Deep learning-based prediction of post-pancreaticoduodenectomy pancreatic fistula.

Postoperative pancreatic fistula is a life-threatening complication with an unmet need for accurate prediction. This study was aimed to develop preoperative artificial intelligence-based prediction models. Patients who underwent pancreaticoduodenectomy were enrolled and stratified into model development and validation sets by surgery between 2016 and 2017 or in 2018, respectively. Machine learning models based on clinical and body composition data, and deep learning models based on computed tomographic data, were developed, combined by ensemble voting, and final models were selected comparison with earlier model. Among the 1333 participants (training, n = 881; test, n = 452), postoperative pancreatic fistula occurred in 421 (47.8%) and 134 (31.8%) and clinically relevant postoperative pancreatic fistula occurred in 59 (6.7%) and 27 (6.0%) participants in the training and test datasets, respectively. In the test dataset, the area under the receiver operating curve [AUC (95% confidence interval)] of the selected preoperative model for predicting all and clinically relevant postoperative pancreatic fistula was 0.75 (0.71-0.80) and 0.68 (0.58-0.78). The ensemble model showed better predictive performance than the individual ML and DL models.

Whole Process of Standardization of Diffusion-Weighted Imaging: Phantom Validation and Clinical Application According to the QIBA Profile.

Background: To use the apparent diffusion coefficient (ADC) as reliable biomarkers, validation of MRI equipment performance and clinical acquisition protocols should be performed prior to application in patients. This study aims to validate various MRI equipment and clinical brain protocols for diffusion weighted imaging (DWI) using commercial phantom, and confirm the validated protocols in patients' images. Methods: The performance of four different scanners and clinical brain protocols were validated using a Quantitative Imaging Biomarker Alliance (QIBA) diffusion phantom and cloud-based analysis tool. We evaluated the performance metrics regarding accuracy and repeatability of ADC measurement using QIBA profile. The validated clinical brain protocols were applied to 17 patients, and image quality and repeatability of ADC were assessed. Results: The MRI equipment performance of all four MRI scanners demonstrated high accuracy in ADC measurement (ADC bias, -2.3% to -0.4%), excellent linear correlation to the reference ADC value (slope, 0.9 to 1.0; R2, 0.999-1.000), and high short-term repeatability [within-subject-coefficient-of-variation (wCV), 0% to 0.3%]. The clinical protocols were also validated by fulfilling QIBA claims with high accuracy (ADC bias, -3.1% to -0.7%) and robust repeatability (wCV, 0% to 0.1%). Brain DWI acquired using the validated clinical protocols showed ideal image quality (mean score ≥ 2.9) and good repeatability (wCV, 1.8-2.2). Conclusions: The whole process of standardization of DWI demonstrated the robustness of ADC with high accuracy and repeatability across diverse MRI equipment and clinical protocols in accordance with the QIBA claims.

Mapping Changes in Glutamate with Glutamate-Weighted MRI in Forced Swim Test Model of Depression in Rats.

Chemical exchange saturation transfer with glutamate (GluCEST) imaging is a novel technique for the non-invasive detection and quantification of cerebral Glu levels in neuromolecular processes. Here we used GluCEST imaging and 1H magnetic resonance spectroscopy (1H MRS) to assess in vivo changes in Glu signals within the hippocampus in a rat model of depression induced by a forced swim test. The forced swimming test (FST) group exhibited markedly reduced GluCEST-weighted levels and Glu concentrations when examined using 1H MRS in the hippocampal region compared to the control group (GluCEST-weighted levels: 3.67 ± 0.81% vs. 5.02 ± 0.44%, p < 0.001; and Glu concentrations: 6.560 ± 0.292 µmol/g vs. 7.133 ± 0.397 µmol/g, p = 0.001). Our results indicate that GluCEST imaging is a distinctive approach to detecting and monitoring Glu levels in a rat model of depression. Furthermore, the application of GluCEST imaging may provide a deeper insight into the neurochemical involvement of glutamate in various psychiatric disorders.

Study of multistep Dense U-Net-based automatic segmentation for head MRI scans.

