone (t-test, p < 0.0001; p < 0.0001; p = 0.002)., Conclusions: Free-breathing SMS-DWI with advanced processing was faster and demonstrated better image quality versus a conventional DWI protocol in liver patients., Clinical Relevance Statement: Free-breathing simultaneous multi-slice- diffusion-weighted imaging (DWI) with advanced processing was faster and demonstrated better image quality versus a conventional DWI protocol in liver patients., Key Points: • Diffusion-weighted imaging (DWI) with simultaneous multi-slice (SMS) can accelerate acquisition time and improve image quality. • Apparent diffusion coefficients (ADC) measured in liver parenchyma, spleen, and liver metastases using the simultaneous multi-slice DWI with advanced processing were significantly lower than those derived from the simultaneous multi-slice DWI method alone. • Simultaneous multi-slice DWI sequence with inline advanced processing was faster and demonstrated better image quality in liver patients., (© 2023. The Author(s).)

Purpose: To evaluate intra-patient and interobserver agreement in patients who underwent liver MRI with gadoxetic acid using two different multi-arterial phase (AP) techniques., Methods: A total of 154 prospectively enrolled patients underwent clinical gadoxetic acid-enhanced liver MRI twice within 12 months, using two different multi-arterial algorithms: CAIPIRINHA-VIBE and TWIST-VIBE. For every patient, breath-holding time, body mass index, sex, age were recorded. The phase without contrast media and the APs were independently evaluated by two radiologists who quantified Gibbs artefacts, noise, respiratory motion artefacts, and general image quality. Presence or absence of Gibbs artefacts and noise was compared by the McNemar's test. Respiratory motion artefacts and image quality scores were compared using Wilcoxon signed rank test. Interobserver agreement was assessed by Cohen kappa statistics., Results: Compared with TWIST-VIBE, CAIPIRINHA-VIBE images had better scores for every parameter except higher noise score. Triple APs were always acquired with TWIST-VIBE but failed in 37% using CAIPIRINHA-VIBE: 11% have only one AP, 26% have two. Breath-holding time was the only parameter that influenced the success of multi-arterial techniques. TWIST-VIBE images had worst score for Gibbs and respiratory motion artefacts but lower noise score., Conclusion: CAIPIRINHA-VIBE images were always diagnostic, but with a failure of triple-AP in 37%. TWIST-VIBE was successful in obtaining three APs in all patients. Breath-holding time is the only parameter which can influence the preliminary choice between CAIPIRINHA-VIBE and TWIST-VIBE algorithm., Advances in Knowledge: If the patient is expected to perform good breath-holds, TWIST-VIBE is preferable; otherwise, CAIPIRINHA-VIBE is more appropriate., (© The Author(s) 2024. Published by Oxford University Press on behalf of the British Institute of Radiology. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Objectives: To compare relative fat fraction (rFF) of active bone lesions from breast, prostate and myeloma malignancies and normal bone marrow; to assess its inter-reader agreement., Methods: Patients with breast ( n = 26), myeloma ( n = 32) and prostate cancer ( n = 52) were retrospectively evaluated. 110 baseline rFF maps from whole-body MRI were reviewed by two radiologists. Regions of interest for up to four focal active lesions in each patient were drawn on rFF maps, one each at the cervicothoracic spine, lumbosacral spine, pelvis and extremity. The mean and standard deviation of rFF were recorded. The rFF of normal marrow was measured in the pelvis for patients without diffuse bone disease ( n = 88). We compared the rFF of malignant bone lesions and normal marrow using Mann-Whitney test. Interobserver agreement was assessed by interclass correlation coefficient., Results: Malignant bone lesions showed significantly lower median rFF (13.87%) compared with normal marrow (89.76%) with little overlap ( p < 0.0001). There was no significant difference in the median rFF of malignant lesions from breast (14.46%), myeloma (13.12%) and prostate cancer (13.67%) ( p > 0.017, Bonferroni correction) and in the median rFF of bone disease according to their anatomical locations ( p > 0.008, Bonferroni correction). There was excellent interobserver agreement (0.95)., Conclusion: The low rFF of active bone lesions in breast, prostate and myeloma malignancies provides high image contrast relative to normal marrow that may be used to detect bone metastases., Advances in Knowledge: This study shows the importance of rFF towards detecting bone metastases.

