Explore the vast range of titles available.

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. 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. 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 cm.sup.3, p = 0.34), post-treatment volume (168 vs 167 cm.sup.3, p = 0.39) or change in splenic volume (-0.002 vs 0.0002 cm.sup.3, 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). 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.

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. Graphical Abstract

Background Deep-learning (DL) reconstructions could improve image quality and reduce acquisition time in diffusion-weighted imaging (DWI). This study assessed, qualitatively and quantitatively, DL-DWI in liver metastasis of colorectal cancer patients. Methods This prospective study enrolled 50 participants from June to November 2022. Phantom and participant data were acquired on a 1.5T MR scanner using a free-breathing DL-DWI research application sequence. Three DWIs were compared: a moderately-accelerated DL-DWI (DL-1), a corresponding standard reconstruction (Standard-1) and a highly-accelerated DL-DWI (DL-2). Image quality (four features on b750 images and one feature on ADC map) was assessed by two radiologists. Region of interest (ROI) based ADC measurements were performed at three locations: liver, spleen, liver metastasis. Across the three series, median scores and ADC values were assessed using a Friedman non-parametric test and post-hoc analysis (pairwise Wilcoxon tests with Bonferroni correction). A p-value < 0.05 was considered statistically significant. Results Fifty participants with metastatic colorectal cancer (mean age 62 years, range 36–88 years, 26 males) were evaluated. ROIs were delineated in liver ( N  = 50), spleen ( N  = 48), and liver metastasis ( N  = 11). Qualitatively, across both readers, DL-1 method received the highest scores for 5/8 features on the b750 images; all methods scored similarly on ADC maps for both readers. Quantitatively, ADCs were significantly different between DL-1 and Standard-1 series across all three organs, with DL-1-based ADC always higher ( p  < 0.01). This ADC increase was small: 8.9% (liver), 3.4% (spleen), 4.5% (liver metastasis). Conclusions This study suggests that a DL-based reconstruction is a promising technique to enable acceleration of liver DWI considering both qualitative and quantitative results. Trial registration NCT05118555 (Evaluation of New Magnetic Resonance Techniques); study date of registration (first submitted: 2021-10-18).

Background Oncology practice is increasingly aiming to be patient centric. Imaging is a decisive part of the management of cancer patients and with the introduction of Digital Health Records (DHR) patients have the possibility of accessing their imaging results independently, yet the optimal way of doing so is still not clear. The introduction of Large Language Models (LLM) offers the potential to turn radiology reports into a clearer, accessible and unambiguous format and to democratise patient’s access to their own medical records. Methods A multi-reader retrospective Service Evaluation (SE) conducted at a tertiary oncology hospital aimed to assess the capability of an LLM to generate two versions of simplified oncology imaging reports. The SE assessed Patient and Public Involvement (PPI) representatives and healthcare professionals’ (HCP) preferences using original radiology reports from two cohorts, colorectal ( n  = 30) and lung ( n  = 30) cancer. A Prompt-development phase created two prompts to generate the summarised (version A) and the full-length (version B) report versions. The review was performed by radiologists with 360 reads and PPI representatives with 180 reads. Results Radiologists scores between summaries and full-length reports differed per cohort. In the lung cohort, version A was rated higher for factual correctness ( P  = 0.001), completeness ( P  < 0.0001), accessibility and readability ( P  = 0.026), and benefit to patients ( P  < 0.0001). The opposite was seen in the colorectal cohort, version B achieved consistently higher scores ( P  < 0.002). When the two cohorts were combined, median scores for version A and B did not differ significantly (all P  > 0.057). PPI reviews indicated that full-length reports were favoured significantly ( P  < 0.0001). Qualitative results from radiologists and PPI identified incorrect statements ( n  = 28), complex terminology ( n  = 18), addition of confusion ( n  = 10), and missing information ( n  = 10). Conclusions LLM simplified reports have the potential to improve patient accessibility in oncology imaging. PPI and HCP preferences for summarised versus full-length reports vary. Findings suggest these outputs are likely to benefit from appropriate adjustments to individual patient needs and clinical context. Reports with incorrect, confusing and missing content, highlight that LLM need improvement, ahead of potential clinical use in this setting.

