Reference Citation Analysis: Find an Article, Find a Category, Find a Journal, Find a Scholar

For: Ahn Y, Yoon JS, Lee SS, Suk HI, Son JH, Sung YS, Lee Y, Kang BK, Kim HS. Deep Learning Algorithm for Automated Segmentation and Volume Measurement of the Liver and Spleen Using Portal Venous Phase Computed Tomography Images. Korean J Radiol 2020;21:987-997. [PMID: 32677383 PMCID: PMC7369202 DOI: 10.3348/kjr.2020.0237] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Revised: 05/06/2020] [Accepted: 05/11/2020] [Indexed: 02/06/2023] Open

For:	Ahn Y, Yoon JS, Lee SS, Suk HI, Son JH, Sung YS, Lee Y, Kang BK, Kim HS. Deep Learning Algorithm for Automated Segmentation and Volume Measurement of the Liver and Spleen Using Portal Venous Phase Computed Tomography Images. Korean J Radiol 2020;21:987-997. [PMID: 32677383 PMCID: PMC7369202 DOI: 10.3348/kjr.2020.0237] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Revised: 05/06/2020] [Accepted: 05/11/2020] [Indexed: 02/06/2023] Open

Number

Cited by Other Article(s)

LIN M, HUI Y, LI B, ZHAO P, ZHENG Z, YANG Z, SU Z, MENG Y, SONG T. [Application Value of an AI-based Imaging Feature Parameter Model  for Predicting the Malignancy of Part-solid Pulmonary Nodule]. ZHONGGUO FEI AI ZA ZHI = CHINESE JOURNAL OF LUNG CANCER 2025;28:281-290. [PMID: 40404476 PMCID: PMC12096090 DOI: 10.3779/j.issn.1009-3419.2025.102.13] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/18/2025] [Indexed: 05/24/2025]

Abstract

BACKGROUND

Lung cancer is one of the most common malignant tumors worldwide and a major cause of cancer-related deaths. Early-stage lung cancer is often manifested as pulmonary nodules, and accurate assessment of the malignancy risk is crucial for prolonging survival and avoiding overtreatment. This study aims to construct a model based on image feature parameters automatically extracted by artificial intelligence (AI) to evaluate its effectiveness in predicting the malignancy of part-solid nodule (PSN).

METHODS

This retrospective study analyzed 229 PSN from 222 patients who underwent pulmonary nodule resection at Lanzhou University Second Hospital between October 2020 and February 2025. According to pathological results, 45 cases of benign lesions and precursor glandular lesion were categorized into the non-malignant group, and 184 cases of pulmonary malignancies were categorized into the malignant group. All patients underwent preoperative chest computed tomography (CT), and AI software was used to extract imaging feature parameters. Univariate analysis was used to screen significant variables; variance inflation factor (VIF) was calculated to exclude highly collinear variables, and LASSO regression was further applied to identify key features. Multivariate Logistic regression was used to determine independent risk factors. Based on the selected variables, five models were constructed: Logistic regression, random forest, XGBoost, LightGBM, and support vector machine (SVM). Receiver operating characteristic (ROC) curves were used to assess the performance of the models.

RESULTS

The independent risk factors for the malignancy of PSN include roughness (ngtdm), dependence variance (gldm), and short run low gray-level emphasis (glrlm). Logistic regression achieved area under the curves ( AUCs) of 0.86 and 0.89 in the training and testing sets, respectively, showing good performance. XGBoost had AUCs of 0.78 and 0.77, respectively, demonstrating relatively balanced performance, but with lower accuracy. SVM showed an AUC of 0.93 in the training set, which decreased to 0.80 in the testing set, indicating overfitting. LightGBM performed excellently in the training set with an AUC of 0.94, but its performance declined in the testing set, with an AUC of 0.88. In contrast, random forest demonstrated stable performance in both the training and testing sets, with AUCs of 0.89 and 0.91, respectively, exhibiting high stability and excellent generalizability.

CONCLUSIONS

The random forest model constructed based on independent risk factors demonstrated the best performance in predicting the malignancy of PSN and could provide effective auxiliary predictions for clinicians, supporting individualized treatment decisions. .

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Kim HY, Lee KJ, Lee SS, Choi SJ, Kim DH, Heo S, Jang HJ, Choi SH. Diagnosis of moderate-to-severe hepatic steatosis using deep learning-based automated attenuation measurements on contrast-enhanced CT. Abdom Radiol (NY) 2025:10.1007/s00261-025-04872-5. [PMID: 40095018 DOI: 10.1007/s00261-025-04872-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2025] [Revised: 02/26/2025] [Accepted: 03/02/2025] [Indexed: 03/19/2025]

Abstract

PURPOSE

To evaluate the utility of deep learning-based automated attenuation measurements on contrast-enhanced CT (CECT) for diagnosing moderate-to-severe hepatic steatosis (HS), using histology as reference standard.

METHODS

This retrospective study included 3,620 liver donors (2,393 men and 1,227 women; mean age, 31.7 ± 9.4 years), divided into the development (n = 2,714) and test (n = 906) cohorts. Attenuation values of the liver and spleen on CECT were measured both manually and using a deep learning algorithm (before and after radiologists' correction of segmentation errors). Performance of: (1) liver attenuation and (2) liver-spleen attenuation difference for diagnosing moderate-to-severe HS (> 33%) was assessed using the area under the receiver operating characteristic curve (AUC). Three different criteria targeting 95% sensitivity, 95% specificity, and the maximum Youden's index, respectively, for diagnosing moderate-to-severe HS, were developed and validated.

RESULTS

The performance of deep learning-based measurements did not differ significantly, with or without radiologists' corrections (p = 0.13). Liver-spleen attenuation difference outperformed liver attenuation alone in diagnosing moderate-to-severe HS in both deep learning-based (AUC, 0.868 vs. 0.821; p = 0.001) and manual (AUC, 0.871 vs. 0.823; p = 0.001) measurements. In the test cohort, the criterion targeting 95% sensitivity for diagnosing moderate-to-severe HS (liver-spleen attenuation difference ≤ 2.8 HU) yielded 92.0% (69/75) sensitivity and 48.5% (403/831) specificity. The criterion targeting 95% specificity (liver-spleen attenuation difference ≤ -18.8 HU) yielded 53.3% (40/75) sensitivity and 95.7% (795/831) specificity. The criterion targeting the maximum Youden's index (liver-spleen attenuation difference ≤ -8.2 HU) yielded 82.7% (62/75) sensitivity and 80.7% (671/831) specificity.

CONCLUSION

Deep learning-based automated measurements of liver and spleen attenuation on CECT can be used reliably to detect moderate-to-severe HS.

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Park J, Joo I, Jeon SK, Kim JM, Park SJ, Yoon SH. Automated abdominal organ segmentation algorithms for non-enhanced CT for volumetry and 3D radiomics analysis. Abdom Radiol (NY) 2025;50:1448-1456. [PMID: 39299987 PMCID: PMC11821665 DOI: 10.1007/s00261-024-04581-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2024] [Revised: 09/07/2024] [Accepted: 09/09/2024] [Indexed: 09/22/2024]

Abstract

PURPOSE

To develop fully-automated abdominal organ segmentation algorithms from non-enhanced abdominal CT and low-dose chest CT and assess their feasibility for automated CT volumetry and 3D radiomics analysis of abdominal solid organs.

METHODS

Fully-automated nnU-Net-based models were developed to segment the liver, spleen, and both kidneys in non-enhanced abdominal CT, and the liver and spleen in low-dose chest CT. 105 abdominal CTs and 60 low-dose chest CTs were used for model development, and 55 abdominal CTs and 10 low-dose chest CTs for external testing. The segmentation performance for each organ was assessed using the Dice similarity coefficients, with manual segmentation results serving as the ground truth. Agreements between ground-truth measurements and model estimates of organ volume and 3D radiomics features were assessed using the Bland-Altman analysis and intraclass correlation coefficients (ICC).

RESULTS

The models accurately segmented the liver, spleen, right kidney, and left kidney in abdominal CT and the liver and spleen in low-dose chest CT, showing mean Dice similarity coefficients in the external dataset of 0.968, 0.960, 0.952, and 0.958, respectively, in abdominal CT, and 0.969 and 0.960, respectively, in low-dose chest CT. The model-estimated and ground truth volumes of these organs exhibited mean differences between - 0.7% and 2.2%, with excellent agreements. The automatically extracted mean and median Hounsfield units (ICCs, 0.970-0.999 and 0.994-0.999, respectively), uniformity (ICCs, 0.985-0.998), entropy (ICCs, 0.931-0.993), elongation (ICCs, 0.978-0.992), and flatness (ICCs, 0.973-0.997) showed excellent agreement with ground truth measurements for each organ; however, skewness (ICCs, 0.210-0.831), kurtosis (ICCs, 0.053-0.933), and sphericity (ICCs, 0.368-0.819) displayed relatively low and inconsistent agreement.

CONCLUSION

Our nnU-Net-based models accurately segmented abdominal solid organs in non-enhanced abdominal and low-dose chest CT, enabling reliable automated measurements of organ volume and specific 3D radiomics features.