BACKGROUND: Despite extensive efforts to obtain accurate segmentation of magnetic resonance imaging (MRI) scans of a head, it remains challenging primarily due to variations in intensity distribution, which depend on the equipment and parameters used. PURPOSE: The goal of this study is to evaluate the effectiveness of an automatic segmentation method for head MRI scans using a multistep Dense U-Net (MDU-Net) architecture. METHODS: The MDU-Net-based method comprises two steps. The first step is to segment the scalp, skull, and whole brain from head MRI scans using a convolutional neural network (CNN). In the first step, a hybrid network is used to combine 2.5D Dense U-Net and 3D Dense U-Net structure. This hybrid network acquires logits in three orthogonal planes (axial, coronal, and sagittal) using 2.5D Dense U-Nets and fuses them by averaging. The resultant fused probability map with head MRI scans then serves as the input to a 3D Dense U-Net. In this process, different ratios of active contour loss and focal loss are applied. The second step is to segment the cerebrospinal fluid (CSF), white matter, and gray matter from extracted brain MRI scans using CNNs. In the second step, the histogram of the extracted brain MRI scans is standardized and then a 2.5D Dense U-Net is used to further segment the brain's specific tissues using the focal loss. A dataset of 100 head MRI scans from an OASIS-3 dataset was used for training, internal validation, and testing, with ratios of 80%, 10%, and 10%, respectively. Using the proposed approach, we segmented the head MRI scans into five areas (scalp, skull, CSF, white matter, and gray matter) and evaluated the segmentation results using the Dice similarity coefficient (DSC) score, Hausdorff distance (HD), and the average symmetric surface distance (ASSD) as evaluation metrics. We compared these results with those obtained using the Res-U-Net, Dense U-Net, U-Net++, Swin-Unet, and H-Dense U-Net models. RESULTS: The MDU-Net model showed DSC values of 0.933, 0.830, 0.833, 0.953, and 0.917 in the scalp, skull, CSF, white matter, and gray matter, respectively. The corresponding HD values were 2.37, 2.89, 2.13, 1.52, and 1.53 mm, respectively. The ASSD values were 0.50, 1.63, 1.28, 0.26, and 0.27 mm, respectively. Comparing these results with other models revealed that the MDU-Net model demonstrated the best performance in terms of the DSC values for the scalp, CSF, white matter, and gray matter. When compared with the H-Dense U-Net model, which showed the highest performance among the other models, the MDU-Net model showed substantial improvements in the HD view, particularly in the gray matter region, with a difference of approximately 9%. In addition, in terms of the ASSD, the MDU-Net model outperformed the H-Dense U-Net model, showing an approximately 7% improvements in the white matter and approximately 9% improvements in the gray matter. CONCLUSION: Compared with existing models in terms of DSC, HD, and ASSD, the proposed MDU-Net model demonstrated the best performance on average and showed its potential to enhance the accuracy of automatic segmentation for head MRI scans.

Radiologist's Guide to Evaluating Publications of Clinical Research on AI: How We Do It.

Literacy in research studies of artificial intelligence (AI) has become an important skill for radiologists. It is required to make a proper assessment of the validity, reproducibility, and clinical applicability of AI studies. However, AI studies are generally perceived to be more difficult for clinician readers to evaluate than traditional clinical research studies. This special report-as an effective, concise guide for readers-aims to assist clinical radiologists in critically evaluating different types of clinical research articles involving AI. It does not intend to be a comprehensive checklist or methodological summary for complete clinical evaluation of AI or a reporting guideline. Ten key items for readers to check are described, regarding study purpose, function and clinical context of AI, training data, data preprocessing, AI modeling techniques, test data, AI performance, helpfulness and value of AI, interpretability of AI, and code sharing. The important aspects of each item are explained for readers to consider when reading publications on AI clinical research. Evaluating each item can help radiologists assess the validity, reproducibility, and clinical applicability of clinical research articles involving AI.

Artificial Intelligence in Gastric Cancer Imaging With Emphasis on Diagnostic Imaging and Body Morphometry.

