Is aryl hydrocarbon receptor antagonism after ischemia effective in alleviating acute hepatic ischemia-reperfusion injury in rats?

Aryl hydrocarbon receptors (AhRs) have been reported to be important mediators of ischemic injury in the brain. Furthermore, the pharmacological inhibition of AhR activation after ischemia has been shown to attenuate cerebral ischemia-reperfusion (IR) injury. Here, we investigated whether AhR antagonist administration after ischemia was also effective in ameliorating hepatic IR injury. A 70% partial hepatic IR (45-min ischemia and 24-h reperfusion) injury was induced in rats. We administered 6,2',4'-trimethoxyflavone (TMF, 5 mg/kg) intraperitoneally 10 min after ischemia. Hepatic IR injury was observed using serum, magnetic resonance imaging-based liver function indices, and liver samples. TMF-treated rats showed significantly lower relative enhancement (RE) values and serum alanine aminotransferase (ALT) and aspartate aminotransferase levels than did untreated rats at 3 h after reperfusion. After 24 h of reperfusion, TMF-treated rats had significantly lower RE values, ΔT1 values, serum ALT levels, and necrotic area percentage than did untreated rats. The expression of the apoptosis-related proteins, Bax and cleaved caspase-3, was significantly lower in TMF-treated rats than in untreated rats. This study demonstrated that inhibition of AhR activation after ischemia was effective in ameliorating IR-induced liver injury in rats.

The effect of hepatic steatosis on liver volume determined by proton density fat fraction and deep learning-measured liver volume.

OBJECTIVES: We aimed to evaluate the effect of hepatic steatosis (HS) on liver volume and to develop a formula to estimate lean liver volume correcting the HS effect. METHODS: This retrospective study included healthy adult liver donors who underwent gadoxetic acid-enhanced MRI and proton density fat fraction (PDFF) measurement from 2015 to 2019. The degree of HS was graded at 5% PDFF intervals from grade 0 (no HS; PDFF < 5.5%). Liver volume was measured with hepatobiliary phase MRI using deep learning algorithm, and standard liver volume (SLV) was calculated as the reference lean liver volume. The association between liver volume and SLV ratio with PDFF grades was evaluated using Spearman's correlation (ρ). The effect of PDFF grades on liver volume was evaluated using the multivariable linear regression model. RESULTS: The study population included 1038 donors (mean age, 31 ± 9 years; 689 men). Mean liver volume to SLV ratio increased according to PDFF grades (ρ = 0.234, p < 0.001). The multivariable analysis indicated that SLV (ß = 1.004, p < 0.001) and PDFF grade*SLV (ß = 0.044, p < 0.001) independently affected liver volume, suggesting a 4.4% increase in liver volume per one-point increment in the PDFF grade. PDFF-adjusted lean liver volume was estimated using the formula, liver volume/[1.004 + 0.044 × PDFF grade]. The mean estimated lean liver volume to SLV ratio approximated to one for all PDFF grades, with no significant association with PDFF grades (p = 0.851). CONCLUSION: HS increases liver volume. The formula to estimate lean liver volume may be useful to adjust for the effect of HS on liver volume. KEY POINTS: • Hepatic steatosis increases liver volume. • The presented formula to estimate lean liver volume using MRI-measured proton density fat fraction and liver volume may be useful to adjust for the effect of hepatic steatosis on measured liver volume.

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.

Prediction of Decompensation and Death in Advanced Chronic Liver Disease Using Deep Learning Analysis of Gadoxetic Acid-Enhanced MRI.

