Characterization Of Liver Lesions Research Articles

The added value of diffusion weighted MRI over standard dynamic liver MRI in characterization of focal liver lesions

The added value of diffusion weighted MRI over standard dynamic liver MRI in characterization of focal liver lesions

Fusion and CNN based classification of liver focal lesions using magnetic resonance imaging phases

The diagnosis and follow-up of focal liver lesions have an important place in radiology practice and in planning the treatment of patients. Lesions detected in the liver can be benign or malign. While benign lesions do not require any treatment, some treatments and surgical operations may be required for malign lesions. Magnetic resonance imaging provides some advantages over other imaging modalities in the detection and characterization of focal liver lesions with its superior soft tissue contrast. Additionally, different phases help make a clear diagnosis of different contrast agent retention properties in magnetic resonance imaging. This study aims to classify focal liver lesions based on convolutional neural networks by fusing magnetic resonance liver images obtained in pre-contrast, venous, arterial, and delayed phases. Magnetic resonance imaging data were obtained from Selcuk University, Faculty of Medicine, Department of Radiology in Turkey. The experiments were performed using 460 magnetic resonance images in four phases of 115 patients. Two experiments were conducted. Two-dimensional discrete wavelet transform was used to fuse the phases in both experiments. In the first experiment, the best model was determined using the original data, different number of convolution layers and different activation functions. In the second experiment, the best-found model was used. Additionally, the number of data was increased using data augmentation methods in this experiment. The results were compared with other state-of-the art methods and the superiority of the proposed method was proved. As a result of the classification, 96.66% accuracy, 86.67% sensitivity and 98.76% specificity rates were obtained. When the results are examined, CNN efficiency increases by fusing MR liver images taken in different phases.

Characterisation of focal liver lesions in multi-phase CT images using textural and pathological descriptors

ABSTRACT Characterisation of Focal Liver Lesions using imaging techniques is a low-risk approach to help physicians in diagnosis and treatment planning. State-of-the-art techniques chiefly rely on texture-based features of deep networks or conventional feature extractor techniques. However, these techniques do not sufficiently exploit the semantic attributes to improve the description of the tumour types. In this paper, we introduce pathologic descriptors to characterise the lesions in Computer Tomography images including, the quality of the tumour’s boundary, the tumour’s geometry, and the lesion’s enhancement. We propose a blurred edge model to characterise the quality of a tumour’s border. The irregularity or homogeneity of a tumour’s geometry is determined by the assessment of the central part and a rim around the lesion’s boundary. We compare the tumour’s appearance with the intensity of normal liver tissue and the blood pools to describe its enhancement behaviour. The proposed algorithm labelled 123 Computer Tomography data consisting of five lesion types and achieved an average accuracy of 92.7%. These results prove the superiority of pathology-based descriptors compared to textural features and their future potential for the discrimination of the lesions in the brain and lung. Moreover, the features from the ‘Non-Contrast’ phase do not improve classification results.

Shear-wave elastography combined with contrast-enhanced ultrasound algorithm for noninvasive characterization of focal liver lesions.