Gastric cancer remains a significant global health concern, coercing the need for advancements in imaging techniques for ensuring accurate diagnosis and effective treatment planning. Artificial intelligence (AI) has emerged as a potent tool for gastric-cancer imaging, particularly for diagnostic imaging and body morphometry. This review article offers a comprehensive overview of the recent developments and applications of AI in gastric cancer imaging. We investigated the role of AI imaging in gastric cancer diagnosis and staging, showcasing its potential to enhance the accuracy and efficiency of these crucial aspects of patient management. Additionally, we explored the application of AI body morphometry specifically for assessing the clinical impact of gastrectomy. This aspect of AI utilization holds significant promise for understanding postoperative changes and optimizing patient outcomes. Furthermore, we examine the current state of AI techniques for the prognosis of patients with gastric cancer. These prognostic models leverage AI algorithms to predict long-term survival outcomes and assist clinicians in making informed treatment decisions. However, the implementation of AI techniques for gastric cancer imaging has several limitations. As AI continues to evolve, we hope to witness the translation of cutting-edge technologies into routine clinical practice, ultimately improving patient care and outcomes in the fight against gastric cancer.

Association between visceral obesity and tumor recurrence in hepatocellular carcinoma recipients undergoing liver transplantation.

BACKGROUND: Excessive visceral obesity in recipients of living donor liver transplantation (LDLT) is associated with mortality, and a recent study reported the correlation between visceral adiposity of male LDLT recipients and hepatocellular carcinoma (HCC) recurrence. However, there is no study on the relationship between the donor's visceral adiposity and surgical outcomes in LDLT recipients. We investigated the association of the visceral-to-subcutaneous fat area ratio (VSR) in donors and recipients with HCC recurrence and mortality in LDLT. METHODS: We analyzed 1386 sets of donors and recipients who underwent LDLT between January 2008 and January 2018. The maximal chi-square method was used to determine the optimal cutoff values for VSR for predicting overall HCC recurrence and mortality. Cox regression analyses were performed to evaluate the association of donor VSR and recipient VSR with overall HCC recurrence and mortality in recipients. RESULTS: The cutoff values of VSR was determined as 0.73 in males and 0.31 in females. High donor VSR was significantly associated with overall HCC recurrence (adjusted hazard ratio [HR]: 1.43, 95% confidence interval [CI]: 1.06-1.93, p = 0.019) and mortality (HR: 1.35, 95% CI: 1.03-1.76, p = 0.030). High recipient VSR was significantly associated with overall HCC recurrence (HR: 1.40, 95% CI: 1.04-1.88, p = 0.027) and mortality (HR: 1.50, 95% CI: 1.14-1.96, p = 0.003). CONCLUSIONS: Both recipient VSR and donor VSR were significant risk factors for HCC recurrence and mortality in LDLT recipients. Preoperative donor VSR and recipient VSR may be strong predictors of the surgical outcomes of LDLT recipients with HCC.

Comprehensive Analysis for Anti-Cancer Target-Indication Prioritization of Placental Growth Factor Inhibitor (PGF) by Use of Omics and Patient Survival Data.

The expression of the placental growth factor (PGF) in cancer cells and the tumor microenvironment can contribute to the induction of angiogenesis, supporting cancer cell metabolism by ensuring an adequate blood supply. Angiogenesis is a key component of cancer metabolism as it facilitates the delivery of nutrients and oxygen to rapidly growing tumor cells. PGF is recognized as a novel target for anti-cancer treatment due to its ability to overcome resistance to existing angiogenesis inhibitors and its impact on the tumor microenvironment. We aimed to integrate bioinformatics evidence using various data sources and analytic tools for target-indication identification of the PGF target and prioritize the indication across various cancer types as an initial step of drug development. The data analysis included PGF gene function, molecular pathway, protein interaction, gene expression and mutation across cancer type, survival prognosis and tumor immune infiltration association with PGF. The overall evaluation was conducted given the totality of evidence, to target the PGF gene to treat the cancer where the PGF level was highly expressed in a certain tumor type with poor survival prognosis as well as possibly associated with poor tumor infiltration level. PGF showed a significant impact on overall survival in several cancers through univariate or multivariate survival analysis. The cancers considered as target diseases for PGF inhibitors, due to their potential effects on PGF, are adrenocortical carcinoma, kidney cancers, liver hepatocellular carcinoma, stomach adenocarcinoma, and uveal melanoma.