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Heo S, Jeong B, Lee SS, Kim M, Jang HJ, Choi SJ, Kim KM, Ha TY, Jung DH. CT-based detection of clinically significant portal hypertension predicts post-hepatectomy outcomes in hepatocellular carcinoma. Eur Radiol 2025:10.1007/s00330-025-11411-9. [PMID: 39953152 DOI: 10.1007/s00330-025-11411-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2024] [Revised: 12/14/2024] [Accepted: 01/14/2025] [Indexed: 02/17/2025]

Abstract

BACKGROUND

While the CT-based method of detecting clinically significant portal hypertension (CSPH) emerged as a noninvasive alternative for evaluating CSPH, its predictive ability for post-hepatectomy outcomes is unknown. Therefore, this study aimed to evaluate the impact of CT-based CSPH on outcomes following hepatectomy for hepatocellular carcinoma (HCC).

METHODS

This retrospective single-center study included patients with advanced chronic liver disease (ACLD) who underwent hepatectomy for very early or early-stage HCC between January 2017 and December 2018. CSPH was assessed using CT-based criteria, which included splenomegaly determined by deep learning-based spleen volume measurements with personalized reference thresholds, and the presence of gastroesophageal varices (GEV), spontaneous portosystemic shunt or ascites. Logistic regression and competing risk analyses were used to identify factors associated with severe post-hepatectomy liver failure (PHLF), hepatic decompensation, and liver-related death or transplantation. The predictive performance of existing models for PHLF was compared using both CT-based and conventional CSPH criteria (endoscopic GEV or splenomegaly with thrombocytopenia).

RESULTS

Among 593 patients (460 men; mean age 57.9 ± 9.3 years), 41 (6.9%) developed severe PHLF. The median follow-up period was 62 months. CT-based CSPH independently predicted severe PHLF (OR 7.672 [95% CI 3.209-18.346]), hepatic decompensation (subdistribution hazard ratio (sHR) 4.518 [1.868-10.929]), and liver-related death or transplantation (sHR 2.756 [1.315-5.773]). When integrated into existing models, CT-based CSPH outperformed conventional CSPH in predicting severe PHLF (AUC 0.724 vs. 0.694 for EASL algorithm (p = 0.036) and 0.854 vs. 0.830 for Wang's model (p = 0.011)).

CONCLUSIONS

CT-based CSPH is a strong predictor of poor post-hepatectomy outcomes in HCC patients with ACLD, offering a noninvasive surgical risk assessment tool.

KEY POINTS

Question Can CT-based detection of clinically significant portal hypertension (CSPH) serve as a noninvasive predictor of post-hepatectomy outcomes in hepatocellular carcinoma (HCC) patients? Findings CT-based CSPH independently predicted severe post-hepatectomy liver failure, hepatic decompensation, and liver-related death or transplantation, outperforming conventional CSPH criteria in predictive performance. Clinical relevance CT-based CSPH offers a noninvasive and effective tool for surgical risk assessment in HCC patients, potentially improving the selection of candidates for hepatectomy and optimizing patient outcomes.

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Sharbatdaran A, Cohen T, Dev H, Sattar U, Bazojoo V, Wang Y, Hu Z, Zhu C, He X, Romano D, Scandura JM, Prince MR. Model-Assisted Spleen Contouring for Assessing Splenomegaly in Myelofibrosis: A Fast and Reproducible Approach to Evaluate Progression and Treatment Response. J Clin Med 2025;14:443. [PMID: 39860449 PMCID: PMC11766003 DOI: 10.3390/jcm14020443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2025] [Revised: 01/06/2025] [Accepted: 01/07/2025] [Indexed: 01/27/2025] Open

Affiliation(s)

Arman Sharbatdaran Department of Radiology, Weill Cornell Medicine, New York, NY 10022, USA; (A.S.); (T.C.); (H.D.); (U.S.); (Y.W.); (Z.H.); (C.Z.); (X.H.)
Téa Cohen Department of Radiology, Weill Cornell Medicine, New York, NY 10022, USA; (A.S.); (T.C.); (H.D.); (U.S.); (Y.W.); (Z.H.); (C.Z.); (X.H.)
Hreedi Dev Department of Radiology, Weill Cornell Medicine, New York, NY 10022, USA; (A.S.); (T.C.); (H.D.); (U.S.); (Y.W.); (Z.H.); (C.Z.); (X.H.)
Usama Sattar Department of Radiology, Weill Cornell Medicine, New York, NY 10022, USA; (A.S.); (T.C.); (H.D.); (U.S.); (Y.W.); (Z.H.); (C.Z.); (X.H.)
Vahid Bazojoo Department of Radiology, Weill Cornell Medicine, New York, NY 10022, USA; (A.S.); (T.C.); (H.D.); (U.S.); (Y.W.); (Z.H.); (C.Z.); (X.H.)
Yin Wang Department of Radiology, Weill Cornell Medicine, New York, NY 10022, USA; (A.S.); (T.C.); (H.D.); (U.S.); (Y.W.); (Z.H.); (C.Z.); (X.H.)
Zhongxiu Hu Department of Radiology, Weill Cornell Medicine, New York, NY 10022, USA; (A.S.); (T.C.); (H.D.); (U.S.); (Y.W.); (Z.H.); (C.Z.); (X.H.)
Chenglin Zhu Department of Radiology, Weill Cornell Medicine, New York, NY 10022, USA; (A.S.); (T.C.); (H.D.); (U.S.); (Y.W.); (Z.H.); (C.Z.); (X.H.)
Xinzi He Department of Radiology, Weill Cornell Medicine, New York, NY 10022, USA; (A.S.); (T.C.); (H.D.); (U.S.); (Y.W.); (Z.H.); (C.Z.); (X.H.) Department of Biomedical Engineering, Cornell University, Ithaca, NY 14853, USA
Dominick Romano Department of Radiology, Weill Cornell Medicine, New York, NY 10022, USA; (A.S.); (T.C.); (H.D.); (U.S.); (Y.W.); (Z.H.); (C.Z.); (X.H.) Department of Biomedical Engineering, Cornell University, Ithaca, NY 14853, USA
Joseph M. Scandura Richard T. Silver Myeloproliferative Neoplasms Center, Weill Cornell Medicine, New York, NY 10065, USA; Division of Hematology and Medical Oncology, Weill Cornell Medicine, New York, NY 10021, USA
Martin R. Prince Department of Radiology, Weill Cornell Medicine, New York, NY 10022, USA; (A.S.); (T.C.); (H.D.); (U.S.); (Y.W.); (Z.H.); (C.Z.); (X.H.) Department of Radiology, Columbia College of Physicians and Surgeons, New York, NY 10032, USA

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Wei D, Jiang Y, Zhou X, Wu D, Feng X. A Review of Advancements and Challenges in Liver Segmentation. J Imaging 2024;10:202. [PMID: 39194991 DOI: 10.3390/jimaging10080202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2024] [Revised: 08/07/2024] [Accepted: 08/13/2024] [Indexed: 08/29/2024] Open

Li S, Li X, Zhou F, Zhang Y, Bie Z, Cheng L, Peng J, Li B. Automated segmentation of liver and hepatic vessels on portal venous phase computed tomography images using a deep learning algorithm. J Appl Clin Med Phys 2024;25:e14397. [PMID: 38773719 PMCID: PMC11302809 DOI: 10.1002/acm2.14397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Revised: 04/20/2024] [Accepted: 04/29/2024] [Indexed: 05/24/2024] Open

Abstract

BACKGROUND

CT-image segmentation for liver and hepatic vessels can facilitate liver surgical planning. However, time-consuming process and inter-observer variations of manual segmentation have limited wider application in clinical practice.

PURPOSE

Our study aimed to propose an automated deep learning (DL) segmentation algorithm for liver and hepatic vessels on portal venous phase CT images.

METHODS

This retrospective study was performed to develop a coarse-to-fine DL-based algorithm that was trained, validated, and tested using private 413, 52, and 50 portal venous phase CT images, respectively. Additionally, the performance of the DL algorithm was extensively evaluated and compared with manual segmentation using an independent clinical dataset of preoperative contrast-enhanced CT images from 44 patients with hepatic focal lesions. The accuracy of DL-based segmentation was quantitatively evaluated using the Dice Similarity Coefficient (DSC) and complementary metrics [Normalized Surface Dice (NSD) and Hausdorff distance_95 (HD95) for liver segmentation, Recall and Precision for hepatic vessel segmentation]. The processing time for DL and manual segmentation was also compared.

RESULTS

Our DL algorithm achieved accurate liver segmentation with DSC of 0.98, NSD of 0.92, and HD95 of 1.52 mm. DL-segmentation of hepatic veins, portal veins, and inferior vena cava attained DSC of 0.86, 0.89, and 0.94, respectively. Compared with the manual approach, the DL algorithm significantly outperformed with better segmentation results for both liver and hepatic vessels, with higher accuracy of liver and hepatic vessel segmentation (all p < 0.001) in independent 44 clinical data. In addition, the DL method significantly reduced the manual processing time of clinical postprocessing (p < 0.001).

CONCLUSIONS

The proposed DL algorithm potentially enabled accurate and rapid segmentation for liver and hepatic vessels using portal venous phase contrast CT images.