To establish shear-wave elastography (SWE) combined with contrast-enhanced ultrasound (CEUS) algorithm (SCCA) and improve the diagnostic performance in differentiating focal liver lesions (FLLs). We retrospectively selected patients with FLLs between January 2018 and December 2019 at the First Affiliated Hospital of Sun Yat-sen University. Histopathology was used as a standard criterion except for hemangiomas and focal nodular hyperplasia. CEUS with SonoVue (Bracco Imaging) and SCCA combining CEUS and maximum value of elastography with < 20kPa and > 90kPa thresholds were used for the diagnosis of FLLs. The diagnostic performance of CEUS and SCCA was calculated and compared. A total of 171 FLLs were included, with 124 malignant FLLs and 47 benign FLLs. The area under curve (AUC), sensitivity, and specificity in detecting malignant FLLs were 0.83, 91.94%, and 74.47% for CEUS, respectively, and 0.89, 91.94%, and 85.11% for SCCA, respectively. The AUC of SCCA was significantly higher than that of CEUS (P = 0.019). Decision curves indicated that SCCA provided greater clinical benefits. The SCCA provided significantly improved prediction of clinical outcomes, with a net reclassification improvement index of 10.64% (P = 0.018) and integrated discrimination improvement of 0.106 (P = 0.019). For subgroup analysis, we divided the FLLs into a chronic-liver-disease group (n = 88 FLLs) and a normal-liver group (n = 83 FLLs) according to the liver background. In the chronic-liver-disease group, there were no differences between the CEUS-based and SCCA diagnoses. In the normal-liver group, the AUC of SCCA and CEUS in the characterization of FLLs were 0.89 and 0.83, respectively (P = 0.018). SCCA is a feasible tool for differentiating FLLs in patients with normal liver backgrounds. Further investigations are necessary to validate the universality of this algorithm.

Can Shearwave Elastography as an Adjunct Non-Invasive Technique to Ultrasound Help in Better Characterization of Focal Liver Lesions?

Can Shearwave Elastography as an Adjunct Non-Invasive Technique to Ultrasound Help in Better Characterization of Focal Liver Lesions?

Diagnostic Accuracy of Triphasic CT in the Characterization of Incidental Focal Liver Lesions

Objectives: To determine the diagnostic accuracy of triphasic CT in the diagnosis of focal liver lesions as benign or malignant by using histopathology as the gold standard. Methods: This descriptive cross-sectional study was carried out at the diagnostic radiology department of the Pakistan Institute of Medical Sciences (P.I.M.S.) in Islamabad.110 individuals with localized liver lesions between the ages of 15 and 65 years and both genders were included. All of the study subjects were then undergoing triphasic CT scan of the liver. The CT findings were recorded as benign or malignant and correlated with histopathology findings. Data collection was done by using the self-structured proforma. Results: Patient’s mean age was 43.92 ± 12.87 years. Out of all, males were 68 (61.82%) and females were 42 (38.18%), with a female-to-male ratio of 1:1.6. Malignant lesions were 73 (66.36%) and 37 (33.54%) patients had benign lesions as per Triphasic CT diagnosis. Furthermore, in these 73 malignant cases, 67 cases were observed with malignant lesions and 6 cases had benign lesions on histopathology, while among the 37 CT cases of benign lesions, 8 cases had malignant lesions as per the histopathological diagnosis. Although triphasic CT showed 89.33% sensitivity and 82.86% specificity in the diagnosis of focal liver lesions by using histopathology as the gold standard. Conclusion: Triphasic CT was observed to be a non-invasive, accurate, and highly sensitive imaging technique in the diagnosis of focal liver lesions, whether (benign or malignant). Therefore, it can be used frequently to lower the number of pure diagnostic biopsies. Keywords: Malignant hepatic lesions, non-invasive, imaging modality, sensitivity

TRIPLE PHASE CT SCAN IMPROVES DETECTION AND CHARACTERISATION OF LIVER LESIONS.

Characterization of various focal liver lesions based on patterns of enhancement, morphology and size is critical for narrowing the diagnosis and determining the treatment options. The differential diagnosis (malignant and non malignant lesions) of focal liver lesions is broad and many benign focal liver lesions such as Cysts, abscesses, hemangiomas, and focal nodular hyperplasia etc. are commonly encountered. It is essential to detect and characterize these lesions for reduction in treatment cost and to avoid unnecessary interventions. In this study we assessed the various imaging features of focal liver lesions. Triple phase CT scan has revolutionised evaluation of liver lesions, minimising the need for biopsy and also surgery in benign lesions. Understanding the various patterns of enhancement of these lesions is crucial to management of these patients.

Clinical value of contrast-enhanced ultrasound in early diagnosis of small hepatocellular carcinoma (≤ 2 cm).