The effects of muscle mass and quality on mortality of patients with acute kidney injury requiring continuous renal replacement therapy.

This study examined the effects of muscle mass on mortality in patients with acute kidney injury requiring continuous renal replacement therapy. It was conducted in eight medical centers between 2006 and 2021. The data of 2200 patients over the age of 18 years with acute kidney injury who required continuous renal replacement therapy were retrospectively collected. Skeletal muscle areas, categorized into normal and low attenuation muscle areas, were obtained from computed tomography images at the level of the third lumbar vertebra. Cox proportional hazards models were used to investigate the association between mortality within 1, 3, and 30 days and skeletal muscle index. Sixty percent of patients were male, and the 30-day mortality rate was 52%. Increased skeletal muscle areas/body mass index was associated with decreased mortality risk. We also identified a 26% decreased risk of low attenuation muscle area/body mass index on mortality. We established that muscle mass had protective effects on the mortality of patients with acute kidney injury requiring continuous renal replacement therapy. This study showed that muscle mass is a significant determinant of mortality, even if the density is low.

Prognostic artificial intelligence model to predict 5 year survival at 1 year after gastric cancer surgery based on nutrition and body morphometry.

BACKGROUND: Personalized survival prediction is important in gastric cancer patients after gastrectomy based on large datasets with many variables including time-varying factors in nutrition and body morphometry. One year after gastrectomy might be the optimal timing to predict long-term survival because most patients experience significant nutritional change, muscle loss, and postoperative changes in the first year after gastrectomy. We aimed to develop a personalized prognostic artificial intelligence (AI) model to predict 5 year survival at 1 year after gastrectomy. METHODS: From a prospectively built gastric surgery registry from a tertiary hospital, 4025 gastric cancer patients (mean age 56.1 ± 10.9, 36.2% females) treated gastrectomy and survived more than a year were selected. Eighty-nine variables including clinical and derived time-varying variables were used as input variables. We proposed a multi-tree extreme gradient boosting (XGBoost) algorithm, an ensemble AI algorithm based on 100 datasets derived from repeated five-fold cross-validation. Internal validation was performed in split datasets (n = 1121) by comparing our proposed model and six other AI algorithms. External validation was performed in 590 patients from other hospitals (mean age 55.9 ± 11.2, 37.3% females). We performed a sensitivity analysis to analyse the effect of the nutritional and fat/muscle indices using a leave-one-out method. RESULTS: In the internal validation, our proposed model showed AUROC of 0.8237, which outperformed the other AI algorithms (0.7988-0.8165), 80.00% sensitivity, 72.34% specificity, and 76.17% balanced accuracy. In the external validation, our model showed AUROC of 0.8903, 86.96% sensitivity, 74.60% specificity, and 80.78% balanced accuracy. Sensitivity analysis demonstrated that the nutritional and fat/muscle indices influenced the balanced accuracy by 0.31% and 6.29% in the internal and external validation set, respectively. Our developed AI model was published on a website for personalized survival prediction. CONCLUSIONS: Our proposed AI model provides substantially good performance in predicting 5 year survival at 1 year after gastric cancer surgery. The nutritional and fat/muscle indices contributed to increase the prediction performance of our AI model.

Impact of Sarcopenia on Clinical Course of Inflammatory Bowel Disease in Korea.

