The Spatial Extracellular Proteomic Tumor Microenvironment Distinguishes Molecular Subtypes of Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) mortality rates continue to increase faster than those of other cancer types due to high heterogeneity, which limits diagnosis and treatment. Pathological and molecular subtyping have identified that HCC tumors with poor outcomes are characterized by intratumoral collagenous accumulation. However, the translational and post-translational regulation of tumor collagen, which is critical to the outcome, remains largely unknown. Here, we investigate the spatial extracellular proteome to understand the differences associated with HCC tumors defined by Hoshida transcriptomic subtypes of poor outcome (Subtype 1; S1; n = 12) and better outcome (Subtype 3; S3; n = 24) that show differential stroma-regulated pathways. Collagen-targeted mass spectrometry imaging (MSI) with the same-tissue reference libraries, built from untargeted and targeted LC-MS/MS was used to spatially define the extracellular microenvironment from clinically-characterized, formalin-fixed, paraffin-embedded tissue sections. Collagen α-1(I) chain domains for discoidin-domain receptor and integrin binding showed distinctive spatial distribution within the tumor microenvironment. Hydroxylated proline (HYP)-containing peptides from the triple helical regions of fibrillar collagens distinguished S1 from S3 tumors. Exploratory machine learning on multiple peptides extracted from the tumor regions could distinguish S1 and S3 tumors (with an area under the receiver operating curve of ≥0.98; 95% confidence intervals between 0.976 and 1.00; and accuracies above 94%). An overall finding was that the extracellular microenvironment has a high potential to predict clinically relevant outcomes in HCC.

Imaging cholangiocarcinoma: CT and MRI techniques.

Cross-sectional imaging plays a crucial role in the detection, diagnosis, staging, and resectability assessment of intra- and extrahepatic cholangiocarcinoma. Despite this vital function, there is a lack of standardized CT and MRI protocol recommendations for imaging cholangiocarcinoma, with substantial differences in image acquisition across institutions and vendor platforms. In this review, we present standardized strategies for the optimal imaging assessment of cholangiocarcinoma including contrast media considerations, patient preparation recommendations, optimal contrast timing, and representative CT and MRI protocols with individual sequence optimization recommendations. Our recommendations are supported by expert opinion from members of the Society of Abdominal Radiology's Disease-Focused Panel (DFP) on Cholangiocarcinoma, encompassing a broad array of institutions and practice patterns.

Artificial intelligence in liver cancer research: a scientometrics analysis of trends and topics.

Background and aims: With the rapid growth of artificial intelligence (AI) applications in various fields, understanding its impact on liver cancer research is paramount. This scientometrics project aims to investigate publication trends and topics in AI-related publications in liver cancer. Materials and Methods: We employed a search strategy to identify AI-related publications in liver cancer using Scopus database. We analyzed the number of publications, author affiliations, and journals that publish AI-related publications in liver cancer. Finally, the publications were grouped based on intended application. Results: We identified 3950 eligible publications (2695 articles, 366 reviews, and 889 other document types) from 1968 to August 3, 2023. There was a 12.7-fold increase in AI-related publications from 2013 to 2022. By comparison, the number of total publications on liver cancer increased by 1.7-fold. Our analysis revealed a significant shift in trends of AI-related publications on liver cancer in 2019. We also found a statistically significant consistent increase in numbers of AI-related publications over time (tau = 0.756, p < 0.0001). Eight (53%) of the top 15 journals with the most publications were radiology journals. The largest number of publications were from China (n=1156), the US (n=719), and Germany (n=236). The three most common publication categories were "medical image analysis for diagnosis" (37%), "diagnostic or prognostic biomarkers modeling & bioinformatics" (19%), and "genomic or molecular analysis" (18%). Conclusion: Our study reveals increasing interest in AI for liver cancer research, evidenced by a 12.7-fold growth in related publications over the past decade. A common application of AI is in medical imaging analysis for various purposes. China, the US, and Germany are leading contributors.

Characterization of renal masses with MRI-based radiomics: assessment of inter-package and inter-observer reproducibility in a prospective pilot study.