OBJECTIVE: This study aimed to evaluate the usefulness of quantitative indices obtained from deep learning analysis of gadoxetic acid-enhanced hepatobiliary phase (HBP) MRI and their longitudinal changes in predicting decompensation and death in patients with advanced chronic liver disease (ACLD). MATERIALS AND METHODS: We included patients who underwent baseline and 1-year follow-up MRI from a prospective cohort that underwent gadoxetic acid-enhanced MRI for hepatocellular carcinoma surveillance between November 2011 and August 2012 at a tertiary medical center. Baseline liver condition was categorized as non-ACLD, compensated ACLD, and decompensated ACLD. The liver-to-spleen signal intensity ratio (LS-SIR) and liver-to-spleen volume ratio (LS-VR) were automatically measured on the HBP images using a deep learning algorithm, and their percentage changes at the 1-year follow-up (ΔLS-SIR and ΔLS-VR) were calculated. The associations of the MRI indices with hepatic decompensation and a composite endpoint of liver-related death or transplantation were evaluated using a competing risk analysis with multivariable Fine and Gray regression models, including baseline parameters alone and both baseline and follow-up parameters. RESULTS: Our study included 280 patients (153 male; mean age ± standard deviation, 57 ± 7.95 years) with non-ACLD, compensated ACLD, and decompensated ACLD in 32, 186, and 62 patients, respectively. Patients were followed for 11-117 months (median, 104 months). In patients with compensated ACLD, baseline LS-SIR (sub-distribution hazard ratio [sHR], 0.81; p = 0.034) and LS-VR (sHR, 0.71; p = 0.01) were independently associated with hepatic decompensation. The ΔLS-VR (sHR, 0.54; p = 0.002) was predictive of hepatic decompensation after adjusting for baseline variables. ΔLS-VR was an independent predictor of liver-related death or transplantation in patients with compensated ACLD (sHR, 0.46; p = 0.026) and decompensated ACLD (sHR, 0.61; p = 0.023). CONCLUSION: MRI indices automatically derived from the deep learning analysis of gadoxetic acid-enhanced HBP MRI can be used as prognostic markers in patients with ACLD.

Preclinical Long-term Magnetic Resonance Imaging Study of Silymarin Liver-protective Effects.

Background and Aims: Efficacy evaluations with preclinical magnetic resonance imaging (MRI) are uncommon, but MRI in the preclinical phase of drug development provides information that is useful for longitudinal monitoring. The study aim was to monitor the protective effectiveness of silymarin with multiparameter MRI and biomarkers in a thioacetamide (TAA)-induced model of liver injury in rats. Correlation analysis was conducted to assess compare the monitoring of liver function by MRI and biomarkers. Methods: TAA was injected three times a week for 8 weeks to generate a disease model (TAA group). In the TAA and silymarin-treated (TAA-SY) groups, silymarin was administered three times weekly from week 4. MR images were acquired at 0, 2, 4, 6, and 8 weeks in the control, TAA, and TAA-SY groups. Results: The area under the curve to maximum time (AUCtmax) and T2* values of the TAA group decreased over the study period, but the serological markers of liver abnormality increased significantly more than those in the control group. In the TAA-SY group, MRI and serological biomarkers indicated attenuation of liver function as in the TAA group. However, pattern changes were observed from week 6 to comparable levels in the control group with silymarin treatment. Negative correlations between either AUCtmax or T2* values and the serological biomarkers were observed. Conclusions: Silymarin had hepatoprotective effects on TAA-induced liver injury and demonstrated the usefulness of multiparametric MRI to evaluate efficacy in preclinical studies of liver drug development.

Development and validation of an ensemble artificial intelligence model for comprehensive imaging quality check to classify body parts and contrast enhancement.