BACKGROUND AND AIMS: Reduced body muscle mass is a poor prognostic factor for inflammatory bowel disease (IBD). In this study, we investigated the prevalence of sarcopenia at diagnosis and its clinical significance in Korean patients with IBD. METHODS: The prevalence of sarcopenia in IBD patients between June 1989 and December 2016 was investigated using a well-characterized referral center-based cohort. Abdominopelvic computed tomography within six months from IBD diagnosis was used for the evaluation. Sarcopenia was defined as an L3 skeletal muscle index of < 49 cm2/m2 for male and < 31 cm2/m2 for female. The clinical characteristics and outcomes were evaluated with respect to sarcopenia. RESULTS: A total of 1,027 patients (854 Crohn's disease [CD]; 173 ulcerative colitis [UC]) were evaluated. Sarcopenia was found in 56.8% of the population (CD, 57.5%; UC, 53.2%), and male were more likely to be sarcopenic (CD, 94.3%; UC, 91.6%). There were no significant differences in the cumulative risk of using steroids, immunomodulators, biologics, and bowel resections (or colectomy) with or without sarcopenia during follow-up (median: CD, 5.8 years; UC, 3.7 years). In sarcopenic patients with CD, there was a significantly higher cumulative risk of perianal surgeries than in non-sarcopenic patients with CD (Log-rank test; P = 0.001). However, the risk of perianal surgeries was not significant in multivariate analysis (Odds ratio 1.368; 95% confidence interval 0.782-2.391; P = 0.272). CONCLUSION: Sarcopenia at diagnosis may have no significant prognostic value for medical treatment and bowel resection, but it may be associated with perianal CD.

Association of sex-specific donor skeletal muscle index with surgical outcomes in living donor liver transplantation recipients.

BACKGROUND: A recent study reported a correlation between the muscle mass of male donors and graft failure in living donor liver transplantation (LDLT) recipients. We investigated the association of sex-specific donor skeletal muscle index (SMI) with mortality and graft failure in LDLT recipients. METHODS: We retrospectively analysed 2750 sets of donors and recipients between January 2008 and January 2018. The recipient outcomes were analysed by dividing the data according to donor sex. Cox regression analyses were performed to evaluate the association between donor SMI by sex and 1-year mortality and graft failure in recipients. RESULTS: In the male donor group, robust donor (increased SMI) was significantly associated with higher risks for mortality (hazard ratio [HR]: 1.03, 95% confidence interval [CI]: 1.00-1.06, p = .023) and graft failure (HR: 1.04, 95% CI: 1.01-1.06, p = .007) at 1 year. In the female donor group, the robust donor was significantly associated with lower risks for mortality (HR: 0.92, 95% CI: 0.87-0.97, p = .003) and graft failure (HR: 0.95, 95% CI: 0.90-1.00, p = .032) at 1 year. CONCLUSIONS: Donor SMI was associated with surgical outcomes in recipients. Robust male and female donors were a significant negative and protective factor for grafts respectively.

Deep Learning-Based Automatic Detection and Grading of Motion-Related Artifacts on Gadoxetic Acid-Enhanced Liver MRI.

OBJECTIVES: The aim of this study was to develop and validate a deep learning-based algorithm (DLA) for automatic detection and grading of motion-related artifacts on arterial phase liver magnetic resonance imaging (MRI). MATERIALS AND METHODS: Multistep DLA for detection and grading of motion-related artifacts, based on the modified ResNet-101 and U-net, were trained using 336 arterial phase images of gadoxetic acid-enhanced liver MRI examinations obtained in 2017 (training dataset; mean age, 68.6 years [range, 18-95]; 254 men). Motion-related artifacts were evaluated in 4 different MRI slices using a 3-tier grading system. In the validation dataset, 313 images from the same institution obtained in 2018 (internal validation dataset; mean age, 67.2 years [range, 21-87]; 228 men) and 329 from 3 different institutions (external validation dataset; mean age, 64.0 years [range, 23-90]; 214 men) were included, and the per-slice and per-examination performances for the detection of motion-related artifacts were evaluated. RESULTS: The per-slice sensitivity and specificity of the DLA for detecting grade 3 motion-related artifacts were 91.5% (97/106) and 96.8% (1134/1172) in the internal validation dataset and 93.3% (265/284) and 91.6% (948/1035) in the external validation dataset. The per-examination sensitivity and specificity were 92.0% (23/25) and 99.7% (287/288) in the internal validation dataset and 90.0% (72/80) and 96.0% (239/249) in the external validation dataset, respectively. The processing time of the DLA for automatic grading of motion-related artifacts was from 4.11 to 4.22 seconds per MRI examination. CONCLUSIONS: The DLA enabled automatic and instant detection and grading of motion-related artifacts on arterial phase gadoxetic acid-enhanced liver MRI.