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Somasundaram E, Taylor Z, Alves VV, Qiu L, Fortson BL, Mahalingam N, Dudley JA, Li H, Brady SL, Trout AT, Dillman JR. Deep Learning Models for Abdominal CT Organ Segmentation in Children: Development and Validation in Internal and Heterogeneous Public Datasets. AJR Am J Roentgenol 2024;223:e2430931. [PMID: 38691411 DOI: 10.2214/ajr.24.30931] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/03/2024]

Abstract

BACKGROUND. Deep learning abdominal organ segmentation algorithms have shown excellent results in adults; validation in children is sparse. OBJECTIVE. The purpose of this article is to develop and validate deep learning models for liver, spleen, and pancreas segmentation on pediatric CT examinations. METHODS. This retrospective study developed and validated deep learning models for liver, spleen, and pancreas segmentation using 1731 CT examinations (1504 training, 221 testing), derived from three internal institutional pediatric (age ≤ 18 years) datasets (n = 483) and three public datasets comprising pediatric and adult examinations with various pathologies (n = 1248). Three deep learning model architectures (SegResNet, DynUNet, and SwinUNETR) from the Medical Open Network for Artificial Intelligence (MONAI) framework underwent training using native training (NT), relying solely on institutional datasets, and transfer learning (TL), incorporating pretraining on public datasets. For comparison, TotalSegmentator, a publicly available segmentation model, was applied to test data without further training. Segmentation performance was evaluated using mean Dice similarity coefficient (DSC), with manual segmentations as reference. RESULTS. For internal pediatric data, the DSC for TotalSegmentator, NT models, and TL models for normal liver was 0.953, 0.964-0.965, and 0.965-0.966, respectively; for normal spleen, 0.914, 0.942-0.945, and 0.937-0.945; for normal pancreas, 0.733, 0.774-0.785, and 0.775-0.786; and for pancreas with pancreatitis, 0.703, 0.590-0.640, and 0.667-0.711. For public pediatric data, the DSC for TotalSegmentator, NT models, and TL models for liver was 0.952, 0.871-0.908, and 0.941-0.946, respectively; for spleen, 0.905, 0.771-0.827, and 0.897-0.926; and for pancreas, 0.700, 0.577-0.648, and 0.693-0.736. For public primarily adult data, the DSC for TotalSegmentator, NT models, and TL models for liver was 0.991, 0.633-0.750, and 0.926-0.952, respectively; for spleen, 0.983, 0.569-0.604, and 0.923-0.947; and for pancreas, 0.909, 0.148-0.241, and 0.699-0.775. The DynUNet TL model was selected as the best-performing NT or TL model considering DSC values across organs and test datasets and was made available as an open-source MONAI bundle (https://github.com/cchmc-dll/pediatric_abdominal_segmentation_bundle.git). CONCLUSION. TL models trained on heterogeneous public datasets and fine-tuned using institutional pediatric data outperformed internal NT models and Total-Segmentator across internal and external pediatric test data. Segmentation performance was better in liver and spleen than in pancreas. CLINICAL IMPACT. The selected model may be used for various volumetry applications in pediatric imaging.

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Affiliation(s)

Elanchezhian Somasundaram Department of Radiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave, MLC 5033, Cincinnati, OH 45229 Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH
Zachary Taylor Department of Radiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave, MLC 5033, Cincinnati, OH 45229
Vinicius V Alves Department of Radiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave, MLC 5033, Cincinnati, OH 45229
Lisa Qiu Department of Radiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave, MLC 5033, Cincinnati, OH 45229
Benjamin L Fortson Department of Radiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave, MLC 5033, Cincinnati, OH 45229
Neeraja Mahalingam Department of Radiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave, MLC 5033, Cincinnati, OH 45229
Jonathan A Dudley Department of Radiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave, MLC 5033, Cincinnati, OH 45229
Hailong Li Department of Radiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave, MLC 5033, Cincinnati, OH 45229 Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH
Samuel L Brady Department of Radiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave, MLC 5033, Cincinnati, OH 45229 Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH
Andrew T Trout Department of Radiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave, MLC 5033, Cincinnati, OH 45229 Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH
Jonathan R Dillman Department of Radiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave, MLC 5033, Cincinnati, OH 45229 Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH

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Kutaiba N, Chung W, Goodwin M, Testro A, Egan G, Lim R. The impact of hepatic and splenic volumetric assessment in imaging for chronic liver disease: a narrative review. Insights Imaging 2024;15:146. [PMID: 38886297 PMCID: PMC11183036 DOI: 10.1186/s13244-024-01727-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 05/26/2024] [Indexed: 06/20/2024] Open

Liu X, Qu L, Xie Z, Zhao J, Shi Y, Song Z. Towards more precise automatic analysis: a systematic review of deep learning-based multi-organ segmentation. Biomed Eng Online 2024;23:52. [PMID: 38851691 PMCID: PMC11162022 DOI: 10.1186/s12938-024-01238-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Accepted: 04/11/2024] [Indexed: 06/10/2024] Open

Heo S, Park HJ, Lee SS. Prognostication of Hepatocellular Carcinoma Using Artificial Intelligence. Korean J Radiol 2024;25:550-558. [PMID: 38807336 PMCID: PMC11136947 DOI: 10.3348/kjr.2024.0070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 03/13/2024] [Accepted: 03/31/2024] [Indexed: 05/30/2024] Open

Jeon SK, Joo I, Park J, Kim JM, Park SJ, Yoon SH. Fully-automated multi-organ segmentation tool applicable to both non-contrast and post-contrast abdominal CT: deep learning algorithm developed using dual-energy CT images. Sci Rep 2024;14:4378. [PMID: 38388824 PMCID: PMC10883917 DOI: 10.1038/s41598-024-55137-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 02/20/2024] [Indexed: 02/24/2024] Open

Sanchez-Garcia J, Lopez-Verdugo F, Shorti R, Krong J, Kastenberg ZJ, Walters S, Gagnon A, Paci P, Zendejas I, Alonso D, Fujita S, Contreras AG, Botha J, Esquivel CO, Rodriguez-Davalos MI. Three-dimensional Liver Model Application for Liver Transplantation. Transplantation 2024;108:464-472. [PMID: 38259179 DOI: 10.1097/tp.0000000000004730] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]

Affiliation(s)

Jorge Sanchez-Garcia Liver Center, Intermountain Primary Children's Hospital, Salt Lake City, UT Abdominal Transplant Service, Intermountain Medical Center, Murray, UT
Fidel Lopez-Verdugo Liver Center, Intermountain Primary Children's Hospital, Salt Lake City, UT Abdominal Transplant Service, Intermountain Medical Center, Murray, UT
Rami Shorti Emerging Technologies, Intermountain Health, Murray, UT
Jake Krong Transplant Research Department, Intermountain Medical Center, Murray, UT
Zachary J Kastenberg Liver Center, Intermountain Primary Children's Hospital, Salt Lake City, UT Division of Pediatric Surgery, University of Utah School of Medicine, Salt Lake City, UT
Shannon Walters Department of Radiology, Stanford University School of Medicine, Stanford, CA
Andrew Gagnon Abdominal Transplant Service, Intermountain Medical Center, Murray, UT
Philippe Paci Abdominal Transplant Service, Intermountain Medical Center, Murray, UT
Ivan Zendejas Abdominal Transplant Service, Intermountain Medical Center, Murray, UT
Diane Alonso Abdominal Transplant Service, Intermountain Medical Center, Murray, UT
Shiro Fujita Liver Center, Intermountain Primary Children's Hospital, Salt Lake City, UT Abdominal Transplant Service, Intermountain Medical Center, Murray, UT
Alan G Contreras Liver Center, Intermountain Primary Children's Hospital, Salt Lake City, UT Abdominal Transplant Service, Intermountain Medical Center, Murray, UT
Jean Botha Liver Center, Intermountain Primary Children's Hospital, Salt Lake City, UT Abdominal Transplant Service, Intermountain Medical Center, Murray, UT
Carlos O Esquivel Division of Abdominal Transplantation, Lucile Packard Children's Hospital, Stanford University School of Medicine, Stanford, CA
Manuel I Rodriguez-Davalos Liver Center, Intermountain Primary Children's Hospital, Salt Lake City, UT Division of Transplant Surgery, University of Utah School of Medicine, Salt Lake City, UT

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Krokos G, Kotwal T, Malaih A, Barrington S, Jackson P, Hicks RJ, Marsden PK, Fischer BM. Evaluation of manual and automated approaches for segmentation and extraction of quantitative indices from [¹⁸F]FDG PET-CT images. Biomed Phys Eng Express 2024;10:025007. [PMID: 38100790 PMCID: PMC10767880 DOI: 10.1088/2057-1976/ad160e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 11/28/2023] [Accepted: 12/15/2023] [Indexed: 12/17/2023]

Abstract

Utilisation of whole organ volumes to extract anatomical and functional information from computed tomography (CT) and positron emission tomography (PET) images may provide key information for the treatment and follow-up of cancer patients. However, manual organ segmentation, is laborious and time-consuming. In this study, a CT-based deep learning method and a multi-atlas method were evaluated for segmenting the liver and spleen on CT images to extract quantitative tracer information from Fluorine-18 fluorodeoxyglucose ([18F]FDG) PET images of 50 patients with advanced Hodgkin lymphoma (HL). Manual segmentation was used as the reference method. The two automatic methods were also compared with a manually defined volume of interest (VOI) within the organ, a technique commonly performed in clinical settings. Both automatic methods provided accurate CT segmentations, with the deep learning method outperforming the multi-atlas with a DICE coefficient of 0.93 ± 0.03 (mean ± standard deviation) in liver and 0.87 ± 0.17 in spleen compared to 0.87 ± 0.05 (liver) and 0.78 ± 0.11 (spleen) for the multi-atlas. Similarly, a mean relative error of -3.2% for the liver and -3.4% for the spleen across patients was found for the mean standardized uptake value (SUVmean) using the deep learning regions while the corresponding errors for the multi-atlas method were -4.7% and -9.2%, respectively. For the maximum SUV (SUVmax), both methods resulted in higher than 20% overestimation due to the extension of organ boundaries to include neighbouring, high-uptake regions. The conservative VOI method which did not extend into neighbouring tissues, provided a more accurate SUVmaxestimate. In conclusion, the automatic, and particularly the deep learning method could be used to rapidly extract information of the SUVmeanwithin the liver and spleen. However, activity from neighbouring organs and lesions can lead to high biases in SUVmaxand current practices of manually defining a volume of interest in the organ should be considered instead.