BACKGROUND: Despite the dramatic increase in the use of medical imaging in various therapeutic fields of clinical trials, the first step of image quality check (image QC), which aims to check whether images are uploaded appropriately according to the predefined rules, is still performed manually by image analysts, which requires a lot of manpower and time. METHODS: In this retrospective study, 1669 computed tomography (CT) images with five specific anatomical locations were collected from Asan Medical Center and Kangdong Sacred Heart Hospital. To generate the ground truth, two radiologists reviewed the anatomical locations and presence of contrast enhancement using the collected data. The individual deep learning model is developed through InceptionResNetv2 and transfer learning, and we propose Image Quality Check-Net (Image QC-Net), an ensemble AI model that utilizes it. To evaluate their clinical effectiveness, the overall accuracy and time spent on image quality check of a conventional model and ImageQC-net were compared. RESULTS: ImageQC-net body part classification showed excellent performance in both internal (precision, 100%; recall, 100% accuracy, 100%) and external verification sets (precision, 99.8%; recovery rate, 99.8%, accuracy, 99.8%). In addition, contrast enhancement classification performance achieved 100% precision, recall, and accuracy in the internal verification set and achieved (precision, 100%; recall, 100%; accuracy 100%) in the external dataset. In the case of clinical effects, the reduction of time by checking the quality of artificial intelligence (AI) support by analysts 1 and 2 (49.7% and 48.3%, respectively) was statistically significant (p < 0.001). CONCLUSIONS: Comprehensive AI techniques to identify body parts and contrast enhancement on CT images are highly accurate and can significantly reduce the time spent on image quality checks.

Deep Learning-Based Assessment of Functional Liver Capacity Using Gadoxetic Acid-Enhanced Hepatobiliary Phase MRI.

OBJECTIVE: We aimed to develop and test a deep learning algorithm (DLA) for fully automated measurement of the volume and signal intensity (SI) of the liver and spleen using gadoxetic acid-enhanced hepatobiliary phase (HBP)-magnetic resonance imaging (MRI) and to evaluate the clinical utility of DLA-assisted assessment of functional liver capacity. MATERIALS AND METHODS: The DLA was developed using HBP-MRI data from 1014 patients. Using an independent test dataset (110 internal and 90 external MRI data), the segmentation performance of the DLA was measured using the Dice similarity score (DSS), and the agreement between the DLA and the ground truth for the volume and SI measurements was assessed with a Bland-Altman 95% limit of agreement (LOA). In 276 separate patients (male:female, 191:85; mean age ± standard deviation, 40 ± 15 years) who underwent hepatic resection, we evaluated the correlations between various DLA-based MRI indices, including liver volume normalized by body surface area (LVBSA), liver-to-spleen SI ratio (LSSR), MRI parameter-adjusted LSSR (aLSSR), LSSR × LVBSA, and aLSSR × LVBSA, and the indocyanine green retention rate at 15 minutes (ICG-R15), and determined the diagnostic performance of the DLA-based MRI indices to detect ICG-R15 ≥ 20%. RESULTS: In the test dataset, the mean DSS was 0.977 for liver segmentation and 0.946 for spleen segmentation. The Bland-Altman 95% LOAs were 0.08% ± 3.70% for the liver volume, 0.20% ± 7.89% for the spleen volume, -0.02% ± 1.28% for the liver SI, and -0.01% ± 1.70% for the spleen SI. Among DLA-based MRI indices, aLSSR × LVBSA showed the strongest correlation with ICG-R15 (r = -0.54, p < 0.001), with area under receiver operating characteristic curve of 0.932 (95% confidence interval, 0.895-0.959) to diagnose ICG-R15 ≥ 20%. CONCLUSION: Our DLA can accurately measure the volume and SI of the liver and spleen and may be useful for assessing functional liver capacity using gadoxetic acid-enhanced HBP-MRI.

A reliable and robust method for the upper thigh muscle quantification on computed tomography: toward a quantitative biomarker for sarcopenia.

