Loss of diffusion restriction and arterial enhancement accurately predicts complete pathological response following Y-90 Selective Internal Radiation Therapy for hepatocellular carcinoma

Loss of diffusion restriction and arterial enhancement accurately predicts complete pathological response following Y-90 Selective Internal Radiation Therapy for hepatocellular carcinoma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Loss of diffusion restriction and arterial enhancement accurately predicts complete pathological response following Y-90 Selective Internal Radiation Therapy for hepatocellular carcinoma Kah Heng Alexander Lim, Matthew Joel Clifford, William Ormiston, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7040415/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 Feb, 2026 Read the published version in Abdominal Radiology → Version 1 posted 10 You are reading this latest preprint version Abstract Purpose To assess the association of diffusion restriction on diffusion weighted MRI (DWI) with complete pathological necrosis (CPN) in hepatocellular carcinoma (HCC) following selective internal radiation therapy with Yttrium-90 resin microspheres (SIRT). Methods A retrospective cohort study of patients undergoing resection or transplantation for HCC following SIRT was performed. Imaging pre- and post-SIRT was assessed for response to treatment via mRECIST and for the presence of diffusion restriction on DWI. Histological specimens were assessed for complete pathological necrosis (CPN). Results Twenty-nine tumours were included from 25 patients; mRECIST complete response (CR) demonstrated moderate reliability (sensitivity 0.92, specificity 0.68) for predicting CPN. Twelve tumours did not have diffusion restriction on pretreatment DWI; following their exclusion, loss of diffusion restriction demonstrated a weak agreement (sensitivity 0.85, specificity 0.80, Cohen’s Kappa 0.197) for prediction of CPN. The combination of mRECIST CR and loss of diffusion restriction was associated with CPN in 100% (6/6) cases; whilst persistent abnormal diffusion restriction despite mRECIST CR was associated with residual disease in 75% (3/4) cases. Conclusion The combination of mRECIST CR when combined with loss of diffusion restriction appears to reliably predict CPN post SIRT; persistent diffusion restriction despite the findings of mRECIST CR appears to correlate with residual disease. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 7 Introduction Selective internal radiation therapy (SIRT) using Yttrium-90 labelled microspheres has evolved to becoming an invaluable component of the multidisciplinary management of hepatocellular carcinoma. Where residual tumour risk exists, patients who underwent SIRT may progress to surgical resection or transplantation (Pardo, Sangro et al. 2017 , Hamad, Aziz et al. 2023 , Sangro, Argemi et al. 2025 ); however radiological assessment of response following SIRT is challenging due to necrosis of tumour and radiation-induced fibrosis and altered post-contrast enhancement patterns of surrounding parenchyma that confounds volume based assessments. Size change alone is unreliable for assessment of treatment response post SIRT (Kamel, Reyes et al. 2007 ) and enhancement based tools that include mRECIST, EASL, and LIRADS Treatment Response Algorithm (LR-TR) are favoured in clinical and research settings (Riaz, Kulik et al. 2009 , Kokabi, Camacho et al. 2014 , Salem, Johnson et al. 2021 , Hamad, Aziz et al. 2023 ). The gold standard for determination of CPN and residual disease remains histopathological evaluation of the surgical specimen; however surgery post SIRT is challenging, carries significant morbidity (Wright, Marsh et al. 2017 , Mafeld, Littler et al. 2020 ), and may be unnecessary in cases where treatment with SIRT has been definitive and complete pathological necrosis (CPN) of a target lesion is obtained (Vouche, Habib et al. 2014 ). Diffusion-weighted magnetic resonance imaging (DWI) allows assessment of changes in intratumoral water diffusion which can manifest as early as 40 days post SIRT (Deng, Miller et al. 2006 ). Assessment of diffusion restriction is performed either by radiologist’s qualitative interpretation of signal intensity on diffusion weighted sequences, or quantitatively through the measurements of apparent diffusion coefficients; the latter itself requiring manual and arguably subjective segmentation of the treated tumour from surrounding irradiated parenchyma where demarcation may be lost (Vouche, Salem et al. 2015 ). The established tools for assessing treatment response, such as mRECIST and LR-TR, however do not include DWI in assessing for viable disease. The aim of this study was to evaluate the diagnostic value of loss of diffusion restriction assessed qualitatively on DWI as a complement to mRECIST for the prediction of CPN in a population of HCC treated with SIRT, followed by resection or transplantation. Methods Design: A retrospective cohort study of patients with HCC treated with SIRT followed by either liver resection or transplantation from two tertiary centres in Western Australia. Participants: A search of institutional liver transplant, interventional radiology and anatomical pathology databases were performed to identify patients from January 2010 to July 2024. Criteria for inclusion was a patient with radiologically or biopsy diagnosed HCC and previous SIRT treatment followed by either surgical resection or liver transplantation, that had at least one pre-operative MRI scan to assess for DWI. For patients with multiple tumours, radiological and macroscopic photographic correlation was performed to ensure correct lesion assignment, which was limited to the two largest lesions. Radiological: Pre and post-SIRT imaging studies were retrieved and read by a consultant interventional radiologist (MC) with indeterminate cases discussed with a second consultant interventional radiologist for a consensus read. Lesion size was taken as the maximal arterial enhancement diameter pre-SIRT. Post treatment tumours were classed using mRECIST criteria (Lencioni and Llovet 2010 ). Pre-treatment and post treatment DWI lesional signal relative to background liver was assessed; tumours were categorised qualitatively as having either complete loss of diffusion restriction (Fig. 1c-1d), persistent abnormal signal (Fig. 2c and 2d), or no diffusion restriction at baseline. Heavily T2 weighted sequences and T1 weighted sequences were used to identify potential sources of error in DWI assessment such as T2 shine through and hemorrhage. Histopathology: Macroscopic photos of resections or explanted livers were assessed and slides retrieved for histological scoring by a consultant pathologist specialising in liver pathology (TK). Pathological response were scored as complete pathological necrosis (CPN; 0% viable tumour cells), followed by 50% residual viable tumour. Analysis: Median and interquartile ranges (IQR) were presented for continuous variables and differences assessed using Mann Whitney U test with a p value of < 0.05 for significance. For analysis, mRECIST scoring was