OBJECTIVES: To evaluate radiomics features' reproducibility using inter-package/inter-observer measurement analysis in renal masses (RMs) based on MRI and to employ machine learning (ML) models for RM characterization. METHODS: 32 Patients (23M/9F; age 61.8 ± 10.6 years) with RMs (25 renal cell carcinomas (RCC)/7 benign masses; mean size, 3.43 ± 1.73 cm) undergoing resection were prospectively recruited. All patients underwent 1.5 T MRI with T2-weighted (T2-WI), diffusion-weighted (DWI)/apparent diffusion coefficient (ADC), and pre-/post-contrast-enhanced T1-weighted imaging (T1-WI). RMs were manually segmented using volume of interest (VOI) on T2-WI, DWI/ADC, and T1-WI pre-/post-contrast imaging (1-min, 3-min post-injection) by two independent observers using two radiomics software packages for inter-package and inter-observer assessments of shape/histogram/texture features common to both packages (104 features; n = 26 patients). Intra-class correlation coefficients (ICCs) were calculated to assess inter-observer and inter-package reproducibility of radiomics measurements [good (ICC ≥ 0.8)/moderate (ICC = 0.5-0.8)/poor (ICC < 0.5)]. ML models were employed using reproducible features (between observers and packages, ICC > 0.8) to distinguish RCC from benign RM. RESULTS: Inter-package comparisons demonstrated that radiomics features from T1-WI-post-contrast had the highest proportion of good/moderate ICCs (54.8-58.6% for T1-WI-1 min), while most features extracted from T2-WI, T1-WI-pre-contrast, and ADC exhibited poor ICCs. Inter-observer comparisons found that radiomics measurements from T1-WI pre/post-contrast and T2-WI had the greatest proportion of features with good/moderate ICCs (95.3-99.1% T1-WI-post-contrast 1-min), while ADC measurements yielded mostly poor ICCs. ML models generated an AUC of 0.71 [95% confidence interval = 0.67-0.75] for diagnosis of RCC vs. benign RM. CONCLUSION: Radiomics features extracted from T1-WI-post-contrast demonstrated greater inter-package and inter-observer reproducibility compared to ADC, with fair accuracy for distinguishing RCC from benign RM. CLINICAL RELEVANCE: Knowledge of reproducibility of MRI radiomics features obtained on renal masses will aid in future study design and may enhance the diagnostic utility of radiomics models for renal mass characterization.

MRI Gadolinium-Based Contrast Media: Meeting Radiological, Clinical, and Environmental Needs.

Gadolinium-based contrast agents (GBCAs) are routinely used in magnetic resonance imaging (MRI). They are essential for choosing the most appropriate medical or surgical strategy for patients with serious pathologies, particularly in oncologic, inflammatory, and cardiovascular diseases. However, GBCAs have been associated with an increased risk of nephrogenic systemic fibrosis in patients with renal failure, as well as the possibility of deposition in the brain, bones, and other organs, even in patients with normal renal function. Research is underway to reduce the quantity of gadolinium injected, without compromising image quality and diagnosis. The next generation of GBCAs will enable a reduction in the gadolinium dose administered. Gadopiclenol is the first of this new generation of GBCAs, with high relaxivity, thus having the potential to reduce the gadolinium dose while maintaining good in vivo stability due to its macrocyclic structure. High-stability and high-relaxivity GBCAs will be one of the solutions for reducing the dose of gadolinium to be administered in clinical practice, while the development of new technologies, including optimization of MRI acquisitions, new contrast mechanisms, and artificial intelligence may help reduce the need for GBCAs. Future solutions may involve a combination of next-generation GBCAs and image-processing techniques to optimize diagnosis and treatment planning while minimizing exposure to gadolinium. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 3.

Early post-treatment MRI predicts long-term hepatocellular carcinoma response to radiation segmentectomy.

