(1) Background: Tumoral heterogeneity (TH) is a major challenge in the treatment of metastatic colorectal cancer (mCRC) and is associated with inferior response. Therefore, the identification of TH would be beneficial for treatment planning. TH can be assessed by identifying genetic alterations. In this work, a radiomics-based approach for assessment of TH in colorectal liver metastases (CRLM) in CT scans is demonstrated. (2) Methods: In this retrospective study, CRLM of mCRC were segmented and radiomics features extracted using pyradiomics. Unsupervised k-means clustering was applied to features and lesions. Feature redundancy was evaluated by principal component analysis and reduced by Pearson correlation coefficient cutoff. Feature selection was conducted by LASSO regression and visual analysis of the clusters by radiologists. (3) Results: A total of 47 patients’ (36% female, median age 64) CTs with 261 lesions were included. Five clusters were identified, and the categories small disseminated (n = 31), heterogeneous (n = 105), homogeneous (n = 64), mixed (n = 59), and very large type (n = 2) were assigned based on visual characteristics. Further statistical analysis showed correlation (p < 0.01) of clusters with sex, primary location, T- and N-status, and mutational status. Feature reduction and selection resulted in the identification of four features as a final set for cluster definition. (4) Conclusions: Radiomics features can characterize TH in liver metastases of mCRC in CT scans, and may be suitable for a better pretherapeutic classification of liver lesion phenotypes.

Similar to the transformation towards personalized oncology treatment, emerging techniques for evaluating oncologic imaging are fostering a transition from traditional response assessment towards more comprehensive cancer characterization via imaging. This development can be seen as key to the achievement of truly personalized and optimized cancer diagnosis and treatment. This review gives a methodological introduction for clinicians interested in the potential of quantitative imaging biomarkers, treating of radiomics models, texture visualization, convolutional neural networks and automated segmentation, in particular. Based on an introduction to these methods, clinical evidence for the corresponding imaging biomarkers—(i) dignity and etiology assessment; (ii) tumoral heterogeneity; (iii) aggressiveness and response; and (iv) targeting for biopsy and therapy—is summarized. Further requirements for the clinical implementation of these imaging biomarkers and the synergistic potential of personalized molecular cancer diagnostics and liquid profiling are discussed.

The clinical use of photon-counting CT (PCCT) allows for the generation of virtual non-contrast (VNC) series from contrast-enhanced images. In routine clinical practice, specific issues such as ruling out acute bleeding require non-contrast images. The aim of this study is to evaluate the use of PCCT-derived VNC reconstructions in abdominal imaging. PCCT scans of 17 patients including early arterial, portal venous and native sequences were enrolled. VNC reconstructions have been calculated. In every sequence and VNC reconstruction, 10 ROIs were measured (portal vein, descending aorta, inferior vena cava, liver parenchyma, spleen parenchyma, erector spinae muscle, subcutaneous adipose tissue, first lumbar vertebral body, air, and psoas muscle) and density values were compared. The VNC reconstructions show significant changes in density compared to the contrast-enhanced images. However, there were no significant differences present between the true non-contrast (TNC) and any VNC reconstructions in the observed organs and vessels. Significant differences (p < 0.05) between the measured mean density values in the TNC versus VNC reconstructions were found in fat and bone tissue. The PCCT-derived VNC reconstructions seemed to be comparable to the TNC images, despite some deviations shown in the adipose tissue and bone structures. However, the further benefits in terms of specific clinical issues need to be evaluated.

Background: Accurate imaging plays a crucial role for prognostic assessment and treatment allocation in soft tissue sarcoma (STS). Guidelines recommend contrast-enhanced magnetic resonance imaging (MRI) including diffusion-weighted imaging (DWI) for tumor assessment. This study aims to evaluate the extent to which the MRI protocols used in clinical practice align with the sequences specified in the guideline in the diagnostic work-up of sarcoma patients. Methods: We analyzed a cohort of patients treated at the Sarcoma Center of the University Medical Center Mannheim (UMM). Pretherapeutic MRI protocols were compared with the recommendations of the German sarcoma guideline. Results: We analyzed 64 MRI examinations of patients with STS of the extremities and trunk, notably 62 performed at external radiology centers and 2 at UMM. A pretherapeutic contrast-enhanced MRI was available in 51 of 64 cases (79.7%), of which 40 referrals explicitly indicated a soft tissue tumor. In 3.1% of the cases, the complete set of guideline-recommended sequences was not performed. At least two of the required sequences were consistently available. The most frequently absent sequences were the T1-weighted sequence without fat saturation acquired before and after contrast administration using identical acquisition parameters (82.8%) and DWI sequences (78.1%). Conclusions: MRI protocol selection is primarily guided by the clinical referral question. Our analysis indicates that a substantial proportion of examinations deviated from recommended protocols. Contributing factors may include inaccurate clinical referral questions, limited guideline awareness, reimbursement constraints in outpatient settings, or diagnostic considerations. Standardized imaging represents a key instrument for quality assurance in the diagnosis of STS and forms the foundation for clinical and translational studies. In the future, the recommendations should be re-evaluated and published in a clear, accessible format to ensure broad implementation.