binarized into complete response (CR) vs no CR (partial response, stable or progressive disease categories); DWI was categorised into loss of diffusion restriction, persistent diffusion restriction, or no baseline diffusion restriction. Pathological assessment was similarly binarized to CPN or residual disease. Performance of mRECIST or DWI in predicting pathologic complete necrosis was expressed in raw agreement, sensitivity and specificity; strength of agreement was assessed using Cohen’s Kappa and Prevalence-adjusted Bias-adjusted Kappa (PABAK) with values 0.4–.75 taken as moderate to good agreement. Ethics: Approval was obtained from the Human Research Ethics Committee of Sir Charles Gairdner Hospital (Project Record Number: RGS6872) Results A total of 29 HCC tumours from 25 patients fulfilled criteria for inclusion; of whom 40% underwent transplant and 60% resection. Median age at time of SIRT was 62 years, with majority male (88%). All cases had the diagnosis of HCC made on radiological and clinical grounds in a multidisciplinary consensus meeting, with an additional 11 cases (44%) undergoing pre-treatment biopsy for confirmation. As expected from the inclusion criteria (i.e. candidates for SIRT and subsequent surgery) the majority of included patients had an ECOG of 0 (n = 20; 80%) and were either non-cirrhotic (n = 9, 36%) or had Child-Pugh A cirrhosis (n = 13, 52%). The demographics, liver disease etiology, and prior treatment history for the cohort are expanded in Table 1 . Table 1 Demographics, etiology, and severity of liver disease for patient cohort (n = 25) Median (IQR)/Frequency (%) Demographic Age 61.76 (36.72–78.14) Male gender 22 (88%) Etiology Hepatitis B 4 (16%) Hepatitis C 2 (8%) Alcohol 2 (8%) Viral/alcohol 7 (28%) NAFLD 6 (25%) Other/unknown 4 (16%) Child-Pugh A 13 (52%) B 3 (12%) Non-cirrhotic 9 (36%) ECOG performance status 0 20 (80%) 1 5 (20%) BCLC Stage 0 1 (4%) A 3 (12%) B 15 (60%) C 6 (24%) MELD score 8.6 (6–15) ECOG – Eastern Cooperative Oncology Group; BCLC – Barcelona Clinic Liver Cancer; MELD – Model for End stage Liver Disease Table 2 Demographics, tumour size, and time interval from SIRT to surgery for residual disease vs CPN groups Residual disease (n = 16) Complete Pathologic Necrosis (n = 13) P value Age (years) 63.5 (58.25-68) 64 (46-74.5) 0.98 * Sex M = 13 F = 3 M = 13 0.23 Ɨ Max enhancement diameter pretreatment (mm) 65.5 (44.25-93.00) 51 (28.00-60.50) 0.050 * Time from SIRT to surgery (days) 158 (141.5-387.25) 330 (166.5–392) 0.184 * *Mann Whitney U; Ɨ Fishers Exact test Thirteen tumours (44.8%) demonstrated CPN on resection or explant pathological assessment; age, sex, and interval (in days) from SIRT to surgery were not associated with CPN. Lesion size (taken as maximum enhancement diameter pre-SIRT) was significantly smaller for complete response (51mm [28.00-60.50] vs incomplete response 65.5mm [44.25-93.00] (p = 0.05). One tumour did not have appropriate posttreatment imaging for mRECIST scoring (Fig. 1, Image 6). The ability of mRECIST to predict compete pathologic necrosis was moderate (raw agreement = 0.79, sensitivity = 0.92, specificity = 0.68, Cohen’s Kappa 0.593, PABAK = 0.58). Regarding the association of time interval from SIRT to mRECIST findings on re-imaging; there was no significant difference in median number of days from SIRT to imaging for mRECIST groups (CR = 177 [107–321] days; no CR = 163 [86–341] days, p = 0.65). Eleven tumours did not demonstrate diffusion restriction prior to treatment (e.g. Figure B) and were excluded from further statistical assessment; for the remaining 17 tumours the agreement between loss of diffusion restriction and complete pathologic response was moderate (raw agreement = 0.82, Sensitivity 0.85, Specificity = 0.80, Cohen’s Kappa = 0.197, PABAK = 0.647). Regarding the association of time interval from SIRT to DWI findings on re-imaging; there was no significant difference in median number of days from SIRT to DWI findings (loss of diffusion restriction = 180 [103–230] days; persistent diffusion restriction = 127 [99–370] days, p = 0.70). A flowchart is presented in Fig. 1 depicting the relationship between mRECIST, DWI, and pathological outcome. MRECIST CR and loss of diffusion restriction occurred in 6 tumours; all of which had CPN (Image 1). MRECIST CR and persistence of diffusion restriction occurred in 4 tumours, of which 3 (75%) had residual disease (Images 2 and 3), and 1 had complete response (Image 4) Discussion Evaluation of post SIRT treatment response is beset by challenges in interpretation of post radiation field changes and heterogeneity in assessment tools with limited validation. While the use of SIRT as a ‘bridge’ or ‘downstaging’ strategy for resection or transplant is increasing in popularity, overall candidate numbers remain small; the largest international series from 16 centres over 16 years produced only 100 cases with usable data (Pardo, Sangro et al. 2017 ). Low rates of post-SIRT surgery corresponds to a low yield of pathologic specimens for the confirmation of CPN, meaning that surrogate markers such as survival metrics and longitudinal imaging are frequently used for validation of posttreatment assessment tools. What is available in terms of pathologic-radiologic validation comes from transplant series examining loss of arterial enhancement as a predictor of complete response. A series of 37 tumours found EASL CR (equivalent to mRECIST CR) to have 100% specificity but only 52% sensitivity for pathological complete necrosis (Riaz, Kulik et al. 2009 ). The low sensitivity for loss of arterial enhancement is consistent with another multicentre series of 33 transplants which found only 50% of tumours exhibiting mRECIST CR had pathological complete necrosis at explant (Vouche, Habib et al. 2014 ). Another series of 37 tumours reported a statistically significant correlation of mRECIST response to amount of tumour necrosis at histopathologic assessment however did not include specific diagnostic accuracy metrics (Toskich, Vidal et al. 2021 ). The use of DWI in the assessment of treatment response is principled on changes in water movement that reflect cellular changes i.e. cell shrinkage and increased membrane permeability (Theilmann, Borders et al. 2004 ). While extensively investigated outside of liver imaging, few studies have expanded to DWI applications in the post SIRT setting. Within the metastatic population, increasing ADC values have been shown to correlate with lesional shrinkage (Dudeck, Zeile et al. 2010 , Kukuk, Murtz et al. 2014 ). Kamel and colleagues showed increases in ADC post SIRT in unresectable HCC (Kamel, Reyes et al. 2007 ). In a subgroup analysis of 21 HCC explants, Vouche et al found ADC and mRECIST scoring at 1 and 3 months post SIRT as poor predictors of CPN (Vouche, Salem et al. 2015 ). A series on 18 unresectable HCC found increasing ADC to correlate with mRECIST grades (Kokabi, Camacho et al. 2014 ). In our study, we opted for visual assessment of diffusion restriction instead of numerical ADC calculations. Despite ADC being the dominant measure for assessment of posttreatment