OBJECTIVES: Radiation segmentectomy using yttrium-90 plays an emerging role in the management of early-stage HCC. However, the value of early post-treatment MRI for response assessment is uncertain. We assessed the value of response criteria obtained early after radiation segmentectomy in predicting long-term response in patients with HCC. MATERIALS AND METHODS: Patients with HCC who underwent contrast-enhanced MRI before, early, and 12 months after radiation segmentectomy were included in this retrospective single-center study. Three independent radiologists reviewed images at baseline and 1st follow-up after radiation segmentectomy and assessed lesion-based response according to mRECIST, LI-RADS treatment response algorithm (TRA), and image subtraction. The endpoint was response at 12 months based on consensus readout of two separate radiologists. Diagnostic accuracy for predicting complete response (CR) at 12 months based on the 1st post-treatment MRI was calculated. RESULTS: Eighty patients (M/F 60/20, mean age 67.7 years) with 80 HCCs were assessed (median size baseline, 1.8 cm [IQR, 1.4-2.9 cm]). At 12 months, 74 patients were classified as CR (92.5%), 5 as partial response (6.3%), and 1 as progressive disease (1.2%). Diagnostic accuracy for predicting CR was fair to good for all readers with excellent positive predictive value (PPV): mRECIST (range between 3 readers, accuracy: 0.763-0.825, PPV: 0.966-1), LI-RADS TRA (accuracy: 0.700-0.825, PPV: 0.983-1), and subtraction (accuracy: 0.775-0.825, PPV: 0.967-1), with no difference in accuracy between criteria (p range 0.053 to > 0.9). CONCLUSION: mRECIST, LI-RADS TRA, and subtraction obtained on early post-treatment MRI show similar performance for predicting long-term response in patients with HCC treated with radiation segmentectomy. CLINICAL RELEVANCE STATEMENT: Response assessment extracted from early post-treatment MRI after radiation segmentectomy predicts complete response in patients with HCC with high PPV (≥ 0.96). KEY POINTS: • Early post-treatment response assessment on MRI predicts response in patients with HCC treated with radiation segmentectomy with fair to good accuracy and excellent positive predictive value. • There was no difference in diagnostic accuracy between mRECIST, LI-RADS, and subtraction for predicting HCC response to radiation segmentectomy.

Prediction of locally advanced rectal cancer response to neoadjuvant chemoradiation therapy using volumetric multiparametric MRI-based radiomics.

PURPOSE: To assess the role of pretreatment multiparametric (mp)MRI-based radiomic features in predicting pathologic complete response (pCR) of locally advanced rectal cancer (LARC) to neoadjuvant chemoradiation therapy (nCRT). METHODS: This was a retrospective dual-center study including 98 patients (M/F 77/21, mean age 60 years) with LARC who underwent pretreatment mpMRI followed by nCRT and total mesorectal excision or watch and wait. Fifty-eight patients from institution 1 constituted the training set and 40 from institution 2 the validation set. Manual segmentation using volumes of interest was performed on T1WI pre-/post-contrast, T2WI and diffusion-weighted imaging (DWI) sequences. Demographic information and serum carcinoembryonic antigen (CEA) levels were collected. Shape, 1st and 2nd order radiomic features were extracted and entered in models based on principal component analysis used to predict pCR. The best model was obtained using a k-fold cross-validation method on the training set, and AUC, sensitivity and specificity for prediction of pCR were calculated on the validation set. RESULTS: Stage distribution was T3 (n = 79) or T4 (n = 19). Overall, 16 (16.3%) patients achieved pCR. Demographics, MRI TNM stage, and CEA were not predictive of pCR (p range 0.59-0.96), while several radiomic models achieved high diagnostic performance for prediction of pCR (in the validation set), with AUCs ranging from 0.7 to 0.9, with the best model based on high b-value DWI demonstrating AUC of 0.9 [95% confidence intervals: 0.67, 1], sensitivity of 100% [100%, 100%], and specificity of 81% [66%, 96%]. CONCLUSION: Radiomic models obtained from pre-treatment MRI show good to excellent performance for the prediction of pCR in patients with LARC, superior to clinical parameters and CEA. A larger study is needed for confirmation of these results.

