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Resistance against radio(chemo)therapy-induced cell death is a major determinant of oncological treatment failure and remains a perpetual clinical challenge. The underlying mechanisms are manifold and demand for comprehensive, cancer entity- and subtype-specific examination. In the present study, resistance against radiotherapy was systematically assessed in a panel of human head-and-neck squamous cell carcinoma (HNSCC) cell lines and xenotransplants derived thereof with the overarching aim to extract master regulators and potential candidates for mechanism-based pharmacological targeting. Clonogenic survival data were integrated with molecular and functional data on DNA damage repair and different cell fate decisions. A positive correlation between radioresistance and early induction of HNSCC cell senescence accompanied by NF-κB-dependent production of distinct senescence-associated cytokines, particularly ligands of the CXCR2 chemokine receptor, was identified. Time-lapse microscopy and medium transfer experiments disclosed the non-cell autonomous, paracrine nature of these mechanisms, and pharmacological interference with senescence-associated cytokine production by the NF-κB inhibitor metformin significantly improved radiotherapeutic performance in vitro and in vivo. With regard to clinical relevance, retrospective analyses of TCGA HNSCC data and an in-house HNSCC cohort revealed that elevated expression of CXCR2 and/or its ligands are associated with impaired treatment outcome. Collectively, our study identifies radiation-induced tumor cell senescence and the NF-κB-dependent production of distinct senescence-associated cytokines as critical drivers of radioresistance in HNSCC whose therapeutic targeting in the context of multi-modality treatment approaches should be further examined and may be of particular interest for the subgroup of patients with elevated expression of the CXCR2/ligand axis.

Patients with Marfan syndrome and related disorders are at risk for aortic dissection and aortic rupture and therefore require appropriate monitoring. Computed tomography (CT) and transthoracic echocardiography (TTE) are routinely used for initial diagnosis and follow-up. The purpose of this study is to compare whole-heart CT and TTE aortic measurement for initial work-up, 2-year follow-up, and detection of progressive aortic enlargement. This retrospective study included 95 patients diagnosed with Marfan syndrome or a related disorder. All patients underwent initial work-up including aortic diameter measurement using both electrocardiography-triggered whole-heart CT and TTE. Forty-two of these patients did not undergo aortic repair after initial work-up and were monitored by follow-up imaging within 2 years. Differences between the two methods for measuring aortic diameters were compared using Bland–Altman plots. The acceptable clinical limit of agreement (acLOA) for initial work-up, follow-up, and progression within 2 years was predefined as <  ± 2 mm. Bland–Altman analysis revealed a small bias of 0.2 mm with wide limits of agreement (LOA) from + 6.3 to − 5.9 mm for the aortic sinus and a relevant bias of − 1.6 mm with wide LOA from + 5.6 to − 8.9 mm for the ascending aorta. Follow-up imaging yielded a small bias of 0.5 mm with a wide LOA from + 6.7 to − 5.8 mm for the aortic sinus and a relevant bias of 1.1 mm with wide LOA from + 8.1 to − 10.2 mm for the ascending aorta. Progressive aortic enlargement at follow-up was detected in 57% of patients using CT and 40% of patients using TTE. Measurement differences outside the acLOA were most frequently observed for the ascending aorta. Whole-heart CT and TTE measurements show good correlation, but the frequency of measurement differences outside the acLOA is high. TTE systematically overestimates aortic diameters. Therefore, whole-heart CT may be preferred for aortic monitoring of patients with Marfan syndrome and related disorders. TTE remains an indispensable imaging tool that provides additional information not available with CT.