diffusion restriction in the literature (Sobeh, Inbar et al. 2023 ); its validation in a post SIRT HCC setting is limited. We argue that ADC measures that are highly susceptible to motion artefact and misregistration, do not capture tumour heterogeneity, suffer from high interobserver variability in region-of-interest segmentation (due to post-SIRT changes) and have never been compared head-to-head with subjective assessment of diffusion restriction which is routine at our institution. In our study, we identified 11 baseline non-diffusion restricting tumours and excluded them from subsequent analysis of post-treatment DWI changes. The significance of pre-treatment DWI on tumour response is unclear; in the earlier cited study of 21 HCC treated with SIRT followed by transplant, mean tumour ADC at baseline was not shown to predict response to treatment (Vouche, Salem et al. 2015 ). The potential of DWI as a biomarker has been summarised in a review (Gluskin, Chegai et al. 2016 ) citing a number of studies that associate diffusion restriction/higher ADC values with higher histological grade/poor tumour differentiation; however the authors note that the converse relationship (i.e. non diffusion restricting/low ADC value with low histological grade/well differentiation tumour) has not been reliably demonstrated in the literature. Given our practice of non-routine pre-treatment biopsy, this relationship is difficult to ascertain and should form the subject of future enquiry. While underpowered to examine combined effects of both tests, the current study demonstrates the complementary role of DWI to augment enhancement-based assessment of response. In keeping with findings from others (Riaz, Kulik et al. 2009 , Vouche, Salem et al. 2015 ), the modest specificity (0.68) for mRECIST CR/loss of enhancement highlights the tendency to missing the diagnosis of residual disease, making it an unreliable predictor of complete response when used alone. Cases such as Image 2 demonstrate the value in adding DWI in detecting residual tumour in a largely devascularised tumour post SIRT. Persistence of diffusion restriction may be due to factors other than residual tumour, such as intertumoral haemorrhage ( Image 4 ) that may demonstrate resolution on interval imaging. Treated tumours demonstrating both loss of enhancement AND loss of diffusion restriction ( Image 1 ) reflect a population with high likelihood of CPN and potential candidates for a watch and wait approach, within the appropriate clinical context. Arterial enhancement persisted despite loss of diffusion restriction in a case of well differentiated HCC (Image 6). Lower tumour grades have shown reduced signal intensity on DWI, which is thought to reflect similarities in the cellularity and microstructure of well differentiated lesions to surrounding background fibrotic parenchyma (Vandecaveye, De Keyzer et al. 2009 , Kim, Kim et al. 2011 ). Limitations in the study include small sample size, non-quantitative assessment of diffusion restriction, retrospective nature, and heterogeneity in time interval between SIRT-imaging-surgery. Post SIRT protocolled response imaging when done too early e.g. 1–3 months may underestimate treatment response (Vouche, Salem et al. 2015 ). By assessing imaging performed closest to surgery we obtained the most representative radiologic-pathologic correlation, as well as maximised the time from treatment to transplant to ensure full treatment effect (Riaz, Kulik et al. 2009 , Toskich, Vidal et al. 2021 ). Conclusion The combination of complete loss of enhancement and diffusion restriction accurately predicts CPN; conversely persistence of diffusion restriction despite complete loss of enhancement predicts residual disease in 75% of cases in our series. DWI is an important adjunct tool in conjunction with mRECIST for post-SIRT response assessment. Declarations Author Contribution K.H.A.L. is credited with conception of idea, study design, manuscript writing, preparation of table 2 and figures 1-7M.J.C contributed to design, performed radiology reads and data collection, and reviewed the manuscript T.K performed pathology reads and reviewed the manuscriptW.O is credited with conception of idea, study design, and reviewed the manuscriptK.Q.S.N is credited with data collection and preparation of table 1A.W. is credited with data collection and reviewed the manuscriptM.B is credited with data collection and reviewed the manuscriptL.D and M.B reviewed the manuscriptJ.T. and L.J.M are credited with conception of idea and reviewed the manuscript Acknowledgement Prof Max Bulsara, University of Notre Dame, Western Australia for assisting with biostatistical analysis and reporting. References Deng, J., F. H. Miller, T. K. Rhee, K. T. Sato, M. F. Mulcahy, L. M. Kulik, R. Salem, R. A. Omary and A. C. Larson (2006). 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Cite Share Download PDF Status: Published Journal Publication published 09 Feb, 2026 Read the published version in Abdominal Radiology → Version 1 posted Editorial decision: Revision requested 12 Oct, 2025 Reviews received at journal 05 Oct, 2025 Reviewers agreed at journal 22 Sep, 2025 Reviews received at journal 31 Aug, 2025 Reviewers agreed at journal 27 Aug, 2025 Reviewers agreed at journal 12 Aug, 2025 Reviewers invited by journal 06 Jul, 2025 Editor assigned by journal 03 Jul, 2025 Submission checks completed at journal 03 Jul, 2025 First submitted to journal 03 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Steven","lastName":"Ngo","suffix":""},{"id":481198897,"identity":"509ee521-f884-48fd-8658-48a4c5a83420","order_by":5,"name":"Alistair Rowcroft","email":"","orcid":"","institution":"Fiona Stanley Hospital","correspondingAuthor":false,"prefix":"","firstName":"Alistair","middleName":"","lastName":"Rowcroft","suffix":""},{"id":481198898,"identity":"20f398f8-bf18-4c48-9e4d-a5ce50748c1d","order_by":6,"name":"Lingjun Mou","email":"","orcid":"","institution":"Sir Charles Gairdner Hospital","correspondingAuthor":false,"prefix":"","firstName":"Lingjun","middleName":"","lastName":"Mou","suffix":""},{"id":481198899,"identity":"a57132e1-daa4-4ed2-914c-d0faf8035dcc","order_by":7,"name":"Luc Delriviere","email":"","orcid":"","institution":"Sir Charles Gairdner Hospital","correspondingAuthor":false,"prefix":"","firstName":"Luc","middleName":"","lastName":"Delriviere","suffix":""},{"id":481198900,"identity":"665d47bd-b3ec-4692-870f-ab80fa191ed7","order_by":8,"name":"Michael 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Tibballs","email":"data:image/png;base64,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","orcid":"","institution":"Sir Charles Gairdner Hospital","correspondingAuthor":true,"prefix":"","firstName":"Jonathan","middleName":"","lastName":"Tibballs","suffix":""}],"badges":[],"createdAt":"2025-07-03 17:08:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7040415/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7040415/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00261-026-05377-5","type":"published","date":"2026-02-09T15:56:50+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":86626791,"identity":"a89d8e54-b0a7-4190-a25f-40fd0384c37f","added_by":"auto","created_at":"2025-07-14 