BACKGROUNDHepatocellular carcinoma (HCC) is the most common type of primary liver malignancy. Contrast-enhanced ultrasound (CEUS) uses contrast microbubbles during ultrasound, allowing the detection and characterization of malignant focal liver lesions with much higher diagnostic accuracy than conventional ultrasound; however, there are few reports focusing on the pattern of enhancement of CEUS for the diagnosis of HCC smaller than 2 cm.AIMTo investigate the clinical value of CEUS in the early detection of small HCC with high risk factors.METHODSA total of 395 patients with 632 nodules at high risk of HCC, who underwent regular follow-up at Xuhui Dahua Hospital from January 2007 to December 2021, were retrospectively examined. Conventional ultrasonography combined with CEUS was adopted to analyze the echo, size, location, and enhancement characteristics of benign and malignant nodules, as well as the enhancement methods for HCC with different diameters. RESULTSThe follow-up rate and duration were 92.15% (364/395) and 51.28 ± 45.09 mo, respectively. Conventional ultrasonography combined with CEUS revealed 65 (11.80%) nodules with a follow-up diagnosis of HCC, 19 (3.45%) dysplastic nodules, and 467 (84.75%) benign cirrhotic hyperplastic nodules. Among 65 cases of confirmed HCC, 40 (61.54%) were transformed from hypoechoic nodules, 9 (13.85%) from hyperechoic nodules, and the remaining 16 (24.62%) from isoechoic nodules. Significant differences in CEUS characteristics were found among cirrhotic nodules, dysplastic nodules, and HCC nodules at each phase. Significant differences in the enhancement mode were observed between nodules ≤ 1 cm and those 1–2 cm. The smaller the HCC nodule, the later the contrast agent began to flush and the longer the duration of contrast enhancement. CONCLUSIONConventional ultrasonography combined with CEUS could identify small HCC and help monitor patients with an early diagnosis of HCC. Significant differences in the enhancement mode are noted between nodules ≤ 1 cm and those 1–2 cm.

Hepatobiliary phases in magnetic resonance imaging using liver-specific contrast for focal lesions in clinical practice.

BACKGROUNDChallenging lesions, difficult to diagnose through non-invasive methods, constitute an important emotional burden for each patient regarding a still uncertain diagnosis (malignant x benign). In addition, from a therapeutic and prognostic point of view, delay in a definitive diagnosis can lead to worse outcomes. One of the main innovative trends currently is the use of molecular and functional methods to diagnosis. Numerous liver-specific contrast agents have been developed and studied in recent years to improve the performance of liver magnetic resonance imaging (MRI). More recently, one of the contrast agents introduced in clinical practice is gadoxetic acid (gadoxetate disodium). AIMTo demonstrate the value of the hepatobiliary phases using gadoxetic acid in MRI for the characterization of focal liver lesions (FLL) in clinical practice.METHODSOverall, 302 Lesions were studied in 136 patients who underwent MRI exams using gadoxetic acid for the assessment of FLL. Two radiologists independently reviewed the MRI exams using four stages, and categorized them on a 6-point scale, from 0 (lesion not detected) to 5 (definitely malignant). The stages were: stage 1- images without contrast, stage 2- addition of dynamic phases after contrast (analogous to usual extracellular contrasts), stage 3- addition of hepatobiliary phase after 10 min (HBP 10’), stage 4- hepatobiliary phase after 20 min (HBP 20’) in addition to stage 2. RESULTSThe interobserver agreement was high (weighted Kappa coefficient: 0.81- 1) at all stages in the characterization of benign and malignant FLL. The diagnostic weighted accuracy (Az) was 0.80 in stage 1 and was increased to 0.90 in stage 2. Addition of the hepatobiliary phase increased Az to 0.98 in stage 3, which was also 0.98 in stage 4. CONCLUSIONThe hepatobiliary sequences improve diagnostic accuracy. With growing potential in the era of precision medicine, the improvement and dissemination of the method among medical specialties can bring benefits in the management of patients with FLL that are difficult to diagnose.