Background Patients with Marfan syndrome are at risk for aortic enlargement and are routinely monitored by computed tomography (CT) imaging. The purpose of this study is to analyse body composition using artificial intelligence (AI)‐based tissue segmentation in patients with Marfan syndrome in order to identify possible predictors of progressive aortic enlargement. Methods In this study, the body composition of 25 patients aged ≤50 years with Marfan syndrome and no prior aortic repair was analysed at the third lumbar vertebra (L3) level from a retrospective dataset using an AI‐based software tool (Visage Imaging). All patients underwent electrocardiography‐triggered CT of the aorta twice within 2 years for suspected progression of aortic disease, suspected dissection, and/or pre‐operative evaluation. Progression of aortic enlargement was defined as an increase in diameter at the aortic sinus or the ascending aorta of at least 2 mm. Patients meeting this definition were assigned to the ‘progressive aortic enlargement’ group (proAE group) and patients with stable diameters to the ‘stable aortic enlargement’ group (staAE group). Statistical analysis was performed using the Mann–Whitney U test. Two possible body composition predictors of aortic enlargement—skeletal muscle density (SMD) and psoas muscle index (PMI)—were analysed further using multivariant logistic regression analysis. Aortic enlargement was defined as the dependent variant, whereas PMI, SMD, age, sex, body mass index (BMI), beta blocker medication, and time interval between CT scans were defined as independent variants. Results There were 13 patients in the proAE group and 12 patients in the staAE group. AI‐based automated analysis of body composition at L3 revealed a significantly increased SMD measured in Hounsfield units (HUs) in patients with aortic enlargement (proAE group: 50.0 ± 8.6 HU vs. staAE group: 39.0 ± 15.0 HU; P = 0.03). PMI also trended towards higher values in the proAE group (proAE group: 6.8 ± 2.3 vs. staAE group: 5.6 ± 1.3; P = 0.19). Multivariate logistic regression revealed significant prediction of aortic enlargement for SMD (P = 0.05) and PMI (P = 0.04). Conclusions Artificial intelligence‐based analysis of body composition at L3 in Marfan patients is feasible and easily available from CT angiography. Analysis of body composition at L3 revealed significantly higher SMD in patients with progressive aortic enlargement. PMI and SMD significantly predicted aortic enlargement in these patients. Using body composition as a predictor of progressive aortic enlargement may contribute information for risk stratification regarding follow‐up intervals and the need for aortic repair.

Background Body composition (BC) analysis is performed to quantify the relative amounts of different body tissues as a measure of physical fitness and tumor cachexia. We hypothesized that relative changes in body composition (BC) parameters, assessed by an artificial intelligence–based, PACS-integrated software, between baseline imaging before the start of radioligand therapy (RLT) and interim staging after two RLT cycles could predict overall survival (OS) in patients with metastatic castration-resistant prostate cancer. Methods We conducted a single-center, retrospective analysis of 92 patients with mCRPC undergoing [ 177 Lu]Lu-PSMA RLT between September 2015 and December 2023. All patients had [ 68  Ga]Ga-PSMA-11 PET/CT at baseline (≤ 6 weeks before the first RLT cycle) and at interim staging (6–8 weeks after the second RLT cycle) allowing for longitudinal BC assessment. Results During follow-up, 78 patients (85%) died. Median OS was 16.3 months. Median follow-up time in survivors was 25.6 months. The 1 year mortality rate was 32.6% (95%CI 23.0–42.2%) and the 5 year mortality rate was 92.9% (95%CI 85.8–100.0%). In multivariable regression, relative change in visceral adipose tissue (VAT) (HR: 0.26; p  = 0.006), previous chemotherapy of any type (HR: 2.4; p  = 0.003), the presence of liver metastases (HR: 2.4; p  = 0.018) and a higher baseline De Ritis ratio (HR: 1.4; p  < 0.001) remained independent predictors of OS. Patients with a higher decrease in VAT (< −20%) had a median OS of 10.2 months versus 18.5 months in patients with a lower VAT decrease or VAT increase (≥ −20%) (log-rank test: p  = 0.008). In a separate Cox model, the change in VAT predicted OS ( p  = 0.005) independent of the best PSA response after 1–2 RLT cycles ( p  = 0.09), and there was no interaction between the two ( p  = 0.09). Conclusions PACS-Integrated, AI-based BC monitoring detects relative changes in the VAT, Which was an independent predictor of shorter OS in our population of patients undergoing RLT