Background: Neoadjuvant treatment with either chemotherapy or immunotherapy is gaining momentum in colon cancers (CC). To reduce over-treatment, increasing staging accuracy using computed tomography (CT) is of high importance., Purpose: To assess and compare CT imaging features of CC between mismatch repair-proficient (pMMR) and MMR-deficient (dMMR) tumours and identify CT features that can distinguish high-risk (pT3-4, N+) CC according to MMR status., Methods: Primary staging CTs of 266 patients who underwent primary surgical resection of a colon tumour were retrospectively and independently evaluated by two radiologists. Logistic regression analysis was performed to identify significant associations between imaging features and positive lymph node status. Receiver operating characteristic (ROC) curves of significantly associated features were assessed and validated in an external cohort of 104 patients., Results: Among pT3 tumours only, dMMR CC were significantly larger than pMMR CC in both length and thickness (length 59.39 ± 26.28 mm versus 48.70 ± 23.72, respectively, p = 0.031; thickness 20.54 mm ± 11.17 versus 16.34 ± 8.73, respectively, p = 0.027). For pMMR tumours, nodal internal heterogeneity on CT was significantly associated with a positive lymph node status (odds ratio (OR) = 2.66, p = 0.027), while for dMMR tumours, the largest short diameter of the nodes was associated with lymph node status (OR = 2.01, p = 0.049). The best cut-off value of the largest short diameter of involved nodes was 10.4 mm for dMMR and 7.95 mm for pMMR. In the external validation cohort, AUCs for predicting involved nodes based on the largest short diameter was 0.764 for dMMR tumours using 10 mm size cut-off and 0.624 for pMMR tumours using 7 mm cut-off., Conclusion: These data show that CT imaging features of primary CC differ between dMMR and pMMR tumours, suggesting that the assessment of CT-based CC staging should take MMR status into consideration, especially for lymph node status, and thus may help in selecting patients for neoadjuvant treatment., Competing Interests: Conflict of interest statement The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Introduction: The spleen is a lymphoid organ and we hypothesize that clinical benefit to immunotherapy may present with an increase in splenic volume during treatment. The purpose of this study was to investigate whether changes in splenic volume could be observed in those showing clinical benefit versus those not showing clinical benefit to pembrolizumab treatment in non-small cell lung cancer (NSCLC) patients., Materials and Methods: In this study, 70 patients with locally advanced or metastatic NSCLC treated with pembrolizumab; and who underwent baseline CT scan within 2 weeks before treatment and follow-up CT within 3 months after commencing immunotherapy were retrospectively evaluated. The splenic volume on each CT was segmented manually by outlining the splenic contour on every image and the total volume summated. We compared the splenic volume in those achieving a clinical benefit and those not achieving clinical benefit, using non-parametric Wilcoxon signed-rank test. Clinical benefit was defined as stable disease or partial response lasting for greater than 24 weeks. A p-value of <0.05 was considered statistically significant., Results: There were 23 responders and 47 non-responders based on iRECIST criteria and 35 patients with clinical benefit and 35 without clinical benefit. There was no significant difference in the median pre-treatment volume (175 vs 187 cm3, p = 0.34), post-treatment volume (168 vs 167 cm3, p = 0.39) or change in splenic volume (-0.002 vs 0.0002 cm3, p = 0.97) between the two groups. No significant differences were also found between the splenic volume of patients with partial response, stable disease or progressive disease (p>0.017). Moreover, there was no statistically significant difference between progression-free survival and time to disease progression when the splenic volume was categorized as smaller or larger than the median pre-treatment or post-treatment volume (p>0.05)., Conclusion: No significant differences were observed in the splenic volume of those showing clinical benefit versus those without clinical benefit to pembrolizumab treatment in NSCLC patients. CT splenic volume cannot be used as a potentially simple biomarker of response to immunotherapy., Competing Interests: I have read the journal’s policy and one of the authors of this manuscript (Anna Minchom) have the following competing interests: has served on advisory boards for Janssen Pharmaceuticals and Merck Pharmaceuticals, has received honoraria from Chugai Pharmaceuticals, Novartis Oncology, Faron Pharmaceuticals, Bayer Pharmaceuticals, has received expenses from Amgen Pharmaceuticals and LOXO Oncology. The other authors have declared that no competing interests exist. that this does not alter our adherence to PLOS ONE policies on sharing data and materials.

Objectives: To investigate sources of variation in a multicenter rectal cancer MRI dataset focusing on hardware and image acquisition, segmentation methodology, and radiomics feature extraction software., Methods: T2W and DWI/ADC MRIs from 649 rectal cancer patients were retrospectively acquired in 9 centers. Fifty-two imaging features (14 first-order/6 shape/32 higher-order) were extracted from each scan using whole-volume (expert/non-expert) and single-slice segmentations using two different software packages (PyRadiomics/CapTk). Influence of hardware, acquisition, and patient-intrinsic factors (age/gender/cTN-stage) on ADC was assessed using linear regression. Feature reproducibility was assessed between segmentation methods and software packages using the intraclass correlation coefficient., Results: Image features differed significantly (p < 0.001) between centers with more substantial variations in ADC compared to T2W-MRI. In total, 64.3% of the variation in mean ADC was explained by differences in hardware and acquisition, compared to 0.4% by patient-intrinsic factors. Feature reproducibility between expert and non-expert segmentations was good to excellent (median ICC 0.89-0.90). Reproducibility for single-slice versus whole-volume segmentations was substantially poorer (median ICC 0.40-0.58). Between software packages, reproducibility was good to excellent (median ICC 0.99) for most features (first-order/shape/GLCM/GLRLM) but poor for higher-order (GLSZM/NGTDM) features (median ICC 0.00-0.41)., Conclusions: Significant variations are present in multicenter MRI data, particularly related to differences in hardware and acquisition, which will likely negatively influence subsequent analysis if not corrected for. Segmentation variations had a minor impact when using whole volume segmentations. Between software packages, higher-order features were less reproducible and caution is warranted when implementing these in prediction models., Key Points: • Features derived from T2W-MRI and in particular ADC differ significantly between centers when performing multicenter data analysis. • Variations in ADC are mainly (> 60%) caused by hardware and image acquisition differences and less so (< 1%) by patient- or tumor-intrinsic variations. • Features derived using different image segmentations (expert/non-expert) were reproducible, provided that whole-volume segmentations were used. When using different feature extraction software packages with similar settings, higher-order features were less reproducible., (© 2021. The Author(s).)