MR Enterography in Ulcerative Colitis: Beyond Endoscopy.

Ulcerative colitis (UC) is a chronic idiopathic inflammatory bowel disease (IBD) that progressively affects mucosa and submuccosa of the colon and rectum in a continual pattern. In comparison, Crohn disease (CD), the other type of IBD, is a chronic transmural inflammatory disorder that can involve any part of the gastrointestinal tract. MR enterography (MRE) has emerged as an important imaging modality for the diagnosis and detection of disease activity and complications in CD, with comparable results to those of endoscopy. But MRE has been underused for assessment of UC in recent years, and clinicians heavily rely on endoscopic findings for management of UC. Despite UC being considered an endoscopically assessable disease, MRE can provide useful information beyond that obtained with endoscopy about mural or extramural abnormalities, inaccessible parts of the colonic lumen, associated extraintestinal diseases, and superimposed pathologic conditions. Moreover, endoscopy might be contraindicated in some clinical settings due to the risk of colonic perforation. In addition to depicting the features of UC activity in different phases, MRE demonstrates findings of disease chronicity that cannot be achieved with endoscopy, particularly in a patient with colitis of unknown cause. The valuable diagnostic role of MRE to exclude undiagnosed CD in patients with UC who have refractory disease or those with postproctocolectomy complications is also emphasized. Radiologists can play a crucial role in the management of UC with MRE by addressing what is beyond endoscopy. ©RSNA, 2023 Test Your Knowledge questions are available in the supplemental material.

Abbreviated MRI Protocols in the Abdomen and Pelvis.

Abbreviated MRI (AMRI) protocols rely on the acquisition of a limited number of sequences tailored to a specific question. The main objective of AMRI protocols is to reduce exam duration and costs, while maintaining an acceptable diagnostic performance. AMRI is of increasing interest in the radiology community; however, challenges limiting clinical adoption remain. In this review, we will address main abdominal and pelvic applications of AMRI in the liver, pancreas, kidney, and prostate, including diagnostic performance, pitfalls, limitations, and cost effectiveness will also be discussed. Level of Evidence: 3 Technical Efficacy Stage: 3.

Benign and Malignant Ovarian Teratomas: Multimodality Imaging Findings With Histopathologic Correlation.

ABSTRACT: The purpose of this article is to provide a comprehensive review of the imaging findings along with histopathologic correlation of mature (benign) teratomas and malignant ovarian teratomas, which include both immature teratomas and malignant degeneration of mature teratomas. The radiologist's ability to provide an accurate diagnosis plays an essential role in guiding the interdisciplinary care of patients with malignant teratomas and improving their outcomes.

Longitudinal assessment of hepatocellular carcinoma response to stereotactic body radiation using gadoxetate-enhanced MRI: A case series.

PURPOSE: To describe the longitudinal response in patients with hepatocellular carcinoma (HCC) treated with stereotactic body radiation therapy (SBRT) and who underwent liver transplant (LT) using gadoxetate-enhanced MRI. METHODS: Five men (median age 61y, range 57-64y) with 6 HCCs treated with SBRT (median dose 50 Gy) who subsequently underwent LT were included in this retrospective study. Patients underwent gadoxetate-enhanced MRI before and after SBRT over a period of 3-18 months. Response was assessed using RECIST1.1, mRECIST, LI-RADS and image subtraction, by 2 observers in consensus. Percentage of pathologic tumor necrosis was evaluated. RESULTS: LT was performed 278 days (IQR, 148-418d) after completion of SBRT and 48d after the last MRI. Histopathology demonstrated tumor necrosis of 48 ± 42% (range, 10-100%). Mean tumor size at baseline and last post-treatment MRIs pre-LT were 2.6 ± 0.8 cm and 2.4 ± 0.9 cm. Enhancing tumor component size at baseline MRI and last post-treatment MRI pre-LT were 1.6 ± 0.8 cm and 0.9 ± 1.0 cm. Responses assessed at the last LRI pre-LT were: partial response (PR, n = 3), stable disease (SD, n = 3) using RECIST1.1; complete response (CR, n = 2), partial response (PR, n = 2), stable disease (SD, n = 2) using mRECIST; and LR-TR viable (n = 4), LR-TR non-viable (n = 2) using LI-RADS. At the last MRI pre-LT, per-lesion features of arterial phase hyperenhancement (APHE, 4/6), portal venous washout (3/6) and capsule (3/6) were observed. 5/6 lesions displayed a hypointense perilesional halo on hepatobiliary phase with a mean delay of 3.1 months post-SBRT. CONCLUSIONS: This case-series showed decreased size, persistent APHE, and incomplete pathologic necrosis in most HCCs treated with SBRT undergoing transplant.