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Machry M, Ferreira LF, Lucchese AM, Kalil AN, Feier FH. Liver volumetric and anatomic assessment in living donor liver transplantation: The role of modern imaging and artificial intelligence. World J Transplant 2023;13:290-298. [PMID: 38174151 PMCID: PMC10758682 DOI: 10.5500/wjt.v13.i6.290] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 08/17/2023] [Accepted: 10/17/2023] [Indexed: 12/15/2023] Open

Abstract

The shortage of deceased donor organs has prompted the development of alternative liver grafts for transplantation. Living-donor liver transplantation (LDLT) has emerged as a viable option, expanding the donor pool and enabling timely transplantation with favorable graft function and improved long-term outcomes. An accurate evaluation of the donor liver's volumetry (LV) and anatomical study is crucial to ensure adequate future liver remnant, graft volume and precise liver resection. Thus, ensuring donor safety and an appropriate graft-to-recipient weight ratio. Manual LV (MLV) using computed tomography has traditionally been considered the gold standard for assessing liver volume. However, the method has been limited by cost, subjectivity, and variability. Automated LV techniques employing advanced segmentation algorithms offer improved reproducibility, reduced variability, and enhanced efficiency compared to manual measurements. However, the accuracy of automated LV requires further investigation. The study provides a comprehensive review of traditional and emerging LV methods, including semi-automated image processing, automated LV techniques, and machine learning-based approaches. Additionally, the study discusses the respective strengths and weaknesses of each of the aforementioned techniques. The use of artificial intelligence (AI) technologies, including machine learning and deep learning, is expected to become a routine part of surgical planning in the near future. The implementation of AI is expected to enable faster and more accurate image study interpretations, improve workflow efficiency, and enhance the safety, speed, and cost-effectiveness of the procedures. Accurate preoperative assessment of the liver plays a crucial role in ensuring safe donor selection and improved outcomes in LDLT. MLV has inherent limitations that have led to the adoption of semi-automated and automated software solutions. Moreover, AI has tremendous potential for LV and segmentation; however, its widespread use is hindered by cost and availability. Therefore, the integration of multiple specialties is necessary to embrace technology and explore its possibilities, ranging from patient counseling to intraoperative decision-making through automation and AI.

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Cheng JA, Lin YC, Lin Y, Wu RC, Lu HY, Yang LY, Chiang HJ, Juan YH, Lai YC, Lin G. Machine Learning Radiomics Signature for Differentiating Lymphoma versus Benign Splenomegaly on CT. Diagnostics (Basel) 2023;13:3632. [PMID: 38132216 PMCID: PMC10742777 DOI: 10.3390/diagnostics13243632] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 12/01/2023] [Accepted: 12/06/2023] [Indexed: 12/23/2023] Open

Affiliation(s)

Jih-An Cheng Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan; (J.-A.C.); (Y.-C.L.); (H.-Y.L.); (H.-J.C.); (Y.-H.J.); (Y.-C.L.)
Yu-Chun Lin Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan; (J.-A.C.); (Y.-C.L.); (H.-Y.L.); (H.-J.C.); (Y.-H.J.); (Y.-C.L.) Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan 333, Taiwan Clinical Metabolomics Core and Imaging Core Laboratory, Institute for Radiological Research, Chang Gung Memorial Hospital at Linkou and Chang Gung University, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan
Yenpo Lin Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan; (J.-A.C.); (Y.-C.L.); (H.-Y.L.); (H.-J.C.); (Y.-H.J.); (Y.-C.L.) Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan 333, Taiwan Clinical Metabolomics Core and Imaging Core Laboratory, Institute for Radiological Research, Chang Gung Memorial Hospital at Linkou and Chang Gung University, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan
Ren-Chin Wu Department of Pathology, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan;
Hsin-Ying Lu Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan; (J.-A.C.); (Y.-C.L.); (H.-Y.L.); (H.-J.C.); (Y.-H.J.); (Y.-C.L.) Clinical Metabolomics Core and Imaging Core Laboratory, Institute for Radiological Research, Chang Gung Memorial Hospital at Linkou and Chang Gung University, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan
Lan-Yan Yang Clinical Trial Center, Chang Gung Memorial Hospital at Linkou and Chang Gung University, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan;
Hsin-Ju Chiang Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan; (J.-A.C.); (Y.-C.L.); (H.-Y.L.); (H.-J.C.); (Y.-H.J.); (Y.-C.L.) Clinical Metabolomics Core and Imaging Core Laboratory, Institute for Radiological Research, Chang Gung Memorial Hospital at Linkou and Chang Gung University, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan
Yu-Hsiang Juan Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan; (J.-A.C.); (Y.-C.L.); (H.-Y.L.); (H.-J.C.); (Y.-H.J.); (Y.-C.L.)
Ying-Chieh Lai Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan; (J.-A.C.); (Y.-C.L.); (H.-Y.L.); (H.-J.C.); (Y.-H.J.); (Y.-C.L.) Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan 333, Taiwan Clinical Metabolomics Core and Imaging Core Laboratory, Institute for Radiological Research, Chang Gung Memorial Hospital at Linkou and Chang Gung University, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan
Gigin Lin Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan; (J.-A.C.); (Y.-C.L.); (H.-Y.L.); (H.-J.C.); (Y.-H.J.); (Y.-C.L.) Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan 333, Taiwan Clinical Metabolomics Core and Imaging Core Laboratory, Institute for Radiological Research, Chang Gung Memorial Hospital at Linkou and Chang Gung University, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan

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Kim DY, Oh HW, Suh CH. Reporting Quality of Research Studies on AI Applications in Medical Images According to the CLAIM Guidelines in a Radiology Journal With a Strong Prominence in Asia. Korean J Radiol 2023;24:1179-1189. [PMID: 38016678 PMCID: PMC10701000 DOI: 10.3348/kjr.2023.1027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 10/25/2023] [Accepted: 10/26/2023] [Indexed: 11/30/2023] Open

Abstract

OBJECTIVE

We aimed to evaluate the reporting quality of research articles that applied deep learning to medical imaging. Using the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) guidelines and a journal with prominence in Asia as a sample, we intended to provide an insight into reporting quality in the Asian region and establish a journal-specific audit.

MATERIALS AND METHODS

A total of 38 articles published in the Korean Journal of Radiology between June 2018 and January 2023 were analyzed. The analysis included calculating the percentage of studies that adhered to each CLAIM item and identifying items that were met by ≤ 50% of the studies. The article review was initially conducted independently by two reviewers, and the consensus results were used for the final analysis. We also compared adherence rates to CLAIM before and after December 2020.

RESULTS

Of the 42 items in the CLAIM guidelines, 12 items (29%) were satisfied by ≤ 50% of the included articles. None of the studies reported handling missing data (item #13). Only one study respectively presented the use of de-identification methods (#12), intended sample size (#19), robustness or sensitivity analysis (#30), and full study protocol (#41). Of the studies, 35% reported the selection of data subsets (#10), 40% reported registration information (#40), and 50% measured inter and intrarater variability (#18). No significant changes were observed in the rates of adherence to these 12 items before and after December 2020.

CONCLUSION

The reporting quality of artificial intelligence studies according to CLAIM guidelines, in our study sample, showed room for improvement. We recommend that the authors and reviewers have a solid understanding of the relevant reporting guidelines and ensure that the essential elements are adequately reported when writing and reviewing the manuscripts for publication.