Background To compare liver image quality and lesion detection using an AI-augmented T1-weighted sequence on hepatobiliary-phase gadoxetate-enhanced magnetic resonance imaging (MRI). Methods Fifty patients undergoing gadoxetate-enhanced MRI were recruited. Two T1-weighted Dixon sequences were utilized: a 17-s breath-hold acquisition and an accelerated 12-s breath-hold acquisition (reduced phase resolution), both reconstructed using neural network (NN) and iterative denoising (ID), NN-alone, ID-alone, and the standard method. Contrast-to-noise ratio (CNR) was assessed quantitatively for all series (ANOVA). Two blinded radiologists independently analyzed three image sets: 17-s acquisition reconstructed with NN and ID (17-s NN + ID), 12-s acquisition reconstructed with NN and ID (12-s NN + ID), and 17-s acquisition with standard reconstruction (17-s standard). Overall image quality, qualitative CNR, lesion edge sharpness, vessel edge sharpness, and respiratory motion artifacts were scored (4-point Likert scale) and compared (Friedman test). Lesion detection was compared between 12-s NN + ID and 17-s standard reconstructions (Wilcoxon signed-rank test). Results Quantitative liver-to-portal vein CNR was significantly higher for 17-s NN + ID than 17-s standard or 17-s NN-alone images ( p  = 0.001). Scores for overall image quality, qualitative CNR, vessel edge sharpness, and lesion edge sharpness were significantly higher for 17-s NN + ID and 12-s NN + ID than standard reconstruction ( p  < 0.001); there was no significant difference between 17-s and 12-s NN + ID. There was no significant difference in respiratory motion artifacts and number of lesions or diameter of the smallest detected lesion using 12-s NN + ID or 17-s standard reconstruction. Conclusion AI-augmented reconstructions can improve image quality while reducing breath-hold duration in T1-weighted hepatobiliary-phase gadoxetate-enhanced MRI, without compromising lesion detection. Relevance statement AI-augmented reconstruction of T1-weighted MRI improves image quality and lesion detection in hepatobiliary phase liver imaging, reducing breath-hold duration without compromising clinical lesion detection. Key Points Liver-to-portal vein CNR was significantly higher for 17-s NN + ID. AI-augmented reconstructions scored higher for image quality, contrast-to-noise, vessel-edge, and lesion-edge sharpness. No significant difference in lesion detection between 12-s NN + ID and 17-s standard reconstructions. Graphical Abstract

Objective Radiogenomics promises noninvasive tumor profiling; however, the extent to which imaging morphology reflects tumor lineage versus host-organ milieu remains unclear. This study aimed to quantify the relative influence of tumor type and anatomical environment on contrast-enhanced computed tomography (CT) radiomic phenotypes. Materials and methods A discovery cohort of 1,598 patients (10,485 lesions) and an external validation cohort of 2,440 patients (6,597 lesions) underwent portal-venous-phase CT. After manual segmentation, lesion-level radiomic features were standardized and embedded using t -distributed stochastic neighbor embedding. Bayesian-optimized agglomerative clustering defined morphology-based groups. Concordance with the primary tumor site (lineage) and anatomical environment was quantified using bootstrapped adjusted Rand indices (ARI); the silhouette score assessed clustering quality. Feature-class (shape, intensity, texture) and mask-erosion experiments probed mechanistic drivers. Results Six morphological clusters were identified in the discovery set (silhouette = 0.44). Morphology aligned more strongly with environment (mean ARI = 0.37) but poorly with lineage (mean ARI = 0.04; p  < 0.010); this pattern held externally. In solid organ metastases, environment dominance was even stronger (mean ARI = 0.60 versus 0.05; p  < 0.010). Intensity and texture drove the morphological association with anatomical environment (ARI = 0.64–0.56) more than shape (ARI = 0.06). When the periphery of the tumor was eroded, the same patterns were observed, implicating the tumor core. Conclusion Across organs and tumor types, tumor morphological phenotype on CT imaging is largely driven by a host tissue-related environmental “imprint” rather than the primary tumor site. Relevance statement Context-aware modeling is essential for reliable radiomic biomarkers and could motivate a two-step AI pipeline that first identifies the organ habitat and refines lineage-specific predictions. Key Points In a large, multicenter cohort, tumors exhibited distinct morphological clustering. These clusters did not align with primary tumor sites (ARI = 0.04). Stronger associations emerged between morphological clusters and the local anatomical environment (ARI = 0.37). Stratification by lesion type revealed even stronger associations between local anatomical context and solid organ metastases (ARI = 0.60). Graphical Abstract

The purpose of this case report is to emphasize the importance of curing any clinical radiological elements in this historical period, especially in the area of endemic to coronavirus disease 19 (COVID-19) such as Lombardy and to stress the importance of the management of the asymptomatic patient, their crucial role in the spread of contagion. The clinical choices must, therefore, to make use of all the diagnostic tools available and full knowledge of the limitation of each of them.

Purpose To evaluate the diagnostic accuracy of imaging features to predict lymph node status of colon cancer using CT. Methods This was a retrospective study from 2 tertiary hospitals in South Korea and Netherlands. 317 Colon cancer patients who underwent primary surgical treatment were included. Number of lymph nodes according to the anatomical location, size, cluster, degree of attenuation, shape, presence of internal heterogeneity and ill-defined margin of the lymph node were assessed and compared according to histological lymph node status. Results The largest short diameter of lymph node and presence of internal heterogeneity of lymph node showed significant association with malignant lymph node status ( P  < 0.001 and P  = 0.041, respectively). The ROC curve analysis revealed AUC of 0.703 for the largest short diameter of lymph node ( P  < 0.001), and AUC of the presence of internal heterogeneity was 0.630 ( P  < 0.001). In addition, our study showed that a total number of lymph nodes, regardless of size, ( P  = 0.022) and number of lymph nodes in peritumoral area ( P  < 0.001) and along the mesenteric vessels ( P  < 0.001) on CT demonstrated significant association with malignant status of lymph nodes in colon cancer. Conclusions There were significant associations between lymph node status and imaging features of lymph nodes on CT in colon cancer patients. The largest short diameter of lymph node and presence of internal heterogeneity can be used to predict the malignant status of lymph node in colon cancer patients. Also, the number of lymph nodes near the colonic tumor should be considered in assessment of colon cancer lymph node involvement on CT.

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.