BACKGROUND: We aimed to evaluate the feasibility of the upper thigh level as a landmark to measure muscle area for sarcopenia assessment on computed tomography (CT). METHODS: In the 116 healthy subjects who performed CT scans covering from mid-abdomen to feet, the skeletal muscle area in the upper thigh level at the inferior tip of ischial tuberosity (SMAUT), the mid-thigh level (SMAMT), and L3 inferior endplate level (SMAL3) were measured by two independent readers. Pearson correlation coefficients between SMAUT, SMAMT, and SMAL3 were calculated. Inter-reader agreement between the two readers were evaluated using intraclass correlation coefficient (ICC) and Bland-Altman plots with 95% limit of agreement (LOA). RESULTS: In readers 1 and 2, very high positive correlations were observed between SMAUT and SMAMT (r = 0.91 and 0.92, respectively) and between SMAUT and SMAL3 (r = 0.90 and 0.91, respectively), while high positive correlation were observed between SMAMT and SMAL3 (r = 0.87 and 0.87, respectively). Based on ICC values, the inter-reader agreement was the best in the SMAUT (0.999), followed by the SMAL3 (0.990) and SMAMT (0.956). The 95% LOAs in the Bland-Altman plots indicated that the inter-reader agreement of the SMAUT (- 0.462 to 1.513) was the best, followed by the SMAL3 (- 9.949 to 7.636) and SMAMT (- 12.105 to 14.605). CONCLUSION: Muscle area measurement at the upper thigh level correlates well with those with the mid-thigh and L3 inferior endpoint level and shows the highest inter-reader agreement. Thus, the upper thigh level might be an excellent landmark enabling SMAUT as a reliable and robust biomarker for muscle area measurement for sarcopenia assessment.

Development of a fully automatic deep learning system for L3 selection and body composition assessment on computed tomography.

As sarcopenia research has been gaining emphasis, the need for quantification of abdominal muscle on computed tomography (CT) is increasing. Thus, a fully automated system to select L3 slice and segment muscle in an end-to-end manner is demanded. We aimed to develop a deep learning model (DLM) to select the L3 slice with consideration of anatomic variations and to segment cross-sectional areas (CSAs) of abdominal muscle and fat. Our DLM, named L3SEG-net, was composed of a YOLOv3-based algorithm for selecting the L3 slice and a fully convolutional network (FCN)-based algorithm for segmentation. The YOLOv3-based algorithm was developed via supervised learning using a training dataset (n = 922), and the FCN-based algorithm was transferred from prior work. Our L3SEG-net was validated with internal (n = 496) and external validation (n = 586) datasets. Ground truth L3 level CT slice and anatomic variation were identified by a board-certified radiologist. L3 slice selection accuracy was evaluated by the distance difference between ground truths and DLM-derived results. Technical success for L3 slice selection was defined when the distance difference was < 10 mm. Overall segmentation accuracy was evaluated by CSA error and DSC value. The influence of anatomic variations on DLM performance was evaluated. In the internal and external validation datasets, the accuracy of automatic L3 slice selection was high, with mean distance differences of 3.7 ± 8.4 mm and 4.1 ± 8.3 mm, respectively, and with technical success rates of 93.1% and 92.3%, respectively. However, in the subgroup analysis of anatomic variations, the L3 slice selection accuracy decreased, with distance differences of 12.4 ± 15.4 mm and 12.1 ± 14.6 mm, respectively, and with technical success rates of 67.2% and 67.9%, respectively. The overall segmentation accuracy of abdominal muscle areas was excellent regardless of anatomic variation, with CSA errors of 1.38-3.10 cm2. A fully automatic system was developed for the selection of an exact axial CT slice at the L3 vertebral level and the segmentation of abdominal muscle areas.

Liver-to-Spleen Volume Ratio Automatically Measured on CT Predicts Decompensation in Patients with B Viral Compensated Cirrhosis.