Radiologic-pathologic correlation of lesions in resected liver specimens with an ex vivo MRI-compatible localization device.

Liver lesion characterization is limited by the lack of an established gold standard for precise correlation of radiologic characteristics with their histologic features. The objective of this study was to demonstrate the feasibility of using an ex vivo MRI-compatible sectioning device for radiologic-pathologic co-localization of lesions in resected liver specimens. In this prospective feasibility study, adults undergoing curative partial hepatectomy from February 2018 to January 2019 were enrolled. Gadoxetic acid was administered intraoperatively prior to hepatic vascular inflow ligation. Liver specimens were stabilized in an MRI-compatible acrylic lesion localization device (27 × 14 × 14 cm3) featuring slicing channels and a silicone gel 3D matrix. High-resolution 3D T1-weighted fast spoiled gradient echo and 3D T2-weighted fast-spin-echo images were acquired using a single channel quadrature head coil. Radiologic lesion coordinates guided pathologic sectioning. A final histopathologic diagnosis was prepared for all lesions. The proportion of successfully co-localized lesions was determined. A total of 57 lesions were identified radiologically and sectioned in liver specimens from 10 participants with liver metastases (n = 8), primary biliary mucinous cystic neoplasm (n = 1), and hepatic adenomatosis (n = 1). Of these, 38 lesions (67%) were < 1 cm. Overall, 52/57 (91%) of radiologically identified lesions were identified pathologically using the device. Of these, 5 lesions (10%) were not initially identified on gross examination but were confirmed histologically using MRI-guided localization. One lesion was identified grossly but not on MRI. We successfully demonstrated the feasibility of a clinical method for image-guided co-localization and histological characterization of liver lesions using an ex vivo MRI-compatible sectioning device. • The ex vivo MRI-compatible sectioning device provides a reliable method for radiologic-pathologic correlation of small (< 1 cm) liver lesions in human liver specimens. • The sectioning method can be feasibly implemented within a clinical practice setting and used in future efforts to study liver lesion characterization. • Intraoperative administration of gadoxetic acid results in enhancement in ex vivo MRI images of liver specimens hours later with excellent image quality.

Microvascular flow imaging to differentiate focal hepatic lesions: the spoke-wheel pattern as a specific sign of focal nodular hyperplasia.

Microvascular flow imaging (MVFI) is an advanced Doppler ultrasound technique designed to detect slow-velocity blood flow in small-caliber microvessels. This technique is capable of realtime, highly detailed visualization of tumor vessels without using a contrast agent. MVFI has been recently applied for the characterization of focal liver lesions and has revealed typical vascularity distributions in multiple types thereof. Focal nodular hyperplasia (FNH) constitutes an important differential diagnosis of malignant liver tumors. In this essay, we provide iconographic documentation of the MVFI appearance of FNH and other common solid liver lesions. Identifying the typical patterns of vascularity, including the spoke-wheel pattern with MVFI, can expedite the diagnosis, spare patients from unnecessary procedures, and save costs.

A Narrative Review on LI-RADS Algorithm in Liver Tumors: Prospects and Pitfalls.