05:31:41","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":66136,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7040415/v1/6e1cc6fd05c892c715dceeec.png"},{"id":86626805,"identity":"a924f078-1a8a-4619-8e78-054dc3234fa7","added_by":"auto","created_at":"2025-07-14 05:31:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":697143,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"Image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7040415/v1/f4c49ec6346a332ac57ee872.png"},{"id":86626795,"identity":"135c087b-9325-47d6-9e63-a7508f4fd129","added_by":"auto","created_at":"2025-07-14 05:31:41","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":423428,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"Image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7040415/v1/7c7a456fe14a53a4dee57eca.png"},{"id":86626800,"identity":"3156d8eb-a87e-4c8c-bc47-db80c1ff2321","added_by":"auto","created_at":"2025-07-14 05:31:42","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":549240,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"Image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7040415/v1/95307a11a149337b74486efd.png"},{"id":86626807,"identity":"dbae3e88-02ac-4fc6-a805-ac5d45d033ad","added_by":"auto","created_at":"2025-07-14 05:31:42","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":654469,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"Image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7040415/v1/80f754fc7ffa4332656191df.png"},{"id":86626798,"identity":"18ddb952-ccec-48c2-bf91-982550407cc0","added_by":"auto","created_at":"2025-07-14 05:31:42","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":261177,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"Image6.png","url":"https://assets-eu.researchsquare.com/files/rs-7040415/v1/6522abeecf3672c736faa712.png"},{"id":102785164,"identity":"fd2c0c65-e416-4e49-9757-cfbfe8548c55","added_by":"auto","created_at":"2026-02-16 16:00:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3090498,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7040415/v1/19cd8a26-0112-45c9-bd06-80a0c62abb1e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eLoss of diffusion restriction and arterial enhancement accurately predicts complete pathological response following Y-90 Selective Internal Radiation Therapy for hepatocellular carcinoma\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSelective internal radiation therapy (SIRT) using Yttrium-90 labelled microspheres has evolved to becoming an invaluable component of the multidisciplinary management of hepatocellular carcinoma. Where residual tumour risk exists, patients who underwent SIRT may progress to surgical resection or transplantation (Pardo, Sangro et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Hamad, Aziz et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, Sangro, Argemi et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e); however radiological assessment of response following SIRT is challenging due to necrosis of tumour and radiation-induced fibrosis and altered post-contrast enhancement patterns of surrounding parenchyma that confounds volume based assessments.\u003c/p\u003e\u003cp\u003eSize change alone is unreliable for assessment of treatment response post SIRT (Kamel, Reyes et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) and enhancement based tools that include mRECIST, EASL, and LIRADS Treatment Response Algorithm (LR-TR) are favoured in clinical and research settings (Riaz, Kulik et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2009\u003c/span\u003e, Kokabi, Camacho et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, Salem, Johnson et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Hamad, Aziz et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The gold standard for determination of CPN and residual disease remains histopathological evaluation of the surgical specimen; however surgery post SIRT is challenging, carries significant morbidity (Wright, Marsh et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Mafeld, Littler et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and may be unnecessary in cases where treatment with SIRT has been definitive and complete pathological necrosis (CPN) of a target lesion is obtained (Vouche, Habib et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDiffusion-weighted magnetic resonance imaging (DWI) allows assessment of changes in intratumoral water diffusion which can manifest as early as 40 days post SIRT (Deng, Miller et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Assessment of diffusion restriction is performed either by radiologist\u0026rsquo;s qualitative interpretation of signal intensity on diffusion weighted sequences, or quantitatively through the measurements of apparent diffusion coefficients; the latter itself requiring manual and arguably subjective segmentation of the treated tumour from surrounding irradiated parenchyma where demarcation may be lost (Vouche, Salem et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The established tools for assessing treatment response, such as mRECIST and LR-TR, however do not include DWI in assessing for viable disease.\u003c/p\u003e\u003cp\u003eThe aim of this study was to evaluate the diagnostic value of loss of diffusion restriction assessed qualitatively on DWI as a complement to mRECIST for the prediction of CPN in a population of HCC treated with SIRT, followed by resection or transplantation.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eDesign: A retrospective cohort study of patients with HCC treated with SIRT followed by either liver resection or transplantation from two tertiary centres in Western Australia.\u003c/p\u003e\u003cp\u003eParticipants: A search of institutional liver transplant, interventional radiology and anatomical pathology databases were performed to identify patients from January 2010 to July 2024. Criteria for inclusion was a patient with radiologically or biopsy diagnosed HCC and previous SIRT treatment followed by either surgical resection or liver transplantation, that had at least one pre-operative MRI scan to assess for DWI. For patients with multiple tumours, radiological and macroscopic photographic correlation was performed to ensure correct lesion assignment, which was limited to the two largest lesions.\u003c/p\u003e\u003cp\u003eRadiological: Pre and post-SIRT imaging studies were retrieved and read by a consultant interventional radiologist (MC) with indeterminate cases discussed with a second consultant interventional radiologist for a consensus read. Lesion size was taken as the maximal arterial enhancement diameter pre-SIRT.\u003c/p\u003e\u003cp\u003ePost treatment tumours were classed using mRECIST criteria (Lencioni and Llovet \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Pre-treatment and post treatment DWI lesional signal relative to background liver was assessed; tumours were categorised qualitatively as having either complete loss of diffusion restriction (Fig.