Background/Objectives: To evaluate the prognostic significance of automated, volumetric body composition analysis (BCA) derived from pretreatment thoracic computed tomography (CT) scans in patients with head and neck cancer (HNC). Methods: We retrospectively assessed 160 patients (median age: 63 years; 26.9% women) undergoing primary treatment. BCA quantified various tissue volumes, including bone (B), skeletal muscle (SM), and subcutaneous adipose tissue (SAT). Optimal sex-specific cutoffs for BCA metrics were established via maximally selected log-rank tests. Internal validation of BCA cutoffs was conducted via bootstrap resampling. Kaplan–Meier survival analysis and Cox proportional hazards modeling were used to investigate progression-free survival (PFS). Results: The median PFS for all patients was 51.7 months (95% confidence interval (CI): 31.4–68.8). Among the continuous BCA parameters, only SM/B was significant across the total cohort (hazard ratio (HR): 0.23; 95%CI: 0.12–0.46; p < 0.0001, males (p = 0.0009), females (p = 0.004)). Internal validation of gender-specific cutoffs demonstrated strong-to-intermediate stability for SM/B across both sexes and for SAT/B in males. In contrast, SAT/B exhibited only weak stability among female participants. In univariate PFS analysis, dichotomized SM/B, SAT/B, Union for International Cancer Control (UICC) stage, Eastern Cooperative Oncology Group (ECOG) status, higher body mass index (BMI), normal albumin, and Charlson Comorbidity Index were identified as significant predictors of PFS. Multivariable analysis identified high SM/B (HR: 0.53; 95% CI: 0.3–0.93; p = 0.026) and high SAT/B (HR: 0.58; 95% CI: 0.35–0.95; p = 0.029) as independent prognostic factors, alongside lower UICC stage (p = 0.045) and lower Charlson Comorbidity Index (p = 0.038). Patients with high SM/B and SAT/B ratios had the longest median PFS (65.9 months, 95%CI: 51.7–.), compared to 36.4 months (95%CI: 19.4–.) for high SM/B or SAT/B and 12.6 months (95%CI: 4.2–25.1) for low SM/B and SAT/B (p < 0.0001). Conclusions: Although the BCA parameters SM/B and, to a lesser extent, SAT/B appear to be promising biomarkers, external validation and investigation within well-defined patient subgroups are warranted to establish their generalizability in clinical practice.

Objectives: To evaluate Sodium Magnetic Resonance (²³Na MR) images of prostate cancer (PCa) and quantify tissue sodium concentration (TSC) based on internal references. Methods: Forty-six patients with clinically suspected prostate cancer were included into a prospective study. The patients underwent multiparametric MRI and an additional ²³Na MRI examination of the prostate, performed at 3T (Magnetom Skyra, Siemens Healthineers, Erlangen, Germany) using a dual-tuned ¹H/²³Na body-coil (Rapid Biomedical, Rimpar, Germany) to acquire a 3D radial density-adapted ²³Na sequence. Three-dimensional regions of interest (ROI) were defined within the femoral blood vessels, which were used as an internal reference for TSC quantification. Prostate zones and tumor ROIs were defined and TSC was evaluated for each ROI. Results: TSC quantification based on femoral blood vessels demonstrated good stability since mean absolute TSC difference between right and left regions of interest in the blood vessels was 3.3 ± 2.2 mM. TSC in the peripheral zone (40.7 ± 6.0 mM) was significantly higher than in the transition zone (37.5 ± 5.7 mM). Nine suspicious lesions (PI-RADS 4 and 5) were identified in eight men, all with biopsy-proven PCa with Gleason scores of ≥3 + 3. TSC in prostate cancer was significantly lower than in contralateral healthy regions, 32.2 ± 5.5 mM and 36.1 ± 3.9 mM, respectively (p = 0.018). Conclusions: TSC quantification of prostate tissue based on internal references is feasible and reliable. TSC was significantly decreased within prostate cancer, which represents a quantitative imaging biomarker that could potentially improve PCa characterization and risk stratification.