Sequence-Type Classification of Brain MRI for Acute Stroke Using a Self-Supervised Machine Learning Algorithm.

We propose a self-supervised machine learning (ML) algorithm for sequence-type classification of brain MRI using a supervisory signal from DICOM metadata (i.e., a rule-based virtual label). A total of 1787 brain MRI datasets were constructed, including 1531 from hospitals and 256 from multi-center trial datasets. The ground truth (GT) was generated by two experienced image analysts and checked by a radiologist. An ML framework called ImageSort-net was developed using various features related to MRI acquisition parameters and used for training virtual labels and ML algorithms derived from rule-based labeling systems that act as labels for supervised learning. For the performance evaluation of ImageSort-net (MLvirtual), we compare and analyze the performances of models trained with human expert labels (MLhumans), using as a test set blank data that the rule-based labeling system failed to infer from each dataset. The performance of ImageSort-net (MLvirtual) was comparable to that of MLhuman (98.5% and 99%, respectively) in terms of overall accuracy when trained with hospital datasets. When trained with a relatively small multi-center trial dataset, the overall accuracy was relatively lower than that of MLhuman (95.6% and 99.4%, respectively). After integrating the two datasets and re-training them, MLvirtual showed higher accuracy than MLvirtual trained only on multi-center datasets (95.6% and 99.7%, respectively). Additionally, the multi-center dataset inference performances after the re-training of MLvirtual and MLhumans were identical (99.7%). Training of ML algorithms based on rule-based virtual labels achieved high accuracy for sequence-type classification of brain MRI and enabled us to build a sustainable self-learning system.

Serial Changes in Body Composition and the Association with Disease Activity during Treatment in Patients with Crohn's Disease.

Objectives: To analyze serial changes in body composition and investigate the association between body composition changes and disease activity changes in patients with Crohn's disease (CD). Methods: Seventy-one patients with CD who had been treated and followed-up at our institution were included. Two to four computed tomography images were acquired at baseline, and the 2−5-year, 5−8-year, and last follow-ups were selected per patient for body composition and disease activity analyses. Visceral fat area (VFA), skeletal muscle index (SMI; skeletal muscle area/height2), and subcutaneous fat area (SFA) were assessed using an artificial-intelligence-driven fully automated method. Disease activity was assessed using a modified computed tomography scoring system and the Simple Endoscopic Score for Crohn's Disease. The associations between body composition, disease activity, and remission were investigated. Results: The mean age was 29.83 ± 11.27 years; most patients were men (48/71, 67.6%); and the median follow-up was 144 (12−264) months. Overall, VFA and SFA gradually increased, while SMI decreased during the follow-up. Sarcopenia was associated with the female sex, higher disease activities at baseline (p = 0.01) and the last follow-up (p = 0.001). SMI and SFA inversely correlated with the disease activity, i.e., the more severe the disease activity, the lower the SMI and SFA (p < 0.05). SMI at the last follow-up was the only significant predictor of remission (OR = 1.21, 95% confidence interval: 1.03−1.42, p = 0.021). Conclusion: SMI decreased while VFA and SFA increased during the treatment follow-up in patients with CD. Sarcopenia was associated with higher disease activity, and SMI and SFA inversely correlated with disease activity. SMI at the last follow-up was the significant factor for remission.

Lifestyle interventions after colorectal cancer surgery using a mobile digital device: A study protocol for a randomized controlled trial.

