Early and accurate diagnosis of focal liver lesions is crucial for effective treatment and prognosis. We developed and validated a fully automated diagnostic system named Liver Artificial Intelligence Diagnosis System (LiAIDS) based on a diverse sample of 12,610 patients from 18 hospitals, both retrospectively and prospectively. In this study, LiAIDS achieved an F1-score of 0.940 for benign and 0.692 for malignant lesions, outperforming junior radiologists (benign: 0.830-0.890, malignant: 0.230-0.360) and being on par with senior radiologists (benign: 0.920-0.950, malignant: 0.550-0.650). Furthermore, with the assistance of LiAIDS, the diagnostic accuracy of all radiologists improved. For benign and malignant lesions, junior radiologists' F1-scores improved to 0.936-0.946 and 0.667-0.680 respectively, while seniors improved to 0.950-0.961 and 0.679-0.753. Additionally, in a triage study of 13,192 consecutive patients, LiAIDS automatically classified 76.46% of patients as low risk with a high NPV of 99.0%. The evidence suggests that LiAIDS can serve as a routine diagnostic tool and enhance the diagnostic capabilities of radiologists for liver lesions.© 2024. The Author(s).

The clinical utility of the DeepSeek-V3 (DSV3) model in enhancing the accuracy of Liver Imaging Reporting and Data System (LI-RADS, LR) classification remains underexplored. This study aimed to evaluate the diagnostic performance of DSV3 in LR classifications compared to radiologists with varying levels of experience and to assess its potential as a decision-support tool in clinical practice.

Tumour pathology contains rich information, including tissue structure and cell morphology, that reflects disease progression and patient survival. However, phenotypic information is subtle and complex, making the discovery of prognostic indicators from pathological images challenging.

Clinical benefits of atezolizumab plus bevacizumab (atezolizumab-bevacizumab) are observed only in a subset of patients with hepatocellular carcinoma and the development of biomarkers is needed to improve therapeutic strategies. The atezolizumab-bevacizumab response signature (ABRS), assessed by molecular biology profiling techniques, has been shown to be associated with progression-free survival after treatment initiation. The primary objective of our study was to develop an artificial intelligence (AI) model able to estimate ABRS expression directly from histological slides, and to evaluate if model predictions were associated with progression-free survival.In this multicentre retrospective study, we developed a model (ABRS-prediction; ABRS-P), which was derived from the previously published clustering-constrained attention multiple instance learning (or CLAM) pipeline. We trained the model fit for regression analysis using a multicentre dataset from The Cancer Genome Atlas (patients treated by surgical resection, n=336). The ABRS-P model was externally validated on two independent series of samples from patients with hepatocellular carcinoma (a surgical resection series, n=225; and a biopsy series, n=157). The predictive value of the model was further tested in a series of biopsy samples from a multicentre cohort of patients with hepatocellular carcinoma treated with atezolizumab-bevacizumab (n=122). All samples in the study were from adults (aged ≥18 years). The validation sets were sampled between Jan 1, 2008, to Jan 1, 2023. For the multicentre validation set, the primary objective was to assess the association of high versus low ABRS-P values, defined relative to cross-validation median split thresholds in the first biopsy series, with progression-free survival after treatment initiation. Finally, we performed spatial transcriptomics and matched prediction heatmaps with in situ expression profiles.Of the 840 patients sampled, 641 (76%) were male and 199 (24%) were female. Across the development and validation datasets, hepatocellular carcinoma risk factors included alcohol intake, hepatitis B and C virus infections, and non-alcoholic steatohepatitis. Using cross-validation in the development series, the mean Pearson's correlation between ABRS-P values and ABRS score (mean expression of ABRS genes) was r=0·62 (SD 0·09; mean p<0·0001, SD<0·0001). The ABRS-P generalised well on the external validation series (surgical resection series, r=0·60 [95% CI 0·51-0·68], p<0·0001; biopsy series, r=0·53 [0·40-0·63], p<0·0001). In the 122 patients treated with atezolizumab-bevacizumab, those with ABRS-P-high tumours (n=74) showed significantly longer median progression-free survival than those with ABRS-P-low tumours (n=48) after treatment initiation (12 months [95% CI 7-not reached] vs 7 months [4-9]; p=0·014). Spatial transcriptomics showed significantly higher ABRS score, along with upregulation of various other immune effectors, in tumour areas with high ABRS-P values versus areas with low ABRS-P values.Our study indicates that AI applied on hepatocellular carcinoma digital slides is able to serve as a biomarker for progression-free survival in patients treated with atezolizumab-bevacizumab. This approach could be used in the development of inexpensive and fast biomarkers for targeted therapies. The combination of AI heatmaps with spatial transcriptomics provides insight on the molecular features associated with predictions. This methodology could be applied to other cancers or diseases and improve understanding of the biological mechanisms that drive responses to treatments.Institut National du Cancer, Fondation ARC, China Scholarship Council, Ligue Contre le Cancer du Val de Marne, Fondation de l'Avenir, Ipsen, and Fondation Bristol Myers Squibb Pour la Recherche en Immuno-Oncologie.Copyright © 2023 Elsevier Ltd. All rights reserved.