State-of-the-art technology in Computed Tomography (CT) includes iterative reconstruction algorithms (IR) and metal artefact reduction (MAR) techniques. The objective of the study is to show the benefits of this technology for the detection of primary and recurrent head and neck cancer. A total of 131 patients underwent contrast-enhanced CT for diagnosis of primary and recurrent Head and Neck cancer; 110 patients were included. All scans were reconstructed using iterative reconstruction, and metal artifact reduction was applied when indicated. Tumor detectability was evaluated dichotomously. Histopathological findings were used as a standard of reference. Data were analyzed retrospectively, statistics was performed through diagnostic test characteristics. State-of-the-art Head and Neck CT showed a sensitivity of 0.83 (95% CI; 0.61–0.95) with 0.93 specificity (95% CI; 0.84–0.98) for primary tumor detection. Recurrent tumors were identified with a 0.94 sensitivity (95% CI; 0.71–0.99) and 0.93 specificity (95% CI; 0.84–0.98) in this study. Conclusion: State-of-the-art reconstruction tools improve the diagnostic quality of Head and Neck CT, especially for recurrent tumor detection, compared with data published for standard CT. IR and MAR are easily implemented in routine clinical settings and improve image evaluation by reducing artifacts and image noise while lowering radiation exposure.

The Eurotransplant Senior Program allocates kidneys to elderly transplant patients. The aim of this retrospective study is to investigate the use of computed tomography (CT) body composition using artificial intelligence (AI)-based tissue segmentation to predict patient and kidney transplant survival. Body composition at the third lumbar vertebra level was analyzed in 42 kidney transplant recipients. Cox regression analysis of 1-year, 3-year and 5-year patient survival, 1-year, 3-year and 5-year censored kidney transplant survival, and 1-year, 3-year and 5-year uncensored kidney transplant survival was performed. First, the body mass index (BMI), psoas muscle index (PMI), skeletal muscle index (SMI), visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT) served as independent variates. Second, the cut-off values for sarcopenia and obesity served as independent variates. The 1-year uncensored and censored kidney transplant survival was influenced by reduced PMI (p = 0.02 and p = 0.03, respectively) and reduced SMI (p = 0.01 and p = 0.03, respectively); 3-year uncensored kidney transplant survival was influenced by increased VAT (p = 0.04); and 3-year censored kidney transplant survival was influenced by reduced SMI (p = 0.05). Additionally, sarcopenia influenced 1-year uncensored kidney transplant survival (p = 0.05), whereas obesity influenced 3-year and 5-year uncensored kidney transplant survival. In summary, AI-based body composition analysis may aid in predicting short- and long-term kidney transplant survival.

BackgroundMagnetic resonance imaging (MRI) is used to detect the prostate index lesion before targeted biopsy. However, the number of biopsy cores that should be obtained from the index lesion is unclear. The aim of this study is to analyze how many MRI-targeted biopsy cores are needed to establish the most relevant histopathologic diagnosis of the index lesion and to build a prediction model.MethodsWe retrospectively included 451 patients who underwent 10-core systematic prostate biopsy and MRI-targeted biopsy with sampling of at least three cores from the index lesion. A total of 1587 biopsy cores were analyzed. The core sampling sequence was recorded, and the first biopsy core detecting the most relevant histopathologic diagnosis was identified. In a subgroup of 261 patients in whom exactly three MRI-targeted biopsy cores were obtained from the index lesion, we generated a prediction model. A nonparametric Bayes classifier was trained using the PI-RADS score, prostate-specific antigen (PSA) density, lesion size, zone, and location as covariates.ResultsThe most relevant histopathologic diagnosis of the index lesion was detected by the first biopsy core in 331 cases (73%), by the second in 66 cases (15%), and by the third in 39 cases (9%), by the fourth in 13 cases (3%), and by the fifth in two cases (<1%). The Bayes classifier correctly predicted which biopsy core yielded the most relevant histopathologic diagnosis in 79% of the subjects. PI-RADS score, PSA density, lesion size, zone, and location did not independently influence the prediction model.ConclusionThe most relevant histopathologic diagnosis of the index lesion was made on the basis of three MRI-targeted biopsy cores in 97% of patients. Our classifier can help in predicting the first MRI-targeted biopsy core revealing the most relevant histopathologic diagnosis; however, at least three MRI-targeted biopsy cores should be obtained regardless of the preinterventionally assessed covariates.