Magnetic resonance elastography for noninvasive detection of liver fibrosis: is there an added value of 3D acquisition?

PURPOSE: (1) Assess the diagnostic performance of liver 3D magnetic resonance elastography (MRE) parameters (including stiffness, storage/loss modulus and damping ratio) compared to liver stiffness measured with 2D MRE for noninvasive detection of advanced liver fibrosis (F3-F4) and cirrhosis (F4) in patients with chronic liver disease. (2) Assess the value of serum markers (FIB-4) in detecting advanced liver fibrosis and cirrhosis in the same patients. METHODS: This was a single center, prospective IRB-approved cross-sectional study that included 49 patients (M/F: 23/26, mean age 50.8 y) with chronic liver disease and concomitant liver biopsy. MRE was acquired at 1.5T using a spin echo-EPI sequence. The following parameters were measured: liver stiffness using 2D MRE (LS-2D) and 3D MRE parameters (LS-3D, liver storage, loss modulus and damping ratio). The Mann-Whitney U test, ROC curve analysis, Spearman correlation and logistic regression were performed to evaluate diagnostic performance of MRE parameters and FIB-4. RESULTS: LS-2D and LS-3D had similar diagnostic performance for diagnosis of F3-F4, with AUCs of 0.87 and 0.88, sensitivity of 0.71 and 0.81, specificity of 0.89 for both. For diagnosis of F4, LS-2D and LS-3D had similar performance with AUCs of 0.81 for both, sensitivity of 0.75 and 0.83, and specificity of 0.84 and 0.73, respectively. Additional 3D parameters (storage modulus, loss modulus, damping ratio) had variable performance, with AUC range of 0.59-0.78 for F3-F4; and 0.52-0.70 for F4. FIB-4 had lower diagnostic performance, with AUCs of 0.66 for F3-F4, and 0.68 for F4. CONCLUSION: Our study shows no added value of 3D MRE compared to 2D MRE for detection of advanced fibrosis and cirrhosis, while FIB-4 had lower diagnostic performance.

The combination of non-contrast abbreviated MRI and alpha foetoprotein has high performance for hepatocellular carcinoma screening.