OBJECTIVE: Although the liver-to-spleen volume ratio (LSVR) based on CT reflects portal hypertension, its prognostic role in cirrhotic patients has not been proven. We evaluated the utility of LSVR, automatically measured from CT images using a deep learning algorithm, as a predictor of hepatic decompensation and transplantation-free survival in patients with hepatitis B viral (HBV)-compensated cirrhosis. MATERIALS AND METHODS: A deep learning algorithm was used to measure the LSVR in a cohort of 1027 consecutive patients (mean age, 50.5 years; 675 male and 352 female) with HBV-compensated cirrhosis who underwent liver CT (2007-2010). Associations of LSVR with hepatic decompensation and transplantation-free survival were evaluated using multivariable Cox proportional hazards and competing risk analyses, accounting for either the Child-Pugh score (CPS) or Model for End Stage Liver Disease (MELD) score and other variables. The risk of the liver-related events was estimated using Kaplan-Meier analysis and the Aalen-Johansen estimator. RESULTS: After adjustment for either CPS or MELD and other variables, LSVR was identified as a significant independent predictor of hepatic decompensation (hazard ratio for LSVR increase by 1, 0.71 and 0.68 for CPS and MELD models, respectively; p < 0.001) and transplantation-free survival (hazard ratio for LSVR increase by 1, 0.8 and 0.77, respectively; p < 0.001). Patients with an LSVR of < 2.9 (n = 381) had significantly higher 3-year risks of hepatic decompensation (16.7% vs. 2.5%, p < 0.001) and liver-related death or transplantation (10.0% vs. 1.1%, p < 0.001) than those with an LSVR ≥ 2.9 (n = 646). When patients were stratified according to CPS (Child-Pugh A vs. B-C) and MELD (< 10 vs. ≥ 10), an LSVR of < 2.9 was still associated with a higher risk of liver-related events than an LSVR of ≥ 2.9 for all Child-Pugh (p ≤ 0.045) and MELD (p ≤ 0.009) stratifications. CONCLUSION: The LSVR measured on CT can predict hepatic decompensation and transplantation-free survival in patients with HBV-compensated cirrhosis.

Population-based and Personalized Reference Intervals for Liver and Spleen Volumes in Healthy Individuals and Those with Viral Hepatitis.

Background Reference intervals guiding volumetric assessment of the liver and spleen have yet to be established. Purpose To establish population-based and personalized reference intervals for liver volume, spleen volume, and liver-to-spleen volume ratio (LSVR). Materials and Methods This retrospective study consecutively included healthy adult liver donors from 2001 to 2013 (reference group) and from 2014 to 2016 (healthy validation group) and patients with viral hepatitis from 2007 to 2017. Liver volume, spleen volume, and LSVR were measured with CT by using a deep learning algorithm. In the reference group, the reference intervals for the volume indexes were determined by using the population-based (ranges encompassing the central 95% of donors) and personalized (quantile regression modeling of the 2.5th and 97.5th percentiles as a function of age, sex, height, and weight) approaches. The validity of the reference intervals was evaluated in the healthy validation group and the viral hepatitis group. Results The reference and healthy validation groups had 2989 donors (mean age ± standard deviation, 30 years ± 9; 1828 men) and 472 donors (mean age, 30 years ± 9; 334 men), respectively. The viral hepatitis group had 158 patients (mean age, 48 years ± 12; 95 men). The population-based reference intervals were 824.5-1700.0 cm3 for liver volume, 81.1-322.0 cm3 for spleen volume, and 3.96-13.78 for LSVR. Formulae and a web calculator (https://i-pacs.com/calculators) were presented to calculate the personalized reference intervals. In the healthy validation group, both the population-based and personalized reference intervals were used to classify the volume indexes of 94%-96% of the donors as falling within the reference interval. In the viral hepatitis group, when compared with the population-based reference intervals, the personalized reference intervals helped identify more patients with volume indexes outside the reference interval (liver volume, 21.5% [34 of 158] vs 13.3% [21 of 158], P = .01; spleen volume, 29.1% [46 of 158] vs 22.2% [35 of 158], P = .01; LSVR, 35.4% [56 of 158] vs 26.6% [42 of 158], P < .001). Conclusion Reference intervals derived from a deep learning approach in healthy adults may enable evidence-based assessments of liver and spleen volume in clinical practice. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Ringl in this issue.

Effects of Contrast Phases on Automated Measurements of Muscle Quantity and Quality Using CT.