BACKGROUND: In treating colorectal cancer, surgical techniques and adjuvant treatments have advanced over the past century, but relatively less attention has been given to improve health-related quality of life (HRQOL). Recent studies report a significant association between cancer recurrence and patient lifestyle after surgery, hence emphasizing the need to assist patients to reduce this risk through appropriate lifestyle choices. The proposed study will evaluate the effects of digital interventions on lifestyle after surgery for colorectal cancer using mobile applications. METHODS: A randomized controlled trial design was proposed. A total of 320 patients diagnosed with colorectal cancer aged between 20 and 70 years were to be enrolled and randomized in equal numbers into 4 groups (3 groups assigned to different mobile applications and a control group). Surveys that evaluate HRQOL, physical measurements, and metabolic parameters (fasting glucose, hemoglobin A1C, triglyceride, high-density lipoprotein cholesterol), and fat/muscle mass measurements by abdominal computed tomography (CT), will be conducted prior to surgery and every 6 months post-surgery for 18 months. Statistical analysis will be used to compare the outcomes between groups. DISCUSSION: Results from this study could provide evidence that easily accessible mobile applications can influence patient lifestyles. Results showing minimal effects of such applications could also be constructive for improving healthcare-related applications.

Prognostic Value of Sarcopenia and Myosteatosis in Patients with Resectable Pancreatic Ductal Adenocarcinoma.

OBJECTIVE: The clinical relevance of myosteatosis has not been well evaluated in patients with pancreatic ductal adenocarcinoma (PDAC), although sarcopenia has been extensively researched. Therefore, we evaluated the prognostic value of muscle quality, including myosteatosis, in patients with resectable PDAC treated surgically. MATERIALS AND METHODS: We retrospectively evaluated 347 patients with resectable PDAC who underwent curative surgery (mean age ± standard deviation, 63.6 ± 9.6 years; 202 male). Automatic muscle segmentation was performed on preoperative computed tomography (CT) images using an artificial intelligence program. A single axial image of the portal phase at the inferior endplate level of the L3 vertebra was used for analysis in each patient. Sarcopenia was evaluated using the skeletal muscle index, calculated as the skeletal muscle area (SMA) divided by the height squared. The mean SMA attenuation was used to evaluate myosteatosis. Diagnostic cutoff values for sarcopenia and myosteatosis were devised using the Contal and O'Quigley methods, and patients were classified according to normal (nMT), sarcopenic (sMT), myosteatotic (mMT), or combined (cMT) muscle quality types. Multivariable Cox regression analyses were conducted to assess the effects of muscle type on the overall survival (OS) and recurrence-free survival (RFS) after surgery. RESULTS: Eighty-four (24.2%), 73 (21.0%), 75 (21.6%), and 115 (33.1%) patients were classified as having nMT, sMT, mMT, and cMT, respectively. Compared to nMT, mMT and cMT were significantly associated with poorer OS, with hazard ratios (HRs) of 1.49 (95% confidence interval, 1.00-2.22) and 1.68 (1.16-2.43), respectively, while sMT was not (HR of 1.40 [0.94-2.10]). Only mMT was significantly associated with poorer RFS, with an HR of 1.59 (1.07-2.35), while sMT and cMT were not. CONCLUSION: Myosteatosis was associated with poor OS and RFS in patients with resectable PDAC who underwent curative surgery.

Evolution of Radiological Treatment Response Assessments for Cancer Immunotherapy: From iRECIST to Radiomics and Artificial Intelligence.

Immunotherapy has revolutionized and opened a new paradigm for cancer treatment. In the era of immunotherapy and molecular targeted therapy, precision medicine has gained emphasis, and an early response assessment is a key element of this approach. Treatment response assessment for immunotherapy is challenging for radiologists because of the rapid development of immunotherapeutic agents, from immune checkpoint inhibitors to chimeric antigen receptor-T cells, with which many radiologists may not be familiar, and the atypical responses to therapy, such as pseudoprogression and hyperprogression. Therefore, new response assessment methods such as immune response assessment, functional/molecular imaging biomarkers, and artificial intelligence (including radiomics and machine learning approaches) have been developed and investigated. Radiologists should be aware of recent trends in immunotherapy development and new response assessment methods.

Transient severe motion artifacts on gadoxetic acid-enhanced MRI: risk factor analysis in 2230 patients.