OBJECTIVES: This study aimed to compare two abbreviated MRI (AMRI) protocols to complete MRI for HCC detection: non-contrast (NC)-AMRI without/with alpha foetoprotein (AFP) and dynamic contrast-enhanced (Dyn)-AMRI. METHODS: This retrospective single-center study included 351 patients (M/F: 264/87, mean age: 57y) with chronic liver disease, who underwent MRI for HCC surveillance between 2014 and 2020. Two reconstructed AMRI sets were obtained based on complete MRI: NC-AMRI (T2-weighted imaging (WI) + diffusion-WI) and Dyn-AMRI (T2-WI + dynamic T1-WI) and were assessed by 2 radiologists who reported all suspicious lesions, using LI-RADS/adapted LI-RADS classification. The reference standard was based on all available patient data. Inter-reader agreement was assessed and MRI diagnostic performance was compared to the reference standard. RESULTS: The reference standard demonstrated 83/351 HCC-positive patients (prevalence: 23.6%, median size: 22 mm, and positive MRIs: 83/631). Inter-reader agreement was substantial for all sets. Sensitivities of Dyn-AMRI and complete MRI (both 92.8%) were similar, higher than NC-AMRI (72.3%, p < 0.001). Specificities were not different between sets. NC-AMRI + AFP (92.8%) had similar sensitivity to Dyn-AMRI and complete MRI. In patients with small size HCCs (≤ 2 cm), sensitivities of Dyn-AMRI (85.3%) and complete MRI (88.2%) remained similar (p = 0.564), also outperforming NC-AMRI (52.9%, p < 0.05). NC-AMRI + AFP had similar sensitivity (88.2%) to Dyn-AMRI and complete MRI (p = 0.706 and p = 1, respectively). CONCLUSIONS: Dyn-AMRI has similar diagnostic performance to complete MRI for HCC detection, while both outperform NC-AMRI, especially for small size HCCs. NC-AMRI + AFP demonstrates similar sensitivity to Dyn-AMRI and complete MRI. CLINICAL RELEVANCE STATEMENT: Due to the low sensitivity of ultrasound for hepatocellular screening, new screening methods are needed. Abbreviated MRI (AMRI) is a candidate, especially non-contrast AMRI with serum alpha foetoprotein as the acquisition time is low, without the need for contrast medium injection. KEY POINTS: • Dynamic contrast-enhanced abbreviated MRI using extracellular gadolinium-based contrast agent and complete MRI have similar diagnostic performance for hepatocellular carcinoma detection in an at-risk population. • Non-contrast abbreviated MRI with alpha foetoprotein has similar diagnostic performance to dynamic contrast-enhanced abbreviated MRI and complete MRI, including when considering small size hepatocellular carcinoma ≤ 2 cm. • Non-contrast abbreviated MRI and dynamic contrast-enhanced abbreviated MRI can be performed in 7 and 10 min, excluding patient setup time.

Consensus report from the 10th global forum for liver magnetic resonance imaging: multidisciplinary team discussion.

The 10th Global Forum for Liver Magnetic Resonance Imaging was held in October 2021. The themes of the presentations and discussions at this Forum are described in detail in the review by Taouli et al (2023). The focus of this second manuscript developed from the Forum is on multidisciplinary tumor board perspectives in hepatocellular carcinoma (HCC) management: how to approach early-, mid-, and late-stage management from the perspectives of a liver surgeon, an interventional radiologist, and an oncologist. The manuscript also includes a panel discussion by multidisciplinary experts on three selected cases that explore challenging aspects of HCC management. CLINICAL RELEVANCE STATEMENT: This review highlights the importance of a multidisciplinary team approach in liver cancer patients and includes the perspectives of a liver surgeon, an interventional radiologist, and an oncologist, including illustrative case studies. KEY POINTS: • A liver surgeon, interventional radiologist, and oncologist presented their perspectives on the treatment of early-, mid-, and late-stage HCC. • Different perspectives on HCC management between specialties emphasize the importance of multidisciplinary tumor boards. • A multidisciplinary faculty discussed challenging aspects of HCC management, as highlighted by three case studies.

Consensus report from the 10th Global Forum for Liver Magnetic Resonance Imaging: developments in HCC management.

The 10th Global Forum for Liver Magnetic Resonance Imaging (MRI) was held as a virtual 2-day meeting in October 2021, attended by delegates from North and South America, Asia, Australia, and Europe. Most delegates were radiologists with experience in liver MRI, with representation also from specialists in liver surgery, oncology, and hepatology. Presentations, discussions, and working groups at the Forum focused on the following themes: • Gadoxetic acid in clinical practice: Eastern and Western perspectives on current uses and challenges in hepatocellular carcinoma (HCC) screening/surveillance, diagnosis, and management • Economics and outcomes of HCC imaging • Radiomics, artificial intelligence (AI) and deep learning (DL) applications of MRI in HCC. These themes are the subject of the current manuscript. A second manuscript discusses multidisciplinary tumor board perspectives: how to approach early-, mid-, and late-stage HCC management from the perspectives of a liver surgeon, interventional radiologist, and oncologist (Taouli et al, 2023). Delegates voted on consensus statements that were developed by working groups on these meeting themes. A consensus was considered to be reached if at least 80% of the voting delegates agreed on the statements. CLINICAL RELEVANCE STATEMENT: This review highlights the clinical applications of gadoxetic acid-enhanced MRI for liver cancer screening and diagnosis, as well as its cost-effectiveness and the applications of radiomics and AI in patients with liver cancer. KEY POINTS: • Interpretation of gadoxetic acid-enhanced MRI differs slightly between Eastern and Western guidelines, reflecting different regional requirements for sensitivity vs specificity. • Emerging data are encouraging for the cost-effectiveness of gadoxetic acid-enhanced MRI in HCC screening and diagnosis, but more studies are required. • Radiomics and artificial intelligence are likely, in the future, to contribute to the detection, staging, assessment of treatment response and prediction of prognosis of HCC-reducing the burden on radiologists and other specialists and supporting timely and targeted treatment for patients.