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Kutaiba N, Dahan A, Goodwin M, Testro A, Egan G, Lim R. Deep Learning for Computed Tomography Assessment of Hepatic Fibrosis and Cirrhosis: A Systematic Review. MAYO CLINIC PROCEEDINGS. DIGITAL HEALTH 2023;1:574-585. [PMID: 40206310 PMCID: PMC11975692 DOI: 10.1016/j.mcpdig.2023.08.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/11/2025]

Rao S, Glavis-Bloom J, Bui TL, Afzali K, Bansal R, Carbone J, Fateri C, Roth B, Chan W, Kakish D, Cortes G, Wang P, Meraz J, Chantaduly C, Chow DS, Chang PD, Houshyar R. Artificial Intelligence for Improved Hepatosplenomegaly Diagnosis. Curr Probl Diagn Radiol 2023;52:501-504. [PMID: 37277270 DOI: 10.1067/j.cpradiol.2023.05.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 04/14/2023] [Accepted: 05/08/2023] [Indexed: 06/07/2023]

Abstract

Hepatosplenomegaly is commonly diagnosed by radiologists based on single dimension measurements and heuristic cut-offs. Volumetric measurements may be more accurate for diagnosing organ enlargement. Artificial intelligence techniques may be able to automatically calculate liver and spleen volume and facilitate more accurate diagnosis. After IRB approval, 2 convolutional neural networks (CNN) were developed to automatically segment the liver and spleen on a training dataset comprised of 500 single-phase, contrast-enhanced CT abdomen and pelvis examinations. A separate dataset of ten thousand sequential examinations at a single institution was segmented with these CNNs. Performance was evaluated on a 1% subset and compared with manual segmentations using Sorensen-Dice coefficients and Pearson correlation coefficients. Radiologist reports were reviewed for diagnosis of hepatomegaly and splenomegaly and compared with calculated volumes. Abnormal enlargement was defined as greater than 2 standard deviations above the mean. Median Dice coefficients for liver and spleen segmentation were 0.988 and 0.981, respectively. Pearson correlation coefficients of CNN-derived estimates of organ volume against the gold-standard manual annotation were 0.999 for the liver and spleen (P < 0.001). Average liver volume was 1556.8 ± 498.7 cc and average spleen volume was 194.6 ± 123.0 cc. There were significant differences in average liver and spleen volumes between male and female patients. Thus, the volume thresholds for ground-truth determination of hepatomegaly and splenomegaly were determined separately for each sex. Radiologist classification of hepatomegaly was 65% sensitive, 91% specific, with a positive predictive value (PPV) of 23% and an negative predictive value (NPV) of 98%. Radiologist classification of splenomegaly was 68% sensitive, 97% specific, with a positive predictive value (PPV) of 50% and a negative predictive value (NPV) of 99%. Convolutional neural networks can accurately segment the liver and spleen and may be helpful to improve radiologist accuracy in the diagnosis of hepatomegaly and splenomegaly.

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Yang X, Park S, Lee S, Han K, Lee MR, Song JS, Yu HC, Do Yang J. Estimation of right lobe graft weight for living donor liver transplantation using deep learning-based fully automatic computed tomographic volumetry. Sci Rep 2023;13:17746. [PMID: 37853228 PMCID: PMC10584880 DOI: 10.1038/s41598-023-45140-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 10/16/2023] [Indexed: 10/20/2023] Open

Radiya K, Joakimsen HL, Mikalsen KØ, Aahlin EK, Lindsetmo RO, Mortensen KE. Performance and clinical applicability of machine learning in liver computed tomography imaging: a systematic review. Eur Radiol 2023;33:6689-6717. [PMID: 37171491 PMCID: PMC10511359 DOI: 10.1007/s00330-023-09609-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 02/02/2023] [Accepted: 02/06/2023] [Indexed: 05/13/2023]

Abstract

OBJECTIVES

Machine learning (ML) for medical imaging is emerging for several organs and image modalities. Our objectives were to provide clinicians with an overview of this field by answering the following questions: (1) How is ML applied in liver computed tomography (CT) imaging? (2) How well do ML systems perform in liver CT imaging? (3) What are the clinical applications of ML in liver CT imaging?

METHODS

A systematic review was carried out according to the guidelines from the PRISMA-P statement. The search string focused on studies containing content relating to artificial intelligence, liver, and computed tomography.

RESULTS

One hundred ninety-one studies were included in the study. ML was applied to CT liver imaging by image analysis without clinicians' intervention in majority of studies while in newer studies the fusion of ML method with clinical intervention have been identified. Several were documented to perform very accurately on reliable but small data. Most models identified were deep learning-based, mainly using convolutional neural networks. Potentially many clinical applications of ML to CT liver imaging have been identified through our review including liver and its lesion segmentation and classification, segmentation of vascular structure inside the liver, fibrosis and cirrhosis staging, metastasis prediction, and evaluation of chemotherapy.

CONCLUSION

Several studies attempted to provide transparent result of the model. To make the model convenient for a clinical application, prospective clinical validation studies are in urgent call. Computer scientists and engineers should seek to cooperate with health professionals to ensure this.

KEY POINTS

• ML shows great potential for CT liver image tasks such as pixel-wise segmentation and classification of liver and liver lesions, fibrosis staging, metastasis prediction, and retrieval of relevant liver lesions from similar cases of other patients. • Despite presenting the result is not standardized, many studies have attempted to provide transparent results to interpret the machine learning method performance in the literature. • Prospective studies are in urgent call for clinical validation of ML method, preferably carried out by cooperation between clinicians and computer scientists.

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Heo S, Lee SS, Choi SH, Kim DW, Park HJ, Kim SY, Lee SJ, Kim KM, Shin YM. CT Rule-in and Rule-out Criteria for Clinically Significant Portal Hypertension in Chronic Liver Disease. Radiology 2023;309:e231208. [PMID: 37906011 DOI: 10.1148/radiol.231208] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2023]

Abstract

Background The value of CT in assessment of clinically significant portal hypertension (CSPH) has not been well determined. Purpose To evaluate the performance of CT features that have been associated with portal hypertension for diagnosing CSPH in patients with chronic liver disease (CLD). Materials and Methods This retrospective study included patients with CLD who underwent contrast-enhanced CT and subsequent hepatic venous pressure gradient (HVPG) measurement within 3 months at two tertiary institutions from January 2001 to December 2019. Two readers independently evaluated the presence of gastroesophageal varix, spontaneous portosystemic shunt (SPSS), and ascites on CT images. Splenomegaly at CT was determined using three methods, as follows: personalized or fixed volume criteria, based on spleen volume as measured by a deep learning algorithm, or manually measured spleen diameter. The diagnostic performance of these findings alone or in combination for detecting CSPH (HVPG ≥10 mm Hg) was evaluated. Results A total of 235 patients (mean age, 53.2 years ± 13.0 [SD]; 155 male patients), including 110 (46.8%) with CSPH, were included. Detection of CSPH according to the presence of both splenomegaly and at least one other CT feature (ie, gastroesophageal varix, SPSS, and ascites) achieved specificities of 94.4%-97.6%, whereas detection of CSPH according to the presence of any feature (ie, splenomegaly, gastroesophageal varix, SPSS, or ascites) achieved sensitivities of 94.5%-98.2%. When employing the former as rule-in criteria with the absence of splenomegaly, gastroesophageal varix, SPSS, and ascites as rule-out criteria for CSPH, 171-185 (range, 72.8%-78.7%) of 235 patients were correctly classified as either having CSPH or not, seven to 13 (range, 3%-5.5%) of 235 patients were incorrectly classified, and 42-54 (range, 17.9%-23%) of 235 patients were unclassified. Conclusion The presence or absence of splenomegaly, gastroesophageal varix, SPSS, and/or ascites on CT images may be useful for ruling in and ruling out CSPH in patients with CLD. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Fraum in this issue.

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Affiliation(s)

Subin Heo From the Department of Radiology, Ajou University School of Medicine, Suwon, South Korea (S.H.); and Department of Radiology and Research Institute of Radiology (S.H., S.S.L., S.H.C., D.W.K., H.J.P., S.Y.K., S.J.L., Y.M.S.) and Department of Gastroenterology, Liver Center (K.M.K.), Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, South Korea
Seung Soo Lee From the Department of Radiology, Ajou University School of Medicine, Suwon, South Korea (S.H.); and Department of Radiology and Research Institute of Radiology (S.H., S.S.L., S.H.C., D.W.K., H.J.P., S.Y.K., S.J.L., Y.M.S.) and Department of Gastroenterology, Liver Center (K.M.K.), Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, South Korea
Sang Hyun Choi From the Department of Radiology, Ajou University School of Medicine, Suwon, South Korea (S.H.); and Department of Radiology and Research Institute of Radiology (S.H., S.S.L., S.H.C., D.W.K., H.J.P., S.Y.K., S.J.L., Y.M.S.) and Department of Gastroenterology, Liver Center (K.M.K.), Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, South Korea
Dong Wook Kim From the Department of Radiology, Ajou University School of Medicine, Suwon, South Korea (S.H.); and Department of Radiology and Research Institute of Radiology (S.H., S.S.L., S.H.C., D.W.K., H.J.P., S.Y.K., S.J.L., Y.M.S.) and Department of Gastroenterology, Liver Center (K.M.K.), Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, South Korea
Hyo Jung Park From the Department of Radiology, Ajou University School of Medicine, Suwon, South Korea (S.H.); and Department of Radiology and Research Institute of Radiology (S.H., S.S.L., S.H.C., D.W.K., H.J.P., S.Y.K., S.J.L., Y.M.S.) and Department of Gastroenterology, Liver Center (K.M.K.), Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, South Korea
So Yeon Kim From the Department of Radiology, Ajou University School of Medicine, Suwon, South Korea (S.H.); and Department of Radiology and Research Institute of Radiology (S.H., S.S.L., S.H.C., D.W.K., H.J.P., S.Y.K., S.J.L., Y.M.S.) and Department of Gastroenterology, Liver Center (K.M.K.), Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, South Korea
So Jung Lee From the Department of Radiology, Ajou University School of Medicine, Suwon, South Korea (S.H.); and Department of Radiology and Research Institute of Radiology (S.H., S.S.L., S.H.C., D.W.K., H.J.P., S.Y.K., S.J.L., Y.M.S.) and Department of Gastroenterology, Liver Center (K.M.K.), Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, South Korea
Kang Mo Kim From the Department of Radiology, Ajou University School of Medicine, Suwon, South Korea (S.H.); and Department of Radiology and Research Institute of Radiology (S.H., S.S.L., S.H.C., D.W.K., H.J.P., S.Y.K., S.J.L., Y.M.S.) and Department of Gastroenterology, Liver Center (K.M.K.), Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, South Korea
Yong Moon Shin From the Department of Radiology, Ajou University School of Medicine, Suwon, South Korea (S.H.); and Department of Radiology and Research Institute of Radiology (S.H., S.S.L., S.H.C., D.W.K., H.J.P., S.Y.K., S.J.L., Y.M.S.) and Department of Gastroenterology, Liver Center (K.M.K.), Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, South Korea