Liver cancer is the sixth most detected tumor and the third leading cause of tumor death worldwide. Hepatocellular carcinoma (HCC) is the most common primary liver malignancy with specific risk factors and a targeted population. Imaging plays a major role in the management of HCC from screening to post-therapy follow-up. In order to optimize the diagnostic-therapeutic management and using a universal report, which allows more effective communication among the multidisciplinary team, several classification systems have been proposed over time, and LI-RADS is the most utilized. Currently, LI-RADS comprises four algorithms addressing screening and surveillance, diagnosis on computed tomography (CT)/magnetic resonance imaging (MRI), diagnosis on contrast-enhanced ultrasound (CEUS) and treatment response on CT/MRI. The algorithm allows guiding the radiologist through a stepwise process of assigning a category to a liver observation, recognizing both major and ancillary features. This process allows for characterizing liver lesions and assessing treatment. In this review, we highlighted both major and ancillary features that could define HCC. The distinctive dynamic vascular pattern of arterial hyperenhancement followed by washout in the portal-venous phase is the key hallmark of HCC, with a specificity value close to 100%. However, the sensitivity value of these combined criteria is inadequate. Recent evidence has proven that liver-specific contrast could be an important tool not only in increasing sensitivity but also in diagnosis as a major criterion. Although LI-RADS emerges as an essential instrument to support the management of liver tumors, still many improvements are needed to overcome the current limitations. In particular, features that may clearly distinguish HCC from cholangiocarcinoma (CCA) and combined HCC-CCA lesions and the assessment after locoregional radiation-based therapy are still fields of research.

Biliary Reflux Post-Hepaticojejunostomy (Sump Syndrome) Diagnosed using Microvascular Flow Imaging (MVI)—a Case Report

Microvascular flow imaging (MVI) is a novel technique which provides detailed visualisation of flow in the small vessels and structures and has a sensitivity comparable to contrast-enhanced ultrasound (CEUS). The application of MVI has developed in several different areas including improved characterisation of liver lesions, breast masses, thyroid nodules, and musculoskeletal disorders. MVI is based on detecting movement rather than vascularity. We report a case of biliary reflux following Roux-en-Y hepaticojejunostomy (Sump syndrome) where MVI provided a detailed assessment of the movement of fluid.

CT-Urography study protocol: Split-Bolus technique

CTU represents the natural technical and instrumental evolution of urography. The multidetector technology, with the possibility of retro-reconstruction of the images, has allowed the direct representation of the excretory tract with a significant reduction in acquisition times, decreasing motion artifacts and increasing the definition of the processed images. Split-Bolus CT dynamic study allows us to obtain, in a single image acquisition, both the nephrographic and the renal excretory phases; at the same time, we can obtain information of the parenchymal organs in the abdominal cavity as in the portal/nephrographic phase of a standard CT protocol. The main advantage of Split-Bolus CTU is undoubtedly the significant saving of the radiation dose administered to the patient, related to the reduction in the number of phases acquired, with a reported diagnostic efficacy comparable to traditional protocols in terms of imaging quality. The Split Bolus technique has been used in several clinical contexts, such as in the characterization of focal liver lesions, in acute pulmonary embolism and in polytrauma patients.

Interpretable Machine Learning for Characterization of Focal Liver Lesions by Contrast-Enhanced Ultrasound.

This work proposes an interpretable radiomics approach to differentiate between malignant and benign focal liver lesions (FLLs) on contrast-enhanced ultrasound (CEUS). Although CEUS has shown promise for differential FLLs diagnosis, current clinical assessment is performed only by qualitative analysis of the contrast enhancement patterns. Quantitative analysis is often hampered by the unavoidable presence of motion artifacts and by the complex, spatiotemporal nature of liver contrast enhancement, consisting of multiple, overlapping vascular phases. To fully exploit the wealth of information in CEUS, while coping with these challenges, here we propose combining features extracted by the temporal and spatiotemporal analysis in the arterial phase enhancement with spatial features extracted by texture analysis at different time points. Using the extracted features as input, several machine learning classifiers are optimized to achieve semiautomatic FLLs characterization, for which there is no need for motion compensation and the only manual input required is the location of a suspicious lesion. Clinical validation on 87 FLLs from 72 patients at risk for hepatocellular carcinoma (HCC) showed promising performance, achieving a balanced accuracy of 0.84 in the distinction between benign and malignant lesions. Analysis of feature relevance demonstrates that a combination of spatiotemporal and texture features is needed to achieve the best performance. Interpretation of the most relevant features suggests that aspects related to microvascular perfusion and the microvascular architecture, together with the spatial enhancement characteristics at wash-in and peak enhancement, are important to aid the accurate characterization of FLLs.