\u0026nbsp;1c-1d), persistent abnormal signal (Fig.\u0026nbsp;2c and 2d), or no diffusion restriction at baseline. Heavily T2 weighted sequences and T1 weighted sequences were used to identify potential sources of error in DWI assessment such as T2 shine through and hemorrhage.\u003c/p\u003e\u003cp\u003eHistopathology: Macroscopic photos of resections or explanted livers were assessed and slides retrieved for histological scoring by a consultant pathologist specialising in liver pathology (TK). Pathological response were scored as complete pathological necrosis (CPN; 0% viable tumour cells), followed by \u0026lt;\u0026thinsp;5%, 5\u0026ndash;10%, 10\u0026ndash;20%, 20\u0026ndash;50%, and \u0026gt;\u0026thinsp;50% residual viable tumour.\u003c/p\u003e\u003cp\u003eAnalysis: Median and interquartile ranges (IQR) were presented for continuous variables and differences assessed using Mann Whitney U test with a p value of \u0026lt;\u0026thinsp;0.05 for significance. For analysis, mRECIST scoring was binarized into complete response (CR) vs no CR (partial response, stable or progressive disease categories); DWI was categorised into loss of diffusion restriction, persistent diffusion restriction, or no baseline diffusion restriction. Pathological assessment was similarly binarized to CPN or residual disease. Performance of mRECIST or DWI in predicting pathologic complete necrosis was expressed in raw agreement, sensitivity and specificity; strength of agreement was assessed using Cohen\u0026rsquo;s Kappa and Prevalence-adjusted Bias-adjusted Kappa (PABAK) with values 0.4\u0026ndash;.75 taken as moderate to good agreement.\u003c/p\u003e\u003cp\u003e Ethics: Approval was obtained from the Human Research Ethics Committee of Sir Charles Gairdner Hospital (Project Record Number: RGS6872)\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 29 HCC tumours from 25 patients fulfilled criteria for inclusion; of whom 40% underwent transplant and 60% resection. Median age at time of SIRT was 62 years, with majority male (88%). All cases had the diagnosis of HCC made on radiological and clinical grounds in a multidisciplinary consensus meeting, with an additional 11 cases (44%) undergoing pre-treatment biopsy for confirmation. As expected from the inclusion criteria (i.e. candidates for SIRT and subsequent surgery) the majority of included patients had an ECOG of 0 (n\u0026thinsp;=\u0026thinsp;20; 80%) and were either non-cirrhotic (n\u0026thinsp;=\u0026thinsp;9, 36%) or had Child-Pugh A cirrhosis (n\u0026thinsp;=\u0026thinsp;13, 52%). The demographics, liver disease etiology, and prior treatment history for the cohort are expanded in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDemographics, etiology, and severity of liver disease for patient cohort (n\u0026thinsp;=\u0026thinsp;25)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMedian (IQR)/Frequency (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eDemographic\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e61.76 (36.72\u0026ndash;78.14)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale gender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22 (88%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEtiology\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHepatitis B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4 (16%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHepatitis C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlcohol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eViral/alcohol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7 (28%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNAFLD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6 (25%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther/unknown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4 (16%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eChild-Pugh\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13 (52%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 (12%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-cirrhotic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9 (36%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eECOG performance status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20 (80%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 (20%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBCLC Stage\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 (12%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15 (60%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6 (24%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMELD score\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.6 (6\u0026ndash;15)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"2\"\u003eECOG \u0026ndash; Eastern Cooperative Oncology Group; BCLC \u0026ndash; Barcelona Clinic Liver Cancer; MELD \u0026ndash; Model for End stage Liver Disease\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDemographics, tumour size, and time interval from SIRT to surgery for residual disease vs CPN groups\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eResidual disease (n\u0026thinsp;=\u0026thinsp;16)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eComplete Pathologic Necrosis (n\u0026thinsp;=\u0026thinsp;13)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e63.5 (58.25-68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e64 (46-74.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.98 *\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eM\u0026thinsp;=\u0026thinsp;13\u003c/p\u003e\u003cp\u003eF\u0026thinsp;=\u0026thinsp;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eM\u0026thinsp;=\u0026thinsp;13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.23 Ɨ\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMax enhancement diameter pretreatment (mm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e65.5 (44.25-93.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e51 (28.00-60.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.050 *\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTime from SIRT to surgery (days)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e158 (141.5-387.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e330 (166.5\u0026ndash;392)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.184 *\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e*Mann Whitney U; Ɨ Fishers Exact test\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThirteen tumours (44.8%) demonstrated CPN on resection or explant pathological assessment; age, sex, and interval (in days) from SIRT to surgery were not associated with CPN. Lesion size (taken as maximum enhancement diameter pre-SIRT) was significantly smaller for complete response (51mm [28.00-60.50] vs incomplete response 65.5mm [44.25-93.00] (p\u0026thinsp;=\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003eOne tumour did not have appropriate posttreatment imaging for mRECIST scoring (Fig.