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Baldisseri F, Wrona A, Menegatti D, Pietrabissa A, Battilotti S, Califano C, Cristofaro A, Di Giamberardino P, Facchinei F, Palagi L, Giuseppi A, Delli Priscoli F. Deep Neural Network Regression to Assist Non-Invasive Diagnosis of Portal Hypertension. Healthcare (Basel) 2023;11:2603. [PMID: 37761800 PMCID: PMC10530845 DOI: 10.3390/healthcare11182603] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 09/15/2023] [Accepted: 09/20/2023] [Indexed: 09/29/2023] Open

Choi JY, Lee SS, Kim NY, Park HJ, Sung YS, Lee Y, Yoon JS, Suk HI. The effect of hepatic steatosis on liver volume determined by proton density fat fraction and deep learning-measured liver volume. Eur Radiol 2023;33:5924-5932. [PMID: 37012546 DOI: 10.1007/s00330-023-09603-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 02/03/2023] [Accepted: 02/22/2023] [Indexed: 04/05/2023]

Abstract

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 (β = 1.004, p < 0.001) and PDFF grade*SLV (β = 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.

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Azuri I, Wattad A, Peri-Hanania K, Kashti T, Rosen R, Caspi Y, Istaiti M, Wattad M, Applbaum Y, Zimran A, Revel-Vilk S, C. Eldar Y. A Deep-Learning Approach to Spleen Volume Estimation in Patients with Gaucher Disease. J Clin Med 2023;12:5361. [PMID: 37629403 PMCID: PMC10455264 DOI: 10.3390/jcm12165361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Revised: 08/04/2023] [Accepted: 08/10/2023] [Indexed: 08/27/2023] Open

Berbís MA, Paulano Godino F, Royuela del Val J, Alcalá Mata L, Luna A. Clinical impact of artificial intelligence-based solutions on imaging of the pancreas and liver. World J Gastroenterol 2023;29:1427-1445. [PMID: 36998424 PMCID: PMC10044858 DOI: 10.3748/wjg.v29.i9.1427] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 01/13/2023] [Accepted: 02/27/2023] [Indexed: 03/07/2023] Open

Abstract

Artificial intelligence (AI) has experienced substantial progress over the last ten years in many fields of application, including healthcare. In hepatology and pancreatology, major attention to date has been paid to its application to the assisted or even automated interpretation of radiological images, where AI can generate accurate and reproducible imaging diagnosis, reducing the physicians’ workload. AI can provide automatic or semi-automatic segmentation and registration of the liver and pancreatic glands and lesions. Furthermore, using radiomics, AI can introduce new quantitative information which is not visible to the human eye to radiological reports. AI has been applied in the detection and characterization of focal lesions and diffuse diseases of the liver and pancreas, such as neoplasms, chronic hepatic disease, or acute or chronic pancreatitis, among others. These solutions have been applied to different imaging techniques commonly used to diagnose liver and pancreatic diseases, such as ultrasound, endoscopic ultrasonography, computerized tomography (CT), magnetic resonance imaging, and positron emission tomography/CT. However, AI is also applied in this context to many other relevant steps involved in a comprehensive clinical scenario to manage a gastroenterological patient. AI can also be applied to choose the most convenient test prescription, to improve image quality or accelerate its acquisition, and to predict patient prognosis and treatment response. In this review, we summarize the current evidence on the application of AI to hepatic and pancreatic radiology, not only in regard to the interpretation of images, but also to all the steps involved in the radiological workflow in a broader sense. Lastly, we discuss the challenges and future directions of the clinical application of AI methods.

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Park HJ, Kim KW, Lee SS. Artificial intelligence in radiology and its application in liver disease. ARTIFICIAL INTELLIGENCE, MACHINE LEARNING, AND DEEP LEARNING IN PRECISION MEDICINE IN LIVER DISEASES 2023:53-79. [DOI: 10.1016/b978-0-323-99136-0.00002-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2025]

Usefulness of deep learning-based noise reduction for 1.5 T MRI brain images. Clin Radiol 2023;78:e13-e21. [PMID: 36116967 DOI: 10.1016/j.crad.2022.08.127] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2022] [Revised: 08/04/2022] [Accepted: 08/04/2022] [Indexed: 01/07/2023]

Im WH, Song JS, Jang W. Noninvasive staging of liver fibrosis: review of current quantitative CT and MRI-based techniques. Abdom Radiol (NY) 2022;47:3051-3067. [PMID: 34228199 DOI: 10.1007/s00261-021-03181-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Revised: 06/12/2021] [Accepted: 06/14/2021] [Indexed: 01/18/2023]

Choi SJ, Lee SS, Jung KH, Lee JB, Kang HJ, Park HJ, Choi SH, Kim DW, Jang JK. Noncirrhotic Portal Hypertension after Trastuzumab Emtansine in HER2-positive Breast Cancer as Determined by Deep Learning-measured Spleen Volume at CT. Radiology 2022;305:606-613. [PMID: 35943338 DOI: 10.1148/radiol.220536] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]

Abstract

Background Trastuzumab emtansine (T-DM1) is an antibody-drug conjugate approved for use in human epidermal growth factor receptor 2 (HER2)-positive breast cancer. Case reports have suggested an association between T-DM1 and portal hypertension. Purpose To evaluate the association of T-DM1 therapy with spleen volume changes and portal hypertension on CT scans and clinical findings compared with lapatinib and capecitabine therapy. Materials and Methods Patients with HER2-positive breast cancer who were administered at least two cycles of T-DM1 or lapatinib and capecitabine (controls) in a tertiary institution from 2001 to 2020 and who underwent CT before initial treatment and at least once during treatment were retrospectively enrolled. Spleen volume changes and the signs of portal hypertension (gastroesophageal varix [GEV], spontaneous portosystemic shunt [SPSS], and ascites) were evaluated at contrast-enhanced CT. Patients were followed until treatment ended or for 2 years after the start of treatment. Spleen volume changes were measured with a deep learning algorithm and evaluated by using a linear mixed model. The incidences of splenomegaly and portal hypertension were compared between the T-DM1 and control groups by using a χ² test or Fisher exact test. Results The T-DM1 group included 111 patients (mean age, 54 years ± 11 [SD]; 111 women) and the control group included 122 patients (mean age, 50 years ± 9; 121 women). Spleen volume progressively increased with T-DM1 therapy but was constant in the control group (104% ± 5 vs -1% ± 6 at the 33rd treatment cycle, respectively; P < .001). Incidences of splenomegaly (46% [51 of 111] vs 3% [four of 122] of patients; P < .001), GEV (11% [12 of 111] vs 1% [one of 122] of patients; P < .001), and SPSS (27% [30 of 111] vs 1% [one of 122] of patients; P < .001) were higher in the T-DM1 group than in the control group. Conclusion Trastuzumab emtansine therapy was associated with noncirrhotic portal hypertension at CT, with higher incidences of splenomegaly, gastroesophageal varix, and spontaneous portosystemic shunt than those with lapatinib and capecitabine therapy. © RSNA, 2022 Online supplemental material is available for this article.