VueBox® for quantitative analysis of contrast-enhanced ultrasound in liver tumors1.

Dynamic contrast-enhanced ultrasound (DCE-US) enables quantification of tumor perfusion. VueBox is a platform independent external software using DICOM cine loops which objectively provides various DCE-US parameters of tumor vascularity. This review summaries its use for diagnosis and treatment monitoring of liver tumors. The existing literature provides evidence on the successful application of Vuebox based DCE-US for characterization and differential diagnosis of focal liver lesions, as well as on its use for monitoring of local ablative therapies and of modern systemic treatment in oncology.

Quantitative analysis of liver function: 3D variable-flip-angle versus Look-Locker T1 relaxometry in hepatocyte-specific contrast-enhanced liver MRI.

Gd-EOB-DTPA, a liver specific contrast agent with T1- shortening effects, is routinely used in clinical magnetic resonance imaging (MRI) for detection and characterization of focal liver lesions. Gd-EOB-DTPA-enhanced T1 relaxometry has recently received increasing attention as a tool for the quantitative analyses of liver function. However, this T1 relaxometry technique is limited due to various artifacts caused by B1 inhomogeneities and motion artifacts. This study aims to compare two different T1 relaxometry techniques for evaluating liver function as determined using a 13C-methacetin-based breath test (13C-MBT). Ninety-six patients underwent gadoxetic acid-enhanced MRI of the liver at 3T and a 13C-MBT. Two different prototype sequences for T1 relaxometry were used, a 3D VIBE sequence using Dixon water-fat separation and variable-flip-angles (VFA), as well as a 2D Look-Locker sequence (LL). Images were acquired before (T1 pre) and 20 minutes after (T1 post) administration of liver-specific contrast agent to evaluate the reduction rates of T1 relaxation time (rrT1) in accordance with the 13C-MBT outcome. To analyze both MR sequences' performance, the intraclass correlation coefficient (ICC) between four ROI measurements within the same liver and the coefficient of repeatability (CR) were calculated. T1 relaxometry measurements based on MR sequences, VFA, and LL show a constant change in line with impaired liver function progression. Simple regression models showed a log-linear correlation of 13C-MBT values with all evaluated T1 relaxometry measurements (VFA T1post, VFA rrT1, LL T1post, LL rrT1, P<0.001). Both ICC (VFA T1post, LL T1post; 0.75, 0.95) and CR (VFA T1 post, LL T1 post; 179, 101) showed a better agreement between ROI measurements using the LL sequence. Both T1 relaxometry sequences are suitable for the evaluation of liver function based on 13C-MBT. However, the Look-Locker sequence is less susceptible to artifacts and might be more advantageous, especially in patients with impaired liver function.

Deep Learning for the Detection, Localization, and Characterization of Focal Liver Lesions on Abdominal US Images.

To train and assess the performance of a deep learning-based network designed to detect, localize, and characterize focal liver lesions (FLLs) in the liver parenchyma on abdominal US images. In this retrospective, multicenter, institutional review board-approved study, two object detectors, Faster region-based convolutional neural network (Faster R-CNN) and Detection Transformer (DETR), were fine-tuned on a dataset of 1026 patients (n = 2551 B-mode abdominal US images obtained between 2014 and 2018). Performance of the networks was analyzed on a test set of 48 additional patients (n = 155 B-mode abdominal US images obtained in 2019) and compared with the performance of three caregivers (one nonexpert and two experts) blinded to the clinical history. The sign test was used to compare accuracy, specificity, sensitivity, and positive predictive value among all raters. DETR achieved a specificity of 90% (95% CI: 75, 100) and a sensitivity of 97% (95% CI: 97, 97) for the detection of FLLs. The performance of DETR met or exceeded that of the three caregivers for this task. DETR correctly localized 80% of the lesions, and it achieved a specificity of 81% (95% CI: 67, 91) and a sensitivity of 82% (95% CI: 62, 100) for FLL characterization (benign vs malignant) among lesions localized by all raters. The performance of DETR met or exceeded that of two experts and Faster R-CNN for these tasks. DETR demonstrated high specificity for detection, localization, and characterization of FLLs on abdominal US images. Supplemental material is available for this article. RSNA, 2022Keywords: Computer-aided Diagnosis (CAD), Ultrasound, Abdomen/GI, Liver, Tissue Characterization, Supervised Learning, Transfer Learning, Convolutional Neural Network (CNN).