\u0026nbsp;1, Image 6). The ability of mRECIST to predict compete pathologic necrosis was moderate (raw agreement\u0026thinsp;=\u0026thinsp;0.79, sensitivity\u0026thinsp;=\u0026thinsp;0.92, specificity\u0026thinsp;=\u0026thinsp;0.68, Cohen\u0026rsquo;s Kappa 0.593, PABAK\u0026thinsp;=\u0026thinsp;0.58). Regarding the association of time interval from SIRT to mRECIST findings on re-imaging; there was no significant difference in median number of days from SIRT to imaging for mRECIST groups (CR\u0026thinsp;=\u0026thinsp;177 [107\u0026ndash;321] days; no CR\u0026thinsp;=\u0026thinsp;163 [86\u0026ndash;341] days, p\u0026thinsp;=\u0026thinsp;0.65).\u003c/p\u003e\u003cp\u003eEleven tumours did not demonstrate diffusion restriction prior to treatment (e.g. Figure B) and were excluded from further statistical assessment; for the remaining 17 tumours the agreement between loss of diffusion restriction and complete pathologic response was moderate (raw agreement\u0026thinsp;=\u0026thinsp;0.82, Sensitivity 0.85, Specificity\u0026thinsp;=\u0026thinsp;0.80, Cohen\u0026rsquo;s Kappa\u0026thinsp;=\u0026thinsp;0.197, PABAK\u0026thinsp;=\u0026thinsp;0.647). Regarding the association of time interval from SIRT to DWI findings on re-imaging; there was no significant difference in median number of days from SIRT to DWI findings (loss of diffusion restriction\u0026thinsp;=\u0026thinsp;180 [103\u0026ndash;230] days; persistent diffusion restriction\u0026thinsp;=\u0026thinsp;127 [99\u0026ndash;370] days, p\u0026thinsp;=\u0026thinsp;0.70).\u003c/p\u003e\u003cp\u003eA flowchart is presented in Fig.\u0026nbsp;1 depicting the relationship between mRECIST, DWI, and pathological outcome. MRECIST CR and loss of diffusion restriction occurred in 6 tumours; all of which had CPN (Image 1). MRECIST CR and persistence of diffusion restriction occurred in 4 tumours, of which 3 (75%) had residual disease (Images 2 and 3), and 1 had complete response (Image 4)\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eEvaluation of post SIRT treatment response is beset by challenges in interpretation of post radiation field changes and heterogeneity in assessment tools with limited validation. While the use of SIRT as a \u0026lsquo;bridge\u0026rsquo; or \u0026lsquo;downstaging\u0026rsquo; strategy for resection or transplant is increasing in popularity, overall candidate numbers remain small; the largest international series from 16 centres over 16 years produced only 100 cases with usable data (Pardo, Sangro et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Low rates of post-SIRT surgery corresponds to a low yield of pathologic specimens for the confirmation of CPN, meaning that surrogate markers such as survival metrics and longitudinal imaging are frequently used for validation of posttreatment assessment tools.\u003c/p\u003e\u003cp\u003eWhat is available in terms of pathologic-radiologic validation comes from transplant series examining loss of arterial enhancement as a predictor of complete response. A series of 37 tumours found EASL CR (equivalent to mRECIST CR) to have 100% specificity but only 52% sensitivity for pathological complete necrosis (Riaz, Kulik et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The low sensitivity for loss of arterial enhancement is consistent with another multicentre series of 33 transplants which found only 50% of tumours exhibiting mRECIST CR had pathological complete necrosis at explant (Vouche, Habib et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Another series of 37 tumours reported a statistically significant correlation of mRECIST response to amount of tumour necrosis at histopathologic assessment however did not include specific diagnostic accuracy metrics (Toskich, Vidal et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe use of DWI in the assessment of treatment response is principled on changes in water movement that reflect cellular changes i.e. cell shrinkage and increased membrane permeability (Theilmann, Borders et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). While extensively investigated outside of liver imaging, few studies have expanded to DWI applications in the post SIRT setting. Within the metastatic population, increasing ADC values have been shown to correlate with lesional shrinkage (Dudeck, Zeile et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2010\u003c/span\u003e, Kukuk, Murtz et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Kamel and colleagues showed increases in ADC post SIRT in unresectable HCC (Kamel, Reyes et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). In a subgroup analysis of 21 HCC explants, Vouche et al found ADC and mRECIST scoring at 1 and 3 months post SIRT as poor predictors of CPN (Vouche, Salem et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). A series on 18 unresectable HCC found increasing ADC to correlate with mRECIST grades (Kokabi, Camacho et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn our study, we opted for visual assessment of diffusion restriction instead of numerical ADC calculations. Despite ADC being the dominant measure for assessment of posttreatment diffusion restriction in the literature (Sobeh, Inbar et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e); its validation in a post SIRT HCC setting is limited. We argue that ADC measures that are highly susceptible to motion artefact and misregistration, do not capture tumour heterogeneity, suffer from high interobserver variability in region-of-interest segmentation (due to post-SIRT changes) and have never been compared head-to-head with subjective assessment of diffusion restriction which is routine at our institution.\u003c/p\u003e\u003cp\u003eIn our study, we identified 11 baseline non-diffusion restricting tumours and excluded them from subsequent analysis of post-treatment DWI changes. The significance of pre-treatment DWI on tumour response is unclear; in the earlier cited study of 21 HCC treated with SIRT followed by transplant, mean tumour ADC at baseline was not shown to predict response to treatment (Vouche, Salem et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The potential of DWI as a biomarker has been summarised in a review (Gluskin, Chegai et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) citing a number of studies that associate diffusion restriction/higher ADC values with higher histological grade/poor tumour differentiation; however the authors note that the converse relationship (i.e. non diffusion restricting/low ADC value with low histological grade/well differentiation tumour) has not been reliably demonstrated in the literature. Given our practice of non-routine pre-treatment biopsy, this relationship is difficult to ascertain and should form the subject of future enquiry.\u003c/p\u003e\u003cp\u003eWhile underpowered to examine combined effects of both tests, the current study demonstrates the complementary role of DWI to augment enhancement-based assessment of response. In keeping with findings from others (Riaz, Kulik et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2009\u003c/span\u003e, Vouche, Salem et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), the modest specificity (0.68) for mRECIST CR/loss of enhancement highlights the tendency to missing the diagnosis of residual disease, making it an unreliable predictor of complete response when used alone. Cases such as \u003cb\u003eImage 2\u003c/b\u003e demonstrate the value in adding DWI in detecting residual tumour in a largely devascularised tumour post SIRT.