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.

BACKGROUND AND PURPOSE: The Neck Imaging Reporting and Data System was introduced to assess the probability of recurrence in surveillance imaging after treatment of head and neck cancer. This study investigated inter- and intrareader agreement in interpreting contrast-enhanced CT after treatment of oral cavity and oropharyngeal squamous cell carcinoma. MATERIALS AND METHODS: This retrospective study analyzed CT datasets of 101 patients. Four radiologists provided the Neck Imaging Reporting and Data System reports for the primary site and neck (cervical lymph nodes). The Kendall9s coefficient of concordance (W), Fleiss κ (κ_F), the Kendall9s rank correlation coefficient (τ_B), and weighted κ statistics (κ_w) were calculated to assess inter- and intrareader agreement. RESULTS: Overall, interreader agreement was strong or moderate for both the primary site (W = 0.74, κ_F = 0.48) and the neck (W = 0.80, κ_F = 0.50), depending on the statistics applied. Interreader agreement was higher in patients with proved recurrence at the primary site (W = 0.96 versus 0.56, κ_F = 0.65 versus 0.30) or in the neck (W = 0.78 versus 0.56, κ_F = 0.41 versus 0.29). Intrareader agreement was moderate to strong or almost perfect at the primary site (range τ_B = 0.67–0.82, κ_w = 0.85–0.96) and strong or almost perfect in the neck (range τ_B = 0.76–0.86, κ_w = 0.89–0.95). CONCLUSIONS: The Neck Imaging Reporting and Data System used for surveillance contrast-enhanced CT after treatment of oral cavity and oropharyngeal squamous cell carcinoma provide acceptable score reproducibility with limitations in patients with posttherapeutic changes but no cancer recurrence.

Purpose To retrospectively investigate the effectiveness of a novel 3-phase protocol for computed tomography (CT) before transcatheter aortic valve implantation/transcatheter aortic valve replacement (TAVI/TAVR) in terms of radiation dose and image quality. Materials and Methods A total of 107 nonrandomized patients (81 ± 7.4 years) scheduled for TAVI/TAVR underwent preprocedural CT on an 80-row CT scanner. 55 patients underwent a combined ECG-synchronized spiral scan of the chest and non-ECG-synchronized spiral scan of the abdomen/pelvis as recommended by the Society of Cardiovascular Computed Tomography (SCCT). 52 patients underwent an updated 3-phase variable helical pitch (vHP3) protocol combining a non-ECG-synchronized spiral scan of the upper thoracic aperture, followed by a prospective ECG-synchronized spiral scan of the heart, and a non-ECG-synchronized abdominal/pelvic spiral scan. The radiation dose was determined from an automatically generated protocol based on the CT dose index (CTDI). Objective image quality in terms of vessel attenuation and image noise was measured, and the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated. Subjective image quality was evaluated using a 4-point scale and compared for interrater agreement using Cohen’s weighted kappa coefficient (κw). All data were compared and statistically analyzed. Results Use of the novel 3-phase vHP3 protocol reduced the dose-length product (DLP) from 1256.58 ± 619.05 mGy*cm to 790.90 ± 238.15 mGy*cm, reducing the effective dose (E) from 21.36 ± 10.52 mSv to 13.44 ± 4.05 mSv and size-specific dose estimates (SSDE) from 20.85 ± 7.29 mGy to 13.84 ± 2.94 mGy (p < 0.001). There were no significant differences in objective and subjective image quality between the two protocols and between the two readers. Conclusion The novel 3-phase vHP3 protocol significantly reduces the radiation dose of preprocedural TAVI/TAVR CT without a loss of image quality. Key Points: Citation Format

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.

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.