Early Prediction of Response of Hepatocellular Carcinoma to Yttrium-90 Radiation Segmentectomy Using a Machine Learning MR Imaging Radiomic Approach.

PURPOSE: To assess the accuracy of a machine learning (ML) approach based on magnetic resonance (MR) imaging radiomic quantification obtained before treatment and early after treatment for prediction of early hepatocellular carcinoma (HCC) response to yttrium-90 transarterial radioembolization (TARE). MATERIALS AND METHODS: In this retrospective single-center study of 76 patients with HCC, baseline and early (1-2 months) post-TARE MR images were collected. Semiautomated tumor segmentation facilitated extraction of shape, first-order histogram, and custom signal intensity-based radiomic features, which were then trained (n = 46) using a ML XGBoost model and validated on a separate cohort (n = 30) not used in training to predict treatment response assessed at 4-6 months (based on modified Response and Evaluation Criteria in Solid Tumors criteria). Performance of this ML radiomic model was compared with those of models comprising clinical parameters and standard imaging characteristics using area under the receiver operating curve (AUROC) analysis for prediction of complete response (CR). RESULTS: Seventy-six tumors with a mean (±SD) diameter of 2.6 cm ± 1.6 were included. Sixty, 12, 1, and 3 patients were classified as having CR, partial response, stable disease, and progressive disease, respectively, at 4-6 months posttreatment on the basis of MR images. In the validation cohort, the radiomic model showed good performance (AUROC, 0.89) for prediction of CR, compared with models comprising clinical and standard imaging criteria (AUROC, 0.58 and 0.59, respectively). Baseline imaging features appeared to be more heavily weighted in the radiomic model. CONCLUSIONS: The use of ML modeling of radiomic data combining baseline and early follow-up MR imaging could predict HCC response to TARE. These models need to be investigated further in an independent cohort.

Liver Fibrosis, Fat, and Iron Evaluation with MRI and Fibrosis and Fat Evaluation with US: A Practical Guide for Radiologists.

Quantitative imaging biomarkers of liver disease measured by using MRI and US are emerging as important clinical tools in the management of patients with chronic liver disease (CLD). Because of their high accuracy and noninvasive nature, in many cases, these techniques have replaced liver biopsy for the diagnosis, quantitative staging, and treatment monitoring of patients with CLD. The most commonly evaluated imaging biomarkers are surrogates for liver fibrosis, fat, and iron. MR elastography is now routinely performed to evaluate for liver fibrosis and typically combined with MRI-based liver fat and iron quantification to exclude or grade hepatic steatosis and iron overload, respectively. US elastography is also widely performed to evaluate for liver fibrosis and has the advantage of lower equipment cost and greater availability compared with those of MRI. Emerging US fat quantification methods can be performed along with US elastography. The author group, consisting of members of the Society of Abdominal Radiology (SAR) Liver Fibrosis Disease-Focused Panel (DFP), the SAR Hepatic Iron Overload DFP, and the European Society of Radiology, review the basics of liver fibrosis, fat, and iron quantification with MRI and liver fibrosis and fat quantification with US. The authors cover technical requirements, typical case display, quality control and proper measurement technique and case interpretation guidelines, pitfalls, and confounding factors. The authors aim to provide a practical guide for radiologists interpreting these examinations. © RSNA, 2023 See the invited commentary by Ronot in this issue. Quiz questions for this article are available in the supplemental material.