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Affiliation(s)

Se Jin Choi From the Departments of Radiology and Research Institute of Radiology (S.J.C., S.S.L., H.J.P., S.H.C., D.W.K., J.K.J.), Oncology (K.H.J.), Clinical Epidemiology and Biostatistics (J.B.L.), and Pathology (H.J.K.), University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea
Seung Soo Lee From the Departments of Radiology and Research Institute of Radiology (S.J.C., S.S.L., H.J.P., S.H.C., D.W.K., J.K.J.), Oncology (K.H.J.), Clinical Epidemiology and Biostatistics (J.B.L.), and Pathology (H.J.K.), University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea
Kyung Hae Jung From the Departments of Radiology and Research Institute of Radiology (S.J.C., S.S.L., H.J.P., S.H.C., D.W.K., J.K.J.), Oncology (K.H.J.), Clinical Epidemiology and Biostatistics (J.B.L.), and Pathology (H.J.K.), University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea
Jung Bok Lee From the Departments of Radiology and Research Institute of Radiology (S.J.C., S.S.L., H.J.P., S.H.C., D.W.K., J.K.J.), Oncology (K.H.J.), Clinical Epidemiology and Biostatistics (J.B.L.), and Pathology (H.J.K.), University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea
Hyo Jeong Kang From the Departments of Radiology and Research Institute of Radiology (S.J.C., S.S.L., H.J.P., S.H.C., D.W.K., J.K.J.), Oncology (K.H.J.), Clinical Epidemiology and Biostatistics (J.B.L.), and Pathology (H.J.K.), University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea
Hyo Jung Park From the Departments of Radiology and Research Institute of Radiology (S.J.C., S.S.L., H.J.P., S.H.C., D.W.K., J.K.J.), Oncology (K.H.J.), Clinical Epidemiology and Biostatistics (J.B.L.), and Pathology (H.J.K.), University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea
Sang Hyun Choi From the Departments of Radiology and Research Institute of Radiology (S.J.C., S.S.L., H.J.P., S.H.C., D.W.K., J.K.J.), Oncology (K.H.J.), Clinical Epidemiology and Biostatistics (J.B.L.), and Pathology (H.J.K.), University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea
Dong Wook Kim From the Departments of Radiology and Research Institute of Radiology (S.J.C., S.S.L., H.J.P., S.H.C., D.W.K., J.K.J.), Oncology (K.H.J.), Clinical Epidemiology and Biostatistics (J.B.L.), and Pathology (H.J.K.), University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea
Jong Keon Jang From the Departments of Radiology and Research Institute of Radiology (S.J.C., S.S.L., H.J.P., S.H.C., D.W.K., J.K.J.), Oncology (K.H.J.), Clinical Epidemiology and Biostatistics (J.B.L.), and Pathology (H.J.K.), University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea

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Pettit RW, Marlatt BB, Corr SJ, Havelka J, Rana A. nnU-Net Deep Learning Method for Segmenting Parenchyma and Determining Liver Volume From Computed Tomography Images. ANNALS OF SURGERY OPEN 2022;3:e155. [PMID: 36275876 PMCID: PMC9585534 DOI: 10.1097/as9.0000000000000155] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Accepted: 03/15/2022] [Indexed: 11/26/2022] Open

Wu L, Ning B, Yang J, Chen Y, Zhang C, Yan Y. Diagnosis of Liver Cirrhosis and Liver Fibrosis by Artificial Intelligence Algorithm-Based Multislice Spiral Computed Tomography. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022;2022:1217003. [PMID: 35341007 PMCID: PMC8941514 DOI: 10.1155/2022/1217003] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 02/19/2022] [Accepted: 02/22/2022] [Indexed: 12/12/2022]

Abstract

This research was aimed at investigating the artificial intelligence (AI) segmentation algorithm-based multislice spiral computed tomography (MSCT) in the diagnosis of liver cirrhosis and liver fibrosis. Besides, it was aimed at providing new methods for the diagnosis of liver cirrhosis and liver fibrosis. All patients were divided into the control group, mild liver fibrosis group, and significant liver fibrosis group. A total of 112 patients were included, with 40 cases in the mild liver fibrosis group, 48 cases in the significant liver fibrosis group, and 24 cases who underwent computed tomography (CT) examination in the control group. In the research, deconvolution algorithm of AI segmentation algorithm was adopted to process the images. The average hepatic arterial fraction (HAF) values of patients in the control group, mild liver fibrosis group, and severe liver fibrosis group were 17.59 ± 10.03%, 18.23 ± 5.57%, and 20.98 ± 6.63%, respectively. The average MTT values of patients in the control group, mild liver fibrosis group, and severe liver fibrosis group were 12.69 ± 1.78S, 12.53 ± 2.05S, and 12.04 ± 1.57S, respectively. The average blood flow (BF) values of patients in the control group, mild liver fibrosis group, and severe liver fibrosis group were 105.68 ± 15.57 mL 100 g-1·min-1, 116.07 ± 16.5 mL·100 g-1·min-1, and 110.39 ± 16.32 mL·100 g-1·min-1, respectively. Besides, the average blood volume (BV) values of patients in the control group, mild liver fibrosis group, and significant liver fibrosis group were 15.69 ± 4.35 mL·log-1, 16.97 ± 2.68 mL·log-1, and 16.11 ± 4.87 mL·100 g-1, respectively. According to statistics, the differences among the average HAF, MTT, BF, and BV values showed no statistical meaning. AI segmentation algorithm-based MSCT imaging could promote the diagnosis of liver cirrhosis and liver fibrosis effectively and offer new methods to clinical diagnosis of liver cirrhosis and liver fibrosis.

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Accuracy and Efficiency of Right-Lobe Graft Weight Estimation Using Deep-Learning-Assisted CT Volumetry for Living-Donor Liver Transplantation. Diagnostics (Basel) 2022;12:diagnostics12030590. [PMID: 35328143 PMCID: PMC8946991 DOI: 10.3390/diagnostics12030590] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 02/21/2022] [Accepted: 02/23/2022] [Indexed: 12/19/2022] Open

Park SH. Looking Ahead to 2022 for the Korean Journal of Radiology. Korean J Radiol 2022;23:6-9. [PMID: 34983089 PMCID: PMC8743157 DOI: 10.3348/kjr.2021.0844] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 11/05/2021] [Indexed: 11/15/2022] Open

Aiello M, Baldi D, Esposito G, Valentino M, Randon M, Salvatore M, Cavaliere C. Evaluation of AI-Based Segmentation Tools for COVID-19 Lung Lesions on Conventional and Ultra-low Dose CT Scans. Dose Response 2022;20:15593258221082896. [PMID: 35422680 PMCID: PMC9002358 DOI: 10.1177/15593258221082896] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Accepted: 02/04/2022] [Indexed: 11/16/2022] Open

Park HJ, Yoon JS, Lee SS, Suk HI, Park B, Sung YS, Hong SB, Ryu H. Deep Learning-Based Assessment of Functional Liver Capacity Using Gadoxetic Acid-Enhanced Hepatobiliary Phase MRI. Korean J Radiol 2022;23:720-731. [PMID: 35434977 PMCID: PMC9240292 DOI: 10.3348/kjr.2021.0892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Revised: 01/12/2022] [Accepted: 01/13/2022] [Indexed: 11/15/2022] Open

Meddeb A, Kossen T, Bressem KK, Hamm B, Nagel SN. Evaluation of a Deep Learning Algorithm for Automated Spleen Segmentation in Patients with Conditions Directly or Indirectly Affecting the Spleen. Tomography 2021;7:950-960. [PMID: 34941650 PMCID: PMC8704906 DOI: 10.3390/tomography7040078] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 12/06/2021] [Accepted: 12/07/2021] [Indexed: 12/12/2022] Open

Sugiura A, Treiling L, Al-Kassou B, Shamekhi J, Wilde N, Sinning JM, Zimmer S, Kuetting D, Oldenburg J, Poetzsch B, Nickenig G, Sedaghat A. Spleen Size and Thrombocytopenia After Transcatheter Aortic Valve Implantation. Am J Cardiol 2021;157:85-92. [PMID: 34404506 DOI: 10.1016/j.amjcard.2021.07.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/22/2021] [Revised: 07/06/2021] [Accepted: 07/08/2021] [Indexed: 11/17/2022]

Abstract

The pathophysiology of thrombocytopenia after transcatheter aortic valve implantation (TAVI) thrombocytopenia is still poorly understood. We assessed the association of spleen size with acquired thrombocytopenia in patients undergoing TAVI. We included 732 patients who underwent TAVI with new generation transcatheter heart valves (THVs) at our center from February 2016 to July 2019. We measured splenic volume index in consecutive patients derived from multidetector row computed tomographic datasets. Patients were stratified according to post-TAVI thrombocytopenia, which was defined as a decline in platelet count (DPC) ≥50% at nadir, and evaluated regarding baseline characteristics and outcome parameters. After the procedure, platelet counts declined from 212.9 ± 67.4 × 10⁹/L at baseline to 138.8 ± 49.8 × 10⁹/L at nadir after a median of 2 days (interquartile range [IQR] 2 to 3). Of all patients, 10.1% showed a DPC ≥50%. Compared with patients with DPC <50%, patients with DPC ≥50% had significantly lower splenic volume index (95.5 ml/m² [IQR 78.0 to 123.7] vs 85.8 ml/m² [IQR 71.4 to 102.6], p = 0.008). A multivariable analysis revealed that the splenic volume index was negatively associated with a DPC ≥50% (OR 0.89, 95% CI 0.82 to 0.97, p = 0.005), independent of the type of THV (balloon-expandable THV: OR 2.06, 95% CI 1.13 to 3.76, p = 0.02), major bleeding (OR 13.40, 95% CI 3.58 to 50.40, p <0.001), blood transfusion (OR 3.63, 95% CI 1.54 to 8.56, p = 0.003), or postprocedural paravalvular leakage ≥moderate (OR 5.48, 95% CI 1.23 to 24.40, p = 0.03). Furthermore, DPC ≥50% was independently associated with 1-year mortality (HR 3.36, 95% CI 1.66 to 6.81, p <0.001). In conclusion, acquired thrombocytopenia remains prevalent in modern TAVI patients. Spleen size appears to be associated with the occurrence of thrombocytopenia after TAVI, which is independently correlated with 1-year mortality.