OBJECTIVE: Muscle quantity and quality can be measured with an automated system on CT. However, the effects of contrast phases on the muscle measurements have not been established, which we aimed to investigate in this study. MATERIALS AND METHODS: Muscle quantity was measured according to the skeletal muscle area (SMA) measured by a convolutional neural network-based automated system at the L3 level in 89 subjects undergoing multiphasic abdominal CT comprising unenhanced phase, arterial phase, portal venous phase (PVP), or delayed phase imaging. Muscle quality was analyzed using the mean muscle density and the muscle quality map, which comprises normal and low-attenuation muscle areas (NAMA and LAMA, respectively) based on the muscle attenuation threshold. The SMA, mean muscle density, NAMA, and LAMA were compared between PVP and other phases using paired t tests. Bland-Altman analysis was used to evaluate the inter-phase variability between PVP and other phases. Based on the cutoffs for low muscle quantity and quality, the counts of individuals who scored lower than the cutoff values were compared between PVP and other phases. RESULTS: All indices showed significant differences between PVP and other phases (p < 0.001 for all). The SMA, mean muscle density, and NAMA increased during the later phases, whereas LAMA decreased during the later phases. Bland-Altman analysis showed that the mean differences between PVP and other phases ranged -2.1 to 0.3 cm² for SMA, -12.0 to 2.6 cm² for NAMA, and -2.2 to 9.9 cm² for LAMA.The number of patients who were categorized as low muscle quantity did not significant differ between PVP and other phases (p ≥ 0.5), whereas the number of patients with low muscle quality significantly differed (p ≤ 0.002). CONCLUSION: SMA was less affected by the contrast phases. However, the muscle quality measurements changed with the contrast phases to greater extents and would require a standardization of the contrast phase for reliable measurement.

Updated systematic review and meta-analysis on diagnostic issues and the prognostic impact of myosteatosis: A new paradigm beyond sarcopenia.

Myosteatosis, which is excessive fat infiltration in the skeletal muscle, is now considered a distinct disease from sarcopenia. Advances in imaging technique have made muscle parameters an evaluable biomarker, and many studies have proved association between myosteatosis and aging or disease process. However, the diagnosis and clinical impact of myosteatosis have not been well established. Thus, we aim to provide a systematic summary with a qualitive review of 73 eligible studies regarding these issues. First, the most widely used modality to diagnose myosteatosis is abdominal computed tomography, based on evaluation of the muscle radiodensity of the total abdominal muscle area predominantly at the L3 vertebral level. However, there was significant heterogeneity in the diagnostic methods and cutoff values used to diagnose myosteatosis (32 different cutoff values among 73 studies). Second, the clinical impact of myosteatosis on prognosis was very straightforward, and most studies have shown a negative impact of myosteatosis on overall survival and complications related to underlying diseases. However, the mechanism of the myosteatosis on mortality has not been explored well, and metabolic dysfunction (i.e. insulin resistance, systemic inflammation) would be a possible explanation. Providing systemic review of current issues can elucidate future directions for developing standardized diagnosis and management of myosteatosis.

Intermuscular fat density as a novel prognostic factor in breast cancer patients treated with adjuvant chemotherapy.

BACKGROUND: Body composition, including sarcopenia and fat parameters, has received much attention as a prognostic factor in breast cancer. METHODS: A total of 479 breast cancer patients who underwent surgery and received adjuvant chemotherapy were enrolled in this study. Body composition, including the index and density of skeletal muscle, visceral fat, subcutaneous fat, and intermuscular fat calculated by CT scan, was used as a prognostic factor. The endpoints were breast cancer-specific survival (BCSS) and overall survival (OS). RESULTS: The number of patients with stages I, II, and III was 146 (30.5%), 237 (49.5%), and 96 (20%), respectively. Sarcopenia and muscle density were not significant prognostic factors for BCSS and OS. A high visceral fat index (VFI) was an independent prognostic factor for BCSS (HR, 2.55; 95% CI 1.10-5.95, p = 0.03) and OS (HR 2.55, 95% CI 1.26-5.16, p = 0.01). In addition, high intermuscular fat density (IMFD) was also a significant prognostic factor for BCSS (HR, 2.95; 95% CI 1.34-6.46, p = 0.007) and OS (HR 2.28, 95% CI 1.22-4.26, p = 0.01) in multivariate analysis. CONCLUSION: VFI and IMFD were significant prognostic factors for BCSS and OS in breast cancer patients treated with adjuvant chemotherapy.

Incidence of Immune-Mediated Pseudoprogression of Lymphoma Treated with Immune Checkpoint Inhibitors: Systematic Review and Meta-Analysis.

We evaluated the incidence of pseudoprogression and indeterminate response (IR) in patients with lymphoma treated with immune checkpoint inhibitors (ICIs). A systematic search of PubMed and EMBASE was performed up to 6 February 2021, using the keywords "lymphoma," "immunotherapy," and "pseudoprogression." Random-effects models were used to calculate both pooled incidence of pseudoprogression patients with lymphoma and an IR according to LYRIC criteria, while the Higgins inconsistency index (I2) test and Cochran's Q test were used for heterogeneity. Eight original articles were included, in which the number of patients ranged from 7 to 243. Among the lymphoma patients with ICIs, the pooled incidence of pseudoprogression was 10% (95% confidence interval [CI]: 0.06-0.17). There was no publication bias in Begg's test (p = 0.14). Three articles were analyzed to determine the pooled incidence of pseudoprogression in patients with IR according to LYRIC criteria in a subgroup analysis, which was shown to be 19% (95% CI: 0.08-0.40). A significant proportion (10%) of patients with lymphoma treated with ICIs showed pseudoprogression, and 19% of patients with an IR response showed pseudoprogression and a delayed response. Immune-related response criteria such as LYRIC may be used for patients with lymphoma treated with ICIs.

Histogram analysis of arterial spin labeling perfusion data to determine the human papillomavirus status of oropharyngeal squamous cell carcinomas.

PURPOSE: To evaluate the correlation between histogram parameters derived from pseudo-continuous arterial spin labeling (PCASL) and human papillomavirus (HPV) status in patients with oropharyngeal squamous cell carcinoma (OPSCC). METHODS: This study included a total of 58 patients (HPV-positive: n = 45; -negative: n = 13) from a prospective cohort of consecutive patients aged ≥ 18 years, who were newly diagnosed with oropharyngeal squamous cell carcinoma. All patients were required to have undergone pre-treatment MRI with PCASL to measure regional perfusion. The region of interest was drawn by two radiologists, encompassing the entire tumor volume on all corresponding slices. Differences in the histogram parameters derived from tumor blood flow (TBF) in ASL were assessed for HPV-positive and -negative patients. Receiver operating characteristic curve analysis was performed to determine the best differentiating parameters, and a leave-one-out cross-validation was used. RESULTS: Patients with HPV-positive OPSCC showed a significantly lower overall standard deviation and 95th percentile value of tumor blood flow (P < .007). The standard deviation of TBF was the single best predictive parameter. Leave-one-out cross-validation tests revealed that the area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity were 0.745, 75.9%, 75.6%, and 76.9%, respectively. CONCLUSION: PCASL revealed differences in perfusion parameters according to HPV status in patients with OPSCC, reflecting their distinct histopathology.

CDISC-compliant clinical trial imaging management system with automatic verification and data Transformation: Focusing on tumor response assessment data in clinical trials.