Hepatic dual-contrast CT imaging: slow triple kVp switching CT with CNN-based sinogram completion and material decomposition.

Purpose: Dual-contrast protocols are a promising clinical multienergy computed tomography (CT) application for focal liver lesion detection and characterization. One avenue to enable multienergy CT is the introduction of photon-counting detectors (PCD). Although clinical translation is highly desired because of the diagnostic benefits of PCDs, it will still be a decade or more before they are broadly available. In our work, we investigated an alternative solution that can be implemented on widely used conventional CT systems (single source and integrating detector) to perform multimaterial spectral decomposition for dual-contrast imaging. Approach: We propose to slowly alternate the x-ray tube voltage between 3 kVp levels so each kVp level covers a few degrees of gantry rotation. This leads to the challenge of sparsely sampled projection data in each energy level. Performing the material decomposition (MD) in the sinogram domain is not directly possible as the projection images of the three energy levels are not angularly aligned. In order to overcome this challenge, we developed a convolutional neural network (CNN) framework for sparse sinogram completion (SC) and MD. To evaluate the feasibility of the slow kVp switching scheme, simulation studies of an abdominal phantom, which included liver lesions, were conducted. Results: The line-integral SC network yielded sinograms with a pixel-wise of the line-integrals compared to the ground truth. This provided acceptable image quality up to a switching angle of 9deg per kVp. The MD network we developed allowed us to differentiate iodine and gadolinium in the sinogram domain. The average relative quantification errors for iodine and gadolinium were below 10%. Conclusions: We developed a slow triple kVp switching data acquisition scheme and a CNN-based data processing pipeline. Results from a digital phantom validation illustrate the potential for future applications of dual-contrast agent protocols on practically available single-energy CT systems.

A Computed Tomography Nomogram for Assessing the Malignancy Risk of Focal Liver Lesions in Patients With Cirrhosis: A Preliminary Study.

PurposeThe detection and characterization of focal liver lesions (FLLs) in patients with cirrhosis is challenging. Accurate information about FLLs is key to their management, which can range from conservative methods to surgical excision. We sought to develop a nomogram that incorporates clinical risk factors, blood indicators, and enhanced computed tomography (CT) imaging findings to predict the nature of FLLs in cirrhotic livers.MethodA total of 348 surgically confirmed FLLs were included. CT findings and clinical data were assessed. All factors with P < 0.05 in univariate analysis were included in multivariate analysis. ROC analysis was performed, and a nomogram was constructed based on the multivariate logistic regression analysis results.ResultsThe FLLs were either benign (n = 79) or malignant (n = 269). Logistic regression evaluated independent factors that positively affected malignancy. AFP (OR = 10.547), arterial phase hyperenhancement (APHE) (OR = 740.876), washout (OR = 0.028), satellite lesions (OR = 15.164), ascites (OR = 156.241), and nodule-in-nodule architecture (OR =27.401) were independent predictors of malignancy. The combined predictors had excellent performance in differentiating benign and malignant lesions, with an AUC of 0.959, a sensitivity of 95.24%, and a specificity of 87.5% in the training cohort and AUC of 0.981, sensitivity of 94.74%, and specificity of 93.33% in the test cohort. The C-index was 96.80%, and calibration curves showed good agreement between the nomogram predictions and the actual data.ConclusionsThe nomogram showed excellent discrimination and calibration for malignancy risk prediction, and it may aid in making FLLs treatment decisions.