Semiautomated segmentation of hepatocellular carcinoma tumors with MRI using convolutional neural networks.

OBJECTIVE: To assess the performance of convolutional neural networks (CNNs) for semiautomated segmentation of hepatocellular carcinoma (HCC) tumors on MRI. METHODS: This retrospective single-center study included 292 patients (237 M/55F, mean age 61 years) with pathologically confirmed HCC between 08/2015 and 06/2019 and who underwent MRI before surgery. The dataset was randomly divided into training (n = 195), validation (n = 66), and test sets (n = 31). Volumes of interest (VOIs) were manually placed on index lesions by 3 independent radiologists on different sequences (T2-weighted imaging [WI], T1WI pre-and post-contrast on arterial [AP], portal venous [PVP], delayed [DP, 3 min post-contrast] and hepatobiliary phases [HBP, when using gadoxetate], and diffusion-weighted imaging [DWI]). Manual segmentation was used as ground truth to train and validate a CNN-based pipeline. For semiautomated segmentation of tumors, we selected a random pixel inside the VOI, and the CNN provided two outputs: single slice and volumetric outputs. Segmentation performance and inter-observer agreement were analyzed using the 3D Dice similarity coefficient (DSC). RESULTS: A total of 261 HCCs were segmented on the training/validation sets, and 31 on the test set. The median lesion size was 3.0 cm (IQR 2.0-5.2 cm). Mean DSC (test set) varied depending on the MRI sequence with a range between 0.442 (ADC) and 0.778 (high b-value DWI) for single-slice segmentation; and between 0.305 (ADC) and 0.667 (T1WI pre) for volumetric-segmentation. Comparison between the two models showed better performance in single-slice segmentation, with statistical significance on T2WI, T1WI-PVP, DWI, and ADC. Inter-observer reproducibility of segmentation analysis showed a mean DSC of 0.71 in lesions between 1 and 2 cm, 0.85 in lesions between 2 and 5 cm, and 0.82 in lesions > 5 cm. CONCLUSION: CNN models have fair to good performance for semiautomated HCC segmentation, depending on the sequence and tumor size, with better performance for the single-slice approach. Refinement of volumetric approaches is needed in future studies. KEY POINTS: • Semiautomated single-slice and volumetric segmentation using convolutional neural networks (CNNs) models provided fair to good performance for hepatocellular carcinoma segmentation on MRI. • CNN models' performance for HCC segmentation accuracy depends on the MRI sequence and tumor size, with the best results on diffusion-weighted imaging and T1-weighted imaging pre-contrast, and for larger lesions.

Multiparametric MRI of Solid Renal Masses: Principles and Applications of Advanced Quantitative and Functional Methods for Tumor Diagnosis and Characterization.

Solid renal masses (SRMs) are increasingly detected and encompass both benign and malignant masses, with renal cell carcinoma (RCC) being the most common malignant SRM. Most patients with SRMs will undergo management without a priori pathologic confirmation. There is an unmet need to noninvasively diagnose and characterize RCCs, as significant variability in clinical behavior is observed and a wide range of differing management options exist. Cross-sectional imaging modalities, including magnetic resonance imaging (MRI), are increasingly used for SRM characterization. Multiparametric (mp) MRI techniques can provide insight into tumor biology by probing different physiologic/pathophysiologic processes noninvasively. These include sequences that probe tissue microstructure, including intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) and T1 relaxometry; oxygen metabolism (blood oxygen level dependent [BOLD-MRI]); as well as vascular flow and perfusion (dynamic contrast-enhanced MRI [DCE-MRI] and arterial spin labeling [ASL]). In this review, we will discuss each mpMRI method in terms of its principles, roles, and discuss the results of human studies for SRM assessment. Future validation of these methods may help to enable a personalized management approach for patients with SRM in the emerging era of precision medicine. EVIDENCE LEVEL: 5. TECHNICAL EFFICACY: 2.