Early diagnosis of hepatocellular carcinoma (HCC) lacks highly sensitive and specific protein biomarkers. Here, we describe a staged mass spectrometry (MS)-based discovery-verification-validation proteomics workflow to explore serum proteomic biomarkers for HCC early diagnosis in 1002 individuals. Machine learning model determined as P4 panel (HABP2, CD163, AFP and PIVKA-II) clearly distinguish HCC from liver cirrhosis (LC, AUC 0.979, sensitivity 0.925, specificity 0.915) and healthy individuals (HC, AUC 0.992, sensitivity 0.975, specificity 1.000) in an independent validation cohort, outperforming existing clinical prediction strategies. Furthermore, the P4 panel can accurately predict LC to HCC conversion (AUC 0.890, sensitivity 0.909, specificity 0.877) with predicting HCC at a median of 11.4 months prior to imaging in prospective external validation cohorts (No.: Keshen 2018_005_02 and NCT03588442). These results suggest that proteomics-driven serum biomarker discovery provides a valuable reference for the liquid biopsy, and has great potential to improve early diagnosis of HCC.© 2023. The Author(s).

The preoperative selection of patients with intermediate-stage hepatocellular carcinoma (HCC) who are likely to have an objective response to first transarterial chemoembolization (TACE) remains challenging.To develop and validate a clinical-radiomic model (CR model) for preoperatively predicting treatment response to first TACE in patients with intermediate-stage HCC.A total of 595 patients with intermediate-stage HCC were included in this retrospective study. A tumoral and peritumoral (10 mm) radiomic signature (TPR-signature) was constructed based on 3,404 radiomic features from 4 regions of interest. A predictive CR model based on TPR-signature and clinical factors was developed using multivariate logistic regression. Calibration curves and area under the receiver operating characteristic curves (AUCs) were used to evaluate the model's performance.The final CR model consisted of 5 independent predictors, including TPR-signature (< 0.001), AFP ( = 0.004), Barcelona Clinic Liver Cancer System Stage B (BCLC B) subclassification ( = 0.01), tumor location ( = 0.039), and arterial hyperenhancement ( = 0.050). The internal and external validation results demonstrated the high-performance level of this model, with internal and external AUCs of 0.94 and 0.90, respectively. In addition, the predicted objective response via the CR model was associated with improved survival in the external validation cohort (hazard ratio: 2.43; 95% confidence interval: 1.60-3.69; < 0.001). The predicted treatment response also allowed for significant discrimination between the Kaplan-Meier curves of each BCLC B subclassification.The CR model had an excellent performance in predicting the first TACE response in patients with intermediate-stage HCC and could provide a robust predictive tool to assist with the selection of patients for TACE.Copyright © 2021 by S. Karger AG, Basel.

Recently, PINPOINT, a novel laparoscopic fusion indocyanine green fluorescence imaging (IