Clinical Significance of the Combination of Serum HE4 Levels, Hemoglobin-to-Red Cell Distribution Width Ratio, and CT Imaging for the Pretreatment Assessment of Adnexal Masses.

Background: This study aimed to determine the optimal combination of biomarkers that can predict epithelial ovarian cancer (EOC) and compare the combination with the risk of ovarian malignancy algorithm (ROMA) or Copenhagen index (CPH-I). Methods: Data from 66 patients with EOC and 599 patients with benign ovarian masses who underwent definitive tissue diagnosis of adnexal masses between January 2017 and March 2021 were analyzed. The Mann-Whitney U test or Kruskal-Wallis test was used for between-group comparisons of medians. Logistic regression was used to establish an EOC predictor model. Area under the curve (AUC) comparisons between models were performed using the Delong nonparametric approach. Results: The median age of the patients was 43 years. Twenty-nine (43.9%) patients had early-stage disease (stages I-II) and 37 (56.1%) patients had advanced-stage disease (stages III-IV). The median age, body mass index, white blood cell count, hemoglobin-to-red cell distribution width ratio (HRR), platelet count, platelet-to-lymphocyte ratio, lymphocyte-to-monocyte ratio, serum albumin level, cancer antigen 125, human epididymal secretory protein 4 (HE4), ROMA, and CPH-I were significantly different between the stage I-IV EOC and benign ovarian mass groups. Multivariate logistic regression analysis revealed that HE4, HRR, and computed tomography (CT) imaging were significant predictors of both stages I-IV and I-II EOC. Using these covariates, an interim model (IM) (consisting of HE4 and HRR) and a full model (FM) (consisting of HE4, HRR, and CT imaging) were constructed. When predicting stage I-IV EOC, the AUC of IM was comparable to that of ROMA or CPH-I, whereas the AUC of FM outperformed ROMA or CPH-I. In predicting stage I-II EOC, the AUC of IM was comparable to that of CPH-I but higher than that of ROMA, and the AUC of FM outperformed ROMA or CPH-I. Conclusion: FM outperformed ROMA or CPH-I in predicting stage I-IV EOC and stage I-II EOC. Therefore, FM could be a promising model for improving preoperative prediction of EOC at an early stage. However, further prospective studies are required to validate these results.

Three segments sampling strategy for the assessment of liver steatosis using magnetic resonance imaging proton density fat fraction.

PURPOSE: This research aims to determine the best liver segments representing the whole-liver fat fraction (FF) in magnetic resonance imaging (MRI)-based measurement of the proton density fat fraction (PDFF). METHOD: This retrospective study included 989 adult subjects who underwent MRI-PDFF from March 2018 to January 2021. Three regions of interest (ROI) were measured and averaged for each hepatic segment and the volume-weighted hepatic FF was calculated. Intrahepatic fat variability was assessed by standard deviation between all ROIs. Univariate and multivariate linear regression analyses were done for the factors associated with intrahepatic fat variability among clinical characteristics, blood parameters and the volume-weighted FF. The arithmetic means of specific hepatic segments that were the closest to the volume-weighted FF were identified in all subjects and those with moderate or severe fatty liver. RESULTS: The volume-weighted FF was 8.18% and variability was 1.33%. Volume-weighted FF was the only associated factor with intrahepatic variability. The arithmetic mean of segments V, VI, and IV was closest to the volume-weighted FF in all subjects and in subjects with moderate or severe fatty liver. CONCLUSIONS: There was considerable heterogeneity in hepatic steatosis between each segment of the liver, and the variability was significantly affected by the volume-weighted FF. The mean hepatic FF from segments V, VI, and IV could be used to estimate the volume-weighted FF of the whole liver, not only in the general population but also in patients with moderate or severe fatty liver.

Deep learning-based imaging reconstruction for MRI after neoadjuvant chemoradiotherapy for rectal cancer: effects on image quality and assessment of treatment response.

PURPOSE: To investigate the effects of deep learning-based imaging reconstruction (DLR) on the image quality of MRI of rectal cancer after chemoradiotherapy (CRT), and its accuracy in diagnosing pathological complete responses (pCR). METHODS: We included 39 patients (men: women, 21:18; mean age ± standard deviation, 59.1 ± 9.7 years) with mid-to-lower rectal cancer who underwent a long-course of CRT and high-resolution rectal MRIs between January 2020 and April 2021. Axial T2WI was reconstructed using the conventional method (MRIconv) and DLR with two different noise reduction factors (MRIDLR30 and MRIDLR50). The signal-to-noise ratio (SNR) of the tumor was measured. Two experienced radiologists independently made a blind assessment of the complete response on MRI. The sensitivity and specificity for pCR were analyzed using a multivariable logistic regression analysis with generalized estimating equations. RESULTS: Thirty-four patients did not have a pCR whereas five (12.8%) had pCR. Compared with the SNR of MRIconv (mean ± SD, 7.94 ± 1.92), MRIDLR30 and MRIDLR50 showed higher SNR (9.44 ± 2.31 and 11.83 ± 3.07, respectively) (p < 0.001). Compared to MRIconv, MRIDLR30 and MRIDLR50 showed significantly higher specificity values (p < 0.036) while the sensitivity values were not significantly different (p > 0.301). The sensitivity and specificity for pCR were 48.9% and 80.8% for MRIconv; 48.9% and 88.2% for MRIDLR30; and 38.8% and 86.7% for MRIDLR50, respectively. CONCLUSION: DLR produced MR images with higher resolution and SNR. The specificity of MRI for identification of pCR was significantly higher with DLR than with conventional MRI.

Role of FIB-4 for reassessment of hepatic fibrosis burden in referral center.

Low cut-off of FIB-4 is a widely used formula to exclude advanced liver fibrosis in primary care centers. However, the range of reported threshold of FIB-4 to rule in advanced fibrosis is too broad across etiologies, and no consensus has been reached. In the present study, we investigated the role of FIB-4 for a reassessment of hepatic fibrosis burden in a referral center. We compared the diagnostic performance of FIB-4 among patients with liver disease of various causes and tried to find an optimal cut-off value for predicting advanced fibrosis. Among 1068 patients, the AUROC of FIB-4 to diagnose advanced fibrosis showed no significant difference among the various etiologies of liver disease, ranging from 0.783 to 0.821. The optimal cut-off value obtained by maximizing Youden's index was 2.68, and the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for predicting advanced fibrosis were 70.7%, 79.1%, 43.5%, and 92.2%, respectively. The PPV was low in patients with autoimmune disease (6.67%). When we incorporated the new cut-off of FIB-4 into abdominal ultrasound findings, 81% of unnecessary work-ups would be appropriately avoided. In conclusion, the cut-off value of 2.68 showed an acceptable PPV while maintaining a high NPV to predict advanced fibrosis, most etiology except for autoimmune diseases. This result could assist in establishing an appropriate timing to reassess the hepatic fibrosis burden during monitoring in the referral center.

How to Combine Diffusion-Weighted and T2-Weighted Imaging for MRI Assessment of Pathologic Complete Response to Neoadjuvant Chemoradiotherapy in Patients with Rectal Cancer?

OBJECTIVE: Adequate methods of combining T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) to assess complete response (CR) to chemoradiotherapy (CRT) for rectal cancer are obscure. We aimed to determine an algorithm for combining T2WI and DWI to optimally suggest CR on MRI using visual assessment. MATERIALS AND METHODS: We included 376 patients (male:female, 256:120; mean age ± standard deviation, 59.7 ± 11.1 years) who had undergone long-course CRT for rectal cancer and both pre- and post-CRT high-resolution rectal MRI during 2017-2018. Two experienced radiologists independently evaluated whether a tumor signal was absent, representing CR, on both post-CRT T2WI and DWI, and whether the pre-treatment DWI showed homogeneous hyperintensity throughout the lesion. Algorithms for combining T2WI and DWI were as follows: 'AND,' if both showed CR; 'OR,' if any one showed CR; and 'conditional OR,' if T2WI showed CR or DWI showed CR after the pre-treatment DWI showed homogeneous hyperintensity. Their efficacies for diagnosing pathologic CR (pCR) were determined in comparison with T2WI alone. RESULTS: Sixty-nine patients (18.4%) had pCR. AND had a lower sensitivity without statistical significance (vs. 62.3% [43/69]; 59.4% [41/69], p = 0.500) and a significantly higher specificity (vs. 87.0% [267/307]; 90.2% [277/307], p = 0.002) than those of T2WI. Both OR and conditional OR combinations resulted in a large increase in sensitivity (vs. 62.3% [43/69]; 81.2% [56/69], p < 0.001; and 73.9% [51/69], p = 0.008, respectively) and a large decrease in specificity (vs. 87.0% [267/307]; 57.0% [175/307], p < 0.001; and 69.1% [212/307], p < 0.001, respectively) as compared with T2WI, ultimately creating additional false interpretations of CR more frequently than additional identification of patients with pCR. CONCLUSION: AND combination of T2WI and DWI is an appropriate strategy for suggesting CR using visual assessment of MRI after CRT for rectal cancer.

An index based on deep learning-measured spleen volume on CT for the assessment of high-risk varix in B-viral compensated cirrhosis.

OBJECTIVES: Deep learning enables an automated liver and spleen volume measurements on CT. The purpose of this study was to develop an index combining liver and spleen volumes and clinical factors for detecting high-risk varices in B-viral compensated cirrhosis. METHODS: This retrospective study included 419 patients with B-viral compensated cirrhosis who underwent endoscopy and CT from 2007 to 2008 (derivation cohort, n = 239) and from 2009 to 2010 (validation cohort, n = 180). The liver and spleen volumes were measured on CT images using a deep learning algorithm. Multivariable logistic regression analysis of the derivation cohort developed an index to detect endoscopically confirmed high-risk varix. The cumulative 5-year risk of varix bleeding was evaluated with patients stratified by their index values. RESULTS: The index of spleen volume-to-platelet ratio was devised from the derivation cohort. In the validation cohort, the cutoff index value for balanced sensitivity and specificity (> 3.78) resulted in the sensitivity of 69.4% and the specificity of 78.5% for detecting high-risk varix, and the cutoff index value for high sensitivity (> 1.63) detected all high-risk varices. The index stratified all patients into the low (index value ≤ 1.63; n = 118), intermediate (n = 162), and high (index value > 3.78; n = 139) risk groups with cumulative 5-year incidences of varix bleeding of 0%, 1.0%, and 12.0%, respectively (p < .001). CONCLUSION: The spleen volume-to-platelet ratio obtained using deep learning-based CT analysis is useful to detect high-risk varices and to assess the risk of varix bleeding. KEY POINTS: • The criterion of spleen volume to platelet > 1.63 detected all high-risk varices in the validation cohort, while the absence of visible varix did not exclude all high-risk varices. • Visual varix grade ≥ 2 detected high-risk varix with a high specificity (96.5-100%). • Combining spleen volume-to-platelet ratio ≤ 1.63 and visual varix grade of 0 identified low-risk patients who had no high-risk varix and varix bleeding on 5-year follow-up.