To evaluate whether Canon's Single-Energy Metal Artifact Reduction (SEMAR) algorithm can significantly improve subjective and objective image quality of patients with nonremovable dental hardware undergoing CT imaging of the oral cavity and oropharynx. SEMAR was reconstructed from routine Adaptive Iterative Dose Reduction (AIDR) images in 154 patients (46 females and 108 males; mean age 66.3 ± 10.5 years). Subjective SEMAR and AIDR image quality of the mouth floor, sublingual glands, lymphatic ring and overall impression were evaluated by two independent radiologists on a 6-point scale (1 = very good image quality, 6 = poor image quality) and compared to ratings of an oral and maxillofacial surgeon. Interrater agreement was assessed using the intraclass correlation coefficient (ICC). Objective image analysis was performed by placing regions of interest (ROIs) on the mouth floor and measuring CT attenuation in Hounsfield units (HU) and standard deviation (SD). SEMAR significantly improved subjective image quality in all evaluated structures for all raters (p < 0.001). Furthermore, SEMAR significantly reduced objective metal artifacts and image noise (p < 0.001). SEMAR significantly improved diagnostic quality of CT images of the oral cavity and oropharynx by reducing artifacts caused by dental hardware. •Metal artifacts from dental implants degrades diagnostic information in CT imaging.•SEMAR is an algorithm to reduce metal artifacts caused by photon starvation.•SEMAR significantly improves subjective image quality across medical specialties.•SEMAR significantly reduces image noise and leads to realistic CT attenuations.•SEMAR can be applied to raw data retrospectively and requires no previous planning.

After hepatic microwave ablation, the differentiation between fully necrotic and persistent vital tissue through contrast enhanced CT remains a clinical challenge. Therefore, there is a need to evaluate new imaging modalities, such as CT perfusion (CTP) to improve the visualization of coagulation necrosis. MWA and CTP were prospectively performed in five healthy pigs. After the procedure, the pigs were euthanized, and the livers explanted. Orthogonal histological slices of the ablations were stained with a vital stain, digitalized and the necrotic core was segmented. CTP maps were calculated using a dual-input deconvolution algorithm. The segmented necrotic zones were overlaid on the DICOM images to calculate the accuracy of depiction by CECT/CTP compared to the histological reference standard. A receiver operating characteristic analysis was performed to determine the agreement/true positive rate and disagreement/false discovery rate between CECT/CTP and histology. Standard CECT showed a true positive rate of 81% and a false discovery rate of 52% for display of the coagulation necrosis. Using CTP, delineation of the coagulation necrosis could be improved significantly through the display of hepatic blood volume and hepatic arterial blood flow (p < 0.001). The ratios of true positive rate/false discovery rate were 89%/25% and 90%/50% respectively. Other parameter maps showed an inferior performance compared to CECT.

Pancreatic cancer is the seventh leading cause of cancer death in both sexes. The aim of this study is to analyze baseline CT body composition using artificial intelligence to identify possible imaging predictors of survival. We retrospectively included 103 patients. First, the presence of surgical treatment and cut-off values for sarcopenia and obesity served as independent variates. Second, the presence of surgery, subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), and skeletal muscle index (SMI) served as independent variates. Cox regression analysis was performed for 1-year, 2-year, and 3-year survival. Possible differences between patients undergoing surgical versus nonsurgical treatment were analyzed. Presence of surgery significantly predicted 1-year, 2-year, and 3-year survival (p = 0.01, <0.001, and <0.001, respectively). Across the follow-up periods of 1-year, 2-year, and 3-year survival, the presence of sarcopenia became an equally important predictor of survival (p = 0.25, 0.07, and <0.001, respectively). Additionally, increased VAT predicted 2-year and 3-year survival (p = 0.02 and 0.04, respectively). The impact of sarcopenia on 3-year survival was higher in the surgical treatment group (p = 0.02 and odds ratio = 2.57) compared with the nonsurgical treatment group (p = 0.04 and odds ratio = 1.92). Fittingly, a lower SMI significantly affected 3-year survival only in patients who underwent surgery (p = 0.02). Especially if surgery is performed, AI-derived sarcopenia and reduced muscle mass are unfavorable imaging predictors.