\u003c/p\u003e\u003cp\u003ePersistence of diffusion restriction may be due to factors other than residual tumour, such as intertumoral haemorrhage (\u003cb\u003eImage 4\u003c/b\u003e) that may demonstrate resolution on interval imaging. Treated tumours demonstrating both loss of enhancement AND loss of diffusion restriction (\u003cb\u003eImage 1\u003c/b\u003e) reflect a population with high likelihood of CPN and potential candidates for a watch and wait approach, within the appropriate clinical context.\u003c/p\u003e\u003cp\u003eArterial enhancement persisted despite loss of diffusion restriction in a case of well differentiated HCC (Image 6). Lower tumour grades have shown reduced signal intensity on DWI, which is thought to reflect similarities in the cellularity and microstructure of well differentiated lesions to surrounding background fibrotic parenchyma (Vandecaveye, De Keyzer et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2009\u003c/span\u003e, Kim, Kim et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eLimitations in the study include small sample size, non-quantitative assessment of diffusion restriction, retrospective nature, and heterogeneity in time interval between SIRT-imaging-surgery. Post SIRT protocolled response imaging when done too early e.g. 1\u0026ndash;3 months may underestimate treatment response (Vouche, Salem et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). By assessing imaging performed closest to surgery we obtained the most representative radiologic-pathologic correlation, as well as maximised the time from treatment to transplant to ensure full treatment effect (Riaz, Kulik et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2009\u003c/span\u003e, Toskich, Vidal et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe combination of complete loss of enhancement and diffusion restriction accurately predicts CPN; conversely persistence of diffusion restriction despite complete loss of enhancement predicts residual disease in 75% of cases in our series. DWI is an important adjunct tool in conjunction with mRECIST for post-SIRT response assessment.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eK.H.A.L. is credited with conception of idea, study design, manuscript writing, preparation of table 2 and figures 1-7M.J.C contributed to design, performed radiology reads and data collection, and reviewed the manuscript T.K performed pathology reads and reviewed the manuscriptW.O is credited with conception of idea, study design, and reviewed the manuscriptK.Q.S.N is credited with data collection and preparation of table 1A.W. is credited with data collection and reviewed the manuscriptM.B is credited with data collection and reviewed the manuscriptL.D and M.B reviewed the manuscriptJ.T. and L.J.M are credited with conception of idea and reviewed the manuscript\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eProf Max Bulsara, University of Notre Dame, Western Australia for assisting with biostatistical analysis and reporting.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDeng, J., F. H. Miller, T. K. Rhee, K. T. Sato, M. F. Mulcahy, L. M. Kulik, R. Salem, R. A. Omary and A. C. Larson (2006). \"Diffusion-weighted MR imaging for determination of hepatocellular carcinoma response to yttrium-90 radioembolization.\" J Vasc Interv Radiol 17(7): 1195\u0026ndash;1200.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDudeck, O., M. Zeile, C. Wybranski, A. Schulmeister, F. Fischbach, M. Pech, G. Wieners, R. Ruhl, O. Grosser, H. Amthauer and J. Ricke (2010). \"Early prediction of anticancer effects with diffusion-weighted MR imaging in patients with colorectal liver metastases following selective internal radiotherapy.\" Eur Radiol 20(11): 2699\u0026ndash;2706.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGluskin, J. S., F. Chegai, S. Monti, E. Squillaci and L. Mannelli (2016). \"Hepatocellular Carcinoma and Diffusion-Weighted MRI: Detection and Evaluation of Treatment Response.\" J Cancer 7(11): 1565\u0026ndash;1570.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHamad, A., H. Aziz, I. R. Kamel, D. A. Diaz and T. M. Pawlik (2023). \"Yttrium-90 Radioembolization: Current Indications and Outcomes.\" Journal of Gastrointestinal Surgery 27(3): 604\u0026ndash;614.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKamel, I. R., D. K. Reyes, E. Liapi, D. A. Bluemke and J. F. Geschwind (2007). \"Functional MR imaging assessment of tumor response after 90Y microsphere treatment in patients with unresectable hepatocellular carcinoma.\" J Vasc Interv Radiol 18(1 Pt 1): 49\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKim, Y. K., C. S. Kim, Y. M. Han and Y. H. Lee (2011). \"Detection of liver malignancy with gadoxetic acid-enhanced MRI: is addition of diffusion-weighted MRI beneficial?\" Clin Radiol 66(6): 489\u0026ndash;496.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKokabi, N., J. C. Camacho, M. Xing, D. Qiu, H. Kitajima, P. K. Mittal and H. S. Kim (2014). \"Apparent diffusion coefficient quantification as an early imaging biomarker of response and predictor of survival following yttrium-90 radioembolization for unresectable infiltrative hepatocellular carcinoma with portal vein thrombosis.\" Abdom Imaging 39(5): 969\u0026ndash;978.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKukuk, G. M., P. Murtz, F. Traber, C. Meyer, J. Ullrich, J. Gieseke, H. Ahmadzadehfar, S. Ezziddin, H. H. Schild and W. A. Willinek (2014). \"Diffusion-weighted imaging with acquisition of three b-values for response evaluation of neuroendocrine liver metastases undergoing selective internal radiotherapy.\" Eur Radiol 24(2): 267\u0026ndash;276.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLencioni, R. and J. M. Llovet (2010). \"Modified RECIST (mRECIST) assessment for hepatocellular carcinoma.\" Semin Liver Dis 30(1): 52\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMafeld, S., P. Littler, H. Hayhurst, D. Manas, R. Jackson, J. Moir and J. French (2020). \"Liver Resection After Selective Internal Radiation Therapy with Yttrium-90: Safety and Outcomes.\" J Gastrointest Cancer 51(1): 152\u0026ndash;158.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePardo, F., B. Sangro, R. C. Lee, D. Manas, R. Jeyarajah, V. Donckier, G. Maleux, A. D. Pinna, L. Bester, D. L. Morris, D. Iannitti, P. K. Chow, R. Stubbs, P. J. Gow, G. Masi, K. T. Fisher, W. Y. Lau, K. 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Padia (2021). \"Yttrium-90 Radioembolization for the Treatment of Solitary, Unresectable HCC: The LEGACY Study.\" Hepatology 74(5): 2342\u0026ndash;2352.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSangro, B., J. Argemi, M. Ronot, V. Paradis, T. Meyer, V. Mazzaferro, P. Jepsen, R. Golfieri, P. Galle, L. Dawson and M. Reig (2025). \"EASL Clinical Practice Guidelines on the management of hepatocellular carcinoma.