OBJECTIVE: Major issues in imaging data management of tumor response assessment in clinical trials include high human errors in data input and unstandardized data structures, warranting a new breakthrough IT solution. Thus, we aim to develop a Clinical Data Interchange Standards Consortium (CDISC)-compliant clinical trial imaging management system (CTIMS) with automatic verification and transformation modules for implementing the CDISC Study Data Tabulation Model (SDTM) in the tumor response assessment dataset of clinical trials. MATERIALS AND METHODS: In accordance with various CDISC standards guides and Response Evaluation Criteria in Solid Tumors (RECIST) guidelines, the overall system architecture of CDISC-compliant CTIMS was designed. Modules for standard-compliant electronic case report form (eCRF) to verify data conformance and transform into SDTM data format were developed by experts in diverse fields such as medical informatics, medical, and clinical trial. External validation of the CDISC-compliant CTIMS was performed by comparing it with our previous CTIMS based on real-world data and CDISC validation rules by Pinnacle 21 Community Software. RESULTS: The architecture of CDISC-compliant CTIMS included the standard-compliant eCRF module of RECIST, the automatic verification module of the input data, and the SDTM transformation module from the eCRF input data to the SDTM datasets based on CDISC Define-XML. This new system was incorporated into our previous CTIMS. External validation demonstrated that all 176 human input errors occurred in the previous CTIMS filtered by a new system yielding zero error and CDISC-compliant dataset. The verified eCRF input data were automatically transformed into the SDTM dataset, which satisfied the CDISC validation rules by Pinnacle 21 Community Software. CONCLUSIONS: To assure data consistency and high quality of the tumor response assessment data, our new CTIMS can minimize human input error by using standard-compliant eCRF with an automatic verification module and automatically transform the datasets into CDISC SDTM format.

Radiomics and deep learning in liver diseases.

Recently, radiomics and deep learning have gained attention as methods for computerized image analysis. Radiomics and deep learning can perform diagnostic or predictive tasks using high-dimensional image-derived features and have the potential to expand the capabilities of liver imaging beyond the scope of traditional visual image analysis. Recent research has demonstrated the potential of these techniques in various fields of liver imaging, including staging of liver fibrosis, prognostication of malignant liver tumors, automated detection and characterization of liver tumors, automated abdominal organ segmentation, and body composition analysis. However, because most of the previous studies were preliminary and focused mainly on technical feasibility, further clinical validation is required for the application of radiomics and deep learning in clinical practice. In this review, we introduce the technical aspects of radiomics and deep learning and summarize the recent studies on the application of these techniques in liver radiology.

Prognostic Implication of Baseline Sarcopenia for Length of Hospital Stay and Survival in Patients With Coronavirus Disease 2019.

BACKGROUND: The impact of sarcopenia on clinical outcomes of coronavirus disease 2019 (COVID-19) is not clearly determined yet. We aimed to investigate the association between baseline sarcopenia and clinical outcomes in patients with COVID-19. METHODS: All hospitalized adult patients with COVID-19 who had baseline chest computed tomography (CT) scans at a Korean university hospital from February 2020 to May 2020 were included. The main outcome was time from hospital admission to discharge. Death was considered as a competing risk for discharge. Baseline skeletal muscle cross-sectional area at the level of the 12th thoracic vertebra was measured from chest CT scans. The lowest quartile of skeletal muscle index (skeletal muscle cross-sectional area divided by height-squared) was defined as sarcopenia. RESULTS: Of 121 patients (median age, 62 years; 44 men; 29 sarcopenic), 7 patients died and 86 patients were discharged during the 60-day follow-up. Patients with sarcopenia showed a longer time to discharge (median, 55 vs 28 days; p < .001) and a higher incidence of death (17.2% vs 2.2%; p = .004) than those without sarcopenia. Baseline sarcopenia was an independent predictor of delayed hospital discharge (adjusted hazard ratio [aHR], 0.47; 95% confidence interval [95% CI], 0.23-0.96), but was not independently associated with mortality in patients with COVID-19 (aHR, 3.80; 95% CI, 0.48-30.26). The association between baseline sarcopenia and delayed hospital discharge was consistent in subgroups stratified by age, sex, comorbidities, and severity of COVID-19. CONCLUSIONS: Baseline sarcopenia was independently associated with a prolonged hospital stay in patients with COVID-19. Sarcopenia could be a prognostic marker in COVID-19.