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Qu T, Wang X, Fang C, Mao L, Li J, Li P, Qu J, Li X, Xue H, Yu Y, Jin Z. M³Net: A multi-scale multi-view framework for multi-phase pancreas segmentation based on cross-phase non-local attention. Med Image Anal 2021;75:102232. [PMID: 34700243 DOI: 10.1016/j.media.2021.102232] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Revised: 06/21/2021] [Accepted: 09/10/2021] [Indexed: 10/20/2022]

Kwon JH, Lee SS, Yoon JS, Suk HI, Sung YS, Kim HS, Lee CM, Kim KM, Lee SJ, Kim SY. Liver-to-Spleen Volume Ratio Automatically Measured on CT Predicts Decompensation in Patients with B Viral Compensated Cirrhosis. Korean J Radiol 2021;22:1985-1995. [PMID: 34564961 PMCID: PMC8628160 DOI: 10.3348/kjr.2021.0348] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Revised: 06/03/2021] [Accepted: 06/30/2021] [Indexed: 01/05/2023] Open

Abstract

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.

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Kim DW, Ha J, Lee SS, Kwon JH, Kim NY, Sung YS, Yoon JS, Suk HI, Lee Y, Kang BK. Population-based and Personalized Reference Intervals for Liver and Spleen Volumes in Healthy Individuals and Those with Viral Hepatitis. Radiology 2021;301:339-347. [PMID: 34402668 DOI: 10.1148/radiol.2021204183] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]

Abstract

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.

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Affiliation(s)

Dong Wook Kim From the Department of Radiology and Research Institute of Radiology (D.W.K., J.H., S.S.L., J.H.K., Y.S.S.) and Department of Clinical Epidemiology and Biostatistics (N.Y.K.), University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea; Department of Brain and Cognitive Engineering (J.S.Y., H.I.S.) and Department of Artificial Intelligence (H.I.S.), Korea University, Seoul, Republic of Korea; Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea (Y.L.); and Department of Radiology, Hanyang University Medical Center, Hanyang University School of Medicine, Seoul, Republic of Korea (B.K.K.)
Jiyeon Ha From the Department of Radiology and Research Institute of Radiology (D.W.K., J.H., S.S.L., J.H.K., Y.S.S.) and Department of Clinical Epidemiology and Biostatistics (N.Y.K.), University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea; Department of Brain and Cognitive Engineering (J.S.Y., H.I.S.) and Department of Artificial Intelligence (H.I.S.), Korea University, Seoul, Republic of Korea; Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea (Y.L.); and Department of Radiology, Hanyang University Medical Center, Hanyang University School of Medicine, Seoul, Republic of Korea (B.K.K.)
Seung Soo Lee From the Department of Radiology and Research Institute of Radiology (D.W.K., J.H., S.S.L., J.H.K., Y.S.S.) and Department of Clinical Epidemiology and Biostatistics (N.Y.K.), University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea; Department of Brain and Cognitive Engineering (J.S.Y., H.I.S.) and Department of Artificial Intelligence (H.I.S.), Korea University, Seoul, Republic of Korea; Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea (Y.L.); and Department of Radiology, Hanyang University Medical Center, Hanyang University School of Medicine, Seoul, Republic of Korea (B.K.K.)
Ji Hye Kwon From the Department of Radiology and Research Institute of Radiology (D.W.K., J.H., S.S.L., J.H.K., Y.S.S.) and Department of Clinical Epidemiology and Biostatistics (N.Y.K.), University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea; Department of Brain and Cognitive Engineering (J.S.Y., H.I.S.) and Department of Artificial Intelligence (H.I.S.), Korea University, Seoul, Republic of Korea; Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea (Y.L.); and Department of Radiology, Hanyang University Medical Center, Hanyang University School of Medicine, Seoul, Republic of Korea (B.K.K.)
Na Young Kim From the Department of Radiology and Research Institute of Radiology (D.W.K., J.H., S.S.L., J.H.K., Y.S.S.) and Department of Clinical Epidemiology and Biostatistics (N.Y.K.), University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea; Department of Brain and Cognitive Engineering (J.S.Y., H.I.S.) and Department of Artificial Intelligence (H.I.S.), Korea University, Seoul, Republic of Korea; Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea (Y.L.); and Department of Radiology, Hanyang University Medical Center, Hanyang University School of Medicine, Seoul, Republic of Korea (B.K.K.)
Yu Sub Sung From the Department of Radiology and Research Institute of Radiology (D.W.K., J.H., S.S.L., J.H.K., Y.S.S.) and Department of Clinical Epidemiology and Biostatistics (N.Y.K.), University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea; Department of Brain and Cognitive Engineering (J.S.Y., H.I.S.) and Department of Artificial Intelligence (H.I.S.), Korea University, Seoul, Republic of Korea; Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea (Y.L.); and Department of Radiology, Hanyang University Medical Center, Hanyang University School of Medicine, Seoul, Republic of Korea (B.K.K.)
Jee Seok Yoon From the Department of Radiology and Research Institute of Radiology (D.W.K., J.H., S.S.L., J.H.K., Y.S.S.) and Department of Clinical Epidemiology and Biostatistics (N.Y.K.), University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea; Department of Brain and Cognitive Engineering (J.S.Y., H.I.S.) and Department of Artificial Intelligence (H.I.S.), Korea University, Seoul, Republic of Korea; Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea (Y.L.); and Department of Radiology, Hanyang University Medical Center, Hanyang University School of Medicine, Seoul, Republic of Korea (B.K.K.)
Heung-Il Suk From the Department of Radiology and Research Institute of Radiology (D.W.K., J.H., S.S.L., J.H.K., Y.S.S.) and Department of Clinical Epidemiology and Biostatistics (N.Y.K.), University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea; Department of Brain and Cognitive Engineering (J.S.Y., H.I.S.) and Department of Artificial Intelligence (H.I.S.), Korea University, Seoul, Republic of Korea; Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea (Y.L.); and Department of Radiology, Hanyang University Medical Center, Hanyang University School of Medicine, Seoul, Republic of Korea (B.K.K.)
Yedaun Lee From the Department of Radiology and Research Institute of Radiology (D.W.K., J.H., S.S.L., J.H.K., Y.S.S.) and Department of Clinical Epidemiology and Biostatistics (N.Y.K.), University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea; Department of Brain and Cognitive Engineering (J.S.Y., H.I.S.) and Department of Artificial Intelligence (H.I.S.), Korea University, Seoul, Republic of Korea; Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea (Y.L.); and Department of Radiology, Hanyang University Medical Center, Hanyang University School of Medicine, Seoul, Republic of Korea (B.K.K.)
Bo-Kyeong Kang From the Department of Radiology and Research Institute of Radiology (D.W.K., J.H., S.S.L., J.H.K., Y.S.S.) and Department of Clinical Epidemiology and Biostatistics (N.Y.K.), University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea; Department of Brain and Cognitive Engineering (J.S.Y., H.I.S.) and Department of Artificial Intelligence (H.I.S.), Korea University, Seoul, Republic of Korea; Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea (Y.L.); and Department of Radiology, Hanyang University Medical Center, Hanyang University School of Medicine, Seoul, Republic of Korea (B.K.K.)

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Tang Y, Zheng Y, Chen X, Wang W, Guo Q, Shu J, Wu J, Su S. Identifying Periampullary Regions in MRI Images Using Deep Learning. Front Oncol 2021;11:674579. [PMID: 34123843 PMCID: PMC8193851 DOI: 10.3389/fonc.2021.674579] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 04/20/2021] [Indexed: 12/15/2022] Open

Cardobi N, Dal Palù A, Pedrini F, Beleù A, Nocini R, De Robertis R, Ruzzenente A, Salvia R, Montemezzi S, D’Onofrio M. An Overview of Artificial Intelligence Applications in Liver and Pancreatic Imaging. Cancers (Basel) 2021;13:2162. [PMID: 33946223 PMCID: PMC8124771 DOI: 10.3390/cancers13092162] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 04/27/2021] [Accepted: 04/28/2021] [Indexed: 12/11/2022] Open

Virarkar M, Szklaruk J, Jensen CT, Taggart MW, Bhosale P. What's New in Hepatic Steatosis. Semin Ultrasound CT MR 2021;42:405-415. [PMID: 34130852 DOI: 10.1053/j.sult.2021.03.001] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]

Su TH, Wu CH, Kao JH. Artificial intelligence in precision medicine in hepatology. J Gastroenterol Hepatol 2021;36:569-580. [PMID: 33709606 DOI: 10.1111/jgh.15415] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/01/2021] [Revised: 01/19/2021] [Accepted: 01/20/2021] [Indexed: 12/14/2022]

Sung YS, Park B, Park HJ, Lee SS. Radiomics and deep learning in liver diseases. J Gastroenterol Hepatol 2021;36:561-568. [PMID: 33709608 DOI: 10.1111/jgh.15414] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Revised: 01/17/2021] [Accepted: 01/19/2021] [Indexed: 12/14/2022]

Lee CM, Lee SS, Choi WM, Kim KM, Sung YS, Lee S, Lee SJ, Yoon JS, Suk HI. An index based on deep learning-measured spleen volume on CT for the assessment of high-risk varix in B-viral compensated cirrhosis. Eur Radiol 2020;31:3355-3365. [PMID: 33128186 DOI: 10.1007/s00330-020-07430-3] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Revised: 09/05/2020] [Accepted: 10/14/2020] [Indexed: 01/11/2023]

Abstract

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.

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