\" Journal of Hepatology 82(2): 315\u0026ndash;374.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSobeh, T., Y. Inbar, S. Apter, S. Soffer, R. Anteby, M. Kraus, E. Konen and E. Klang (2023). \"Diffusion-weighted MRI for predicting and assessing treatment response of liver metastases from CRC - A systematic review and meta-analysis.\" Eur J Radiol 163: 110810.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTheilmann, R. J., R. Borders, T. P. Trouard, G. Xia, E. Outwater, J. Ranger-Moore, R. J. Gillies and A. Stopeck (2004). \"Changes in water mobility measured by diffusion MRI predict response of metastatic breast cancer to chemotherapy.\" Neoplasia 6(6): 831\u0026ndash;837.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eToskich, B., L. L. Vidal, M. T. Olson, J. T. Lewis, J. D. LeGout, D. M. Sella, S. A. Montazeri, Z. Devcic, A. R. Lewis, G. T. Frey, C. A. Ritchie, R. Paz-Fumagalli, K. P. Croome and T. C. Patel (2021). \"Pathologic Response of Hepatocellular Carcinoma Treated with Yttrium-90 Glass Microsphere Radiation Segmentectomy Prior to Liver Transplantation: A Validation Study.\" J Vasc Interv Radiol 32(4): 518\u0026ndash;526 e511.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVandecaveye, V., F. De Keyzer, C. Verslype, K. Op de Beeck, M. Komuta, B. Topal, I. Roebben, D. Bielen, T. Roskams, F. Nevens and S. Dymarkowski (2009). \"Diffusion-weighted MRI provides additional value to conventional dynamic contrast-enhanced MRI for detection of hepatocellular carcinoma.\" Eur Radiol 19(10): 2456\u0026ndash;2466.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVouche, M., A. Habib, T. J. Ward, E. Kim, L. Kulik, D. Ganger, M. Mulcahy, T. Baker, M. Abecassis, K. T. Sato, J. C. Caicedo, J. Fryer, R. Hickey, E. Hohlastos, R. J. Lewandowski and R. Salem (2014). \"Unresectable solitary hepatocellular carcinoma not amenable to radiofrequency ablation: multicenter radiology-pathology correlation and survival of radiation segmentectomy.\" Hepatology 60(1): 192\u0026ndash;201.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVouche, M., R. Salem, R. J. Lewandowski and F. H. Miller (2015). \"Can volumetric ADC measurement help predict response to Y90 radioembolization in HCC?\" Abdom Imaging 40(6): 1471\u0026ndash;1480.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWright, G. P., J. W. Marsh, M. K. Varma, M. G. Doherty, D. L. Bartlett and M. H. Chung (2017). \"Liver Resection After Selective Internal Radiation Therapy with Yttrium-90 is Safe and Feasible: A Bi-institutional Analysis.\" Ann Surg Oncol 24(4): 906\u0026ndash;913.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"abdominal-radiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"aima","sideBox":"Learn more about [Abdominal Radiology](http://link.springer.com/journal/261)","snPcode":"261","submissionUrl":"https://submission.springernature.com/new-submission/261/3","title":"Abdominal Radiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7040415/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7040415/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e\u003cp\u003eTo assess the association of diffusion restriction on diffusion weighted MRI (DWI) with complete pathological necrosis (CPN) in hepatocellular carcinoma (HCC) following selective internal radiation therapy with Yttrium-90 resin microspheres (SIRT).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA retrospective cohort study of patients undergoing resection or transplantation for HCC following SIRT was performed. Imaging pre- and post-SIRT was assessed for response to treatment via mRECIST and for the presence of diffusion restriction on DWI. Histological specimens were assessed for complete pathological necrosis (CPN).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eTwenty-nine tumours were included from 25 patients; mRECIST complete response (CR) demonstrated moderate reliability (sensitivity 0.92, specificity 0.68) for predicting CPN. Twelve tumours did not have diffusion restriction on pretreatment DWI; following their exclusion, loss of diffusion restriction demonstrated a weak agreement (sensitivity 0.85, specificity 0.80, Cohen\u0026rsquo;s Kappa 0.197) for prediction of CPN. The combination of mRECIST CR and loss of diffusion restriction was associated with CPN in 100% (6/6) cases; whilst persistent abnormal diffusion restriction despite mRECIST CR was associated with residual disease in 75% (3/4) cases.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThe combination of mRECIST CR when combined with loss of diffusion restriction appears to reliably predict CPN post SIRT; persistent diffusion restriction despite the findings of mRECIST CR appears to correlate with residual disease.\u003c/p\u003e","manuscriptTitle":"Loss of diffusion restriction and arterial enhancement accurately predicts complete pathological response following Y-90 Selective Internal Radiation Therapy for hepatocellular carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-14 05:31:36","doi":"10.21203/rs.3.rs-7040415/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-13T01:58:13+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-05T10:22:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"201225341008093377045483203881468165755","date":"2025-09-22T20:16:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-31T22:12:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"215992839086393507397066231357348299863","date":"2025-08-27T16:15:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"188000949893927707072894096484364703438","date":"2025-08-12T16:08:24+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-06T05:48:44+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-04T01:17:47+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-04T01:16:30+00:00","index":"","fulltext":""},{"type":"submitted","content":"Abdominal Radiology","date":"2025-07-03T17:05:40+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"abdominal-radiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"aima","sideBox":"Learn more about [Abdominal Radiology](http://link.springer.com/journal/261)","snPcode":"261","submissionUrl":"https://submission.springernature.com/new-submission/261/3","title":"Abdominal Radiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"f2247c90-c4fc-4532-99b1-4a1dacd90946","owner":[],"postedDate":"July 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-16T15:59:54+00:00","versionOfRecord":{"articleIdentity":"rs-7040415","link":"https://doi.org/10.1007/s00261-026-05377-5","journal":{"identity":"abdominal-radiology","isVorOnly":false,"title":"Abdominal Radiology"},"publishedOn":"2026-02-09 15:56:50","publishedOnDateReadable":"February 9th, 2026"},"versionCreatedAt":"2025-07-14 05:31:36","video":"","vorDoi":"10.1007/s00261-026-05377-5","vorDoiUrl":"https://doi.org/10.1007/s00261-026-05377-5","workflowStages":[]},"version":"v1","identity":"rs-7040415","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7040415","identity":"rs-7040415","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}