OBJECTIVES: To determine risk factors for transient severe motion (TSM) artifact on arterial phase of gadoxetic acid-enhanced MRI using a large cohort. METHODS: A total of 2230 patients who underwent gadoxetic acid-enhanced MRI was consecutively included. Two readers evaluated respiratory motion artifact on arterial phase images using a 5-point grading scale. Clinical factors including demographic data, underlying disease, laboratory data, presence of ascites and pleural effusion, and previous experience of gadoxetic acid-enhanced MRI were investigated. Univariable and multivariable logistic regression analyses were performed to determine significant risk factors for TSM. Predictive value of TSM was calculated according to the number of significant risk factors. RESULTS: Overall incidence of TSM was 5.0% (111/2230). In the multivariable analysis, old age (≥ 65 years; odds ratio [OR] = 2.01 [95% CI, 1.31-3.07]), high body mass index (≥ 25 kg/m2; OR = 1.76 [1.18-2.63]), chronic obstructive pulmonary disease (OR = 6.11 [2.32-16.04]), and moderate to severe pleural effusion (OR = 3.55 [1.65-7.65]) were independent significant risk factors for TSM. Presence of hepatitis B (OR = 0.66 [0.43-0.99]) and previous experience of gadoxetic acid-enhanced MRI (OR = 0.52 [0.33-0.83]) were negative risk factors for TSM. When at least one of the significant factors was present, the predictive risk was 5.7% (109/1916), whereas it was 16.3% (17/104) when at least four factors were present. CONCLUSION: Knowing risk factors for transient severe motion artifact on gadoxetic acid-enhanced MRI can be clinically useful for providing diagnostic strategies more tailored to individual patients. KEY POINTS: • Old age, high body mass index, chronic obstructive pulmonary disease, and moderate to severe pleural effusion were independent risk factors for transient severe motion artifact on gadoxetic acid-enhanced MRI. • Patients with hepatitis B or previous experience of gadoxetic acid-enhanced MRI were less likely to show transient severe motion artifact. • As the number of risk factors for transient severe motion artifact increased, the predicted risk for it also showed a tendency to increase.

Impact of Baseline Muscle Mass and Myosteatosis on the Development of Early Toxicity During First-Line Chemotherapy in Patients With Initially Metastatic Pancreatic Cancer.

Objectives: Although chemotherapy is the only treatment option for metastatic pancreatic cancer (PDAC), patients frequently encounter adverse events during chemotherapy leading deterioration of patients' quality of life and treatment interruption. We evaluated the role of baseline CT-assessed body composition in predicting early toxicity during first cycle of the first-line chemotherapy in patients with metastatic PDAC. Methods: This retrospective study included 636 patients with initially metastatic PDAC who underwent first-line chemotherapy from January 2009 to December 2019. Chemotherapy regimen, baseline laboratory data, and body composition parameters acquired from baseline CT were obtained. The skeletal muscle index (SMI) was used to identify patients with a low muscle mass (SMI < 41 cm2/m2 for women, and < 43 cm2/m2 [body mass index < 25 cm/kg2] or < 53 cm2/m2 [body mass index ≥ 25 cm/kg2] for men), and myosteatosis was defined as low-attenuated muscle area divided by skeletal muscle area (LAMA/SMA index) ≥ 20%. Univariate and multivariable binary logistic regression analyses were performed using bootstrapping with 500 interactions to identify predictors of grade 3-4 toxicity and any treatment-modifying toxicity which led to a dose reduction, delayed administration, drug skip or discontinuation. Results: During the first cycle of the first-line chemotherapy, grade 3-4 toxicity and treatment-modifying toxicity occurred in 160 patients (25.2%) and in 247 patients (38.8%), respectively. The presence of both low muscle mass and myosteatosis was significantly associated with the occurrence of both grade 3-4 toxicity (odd ratio [OR], 1.73; 95% confidence interval [CI], 1.14-2.63) and treatment-modifying toxicity (OR, 1.83; 95% CI, 1.26-2.66) whereas low muscle mass alone did not. Conclusions: The presence of both low muscle mass and myosteatosis assessed on baseline CT may be used to predict early chemotherapy-related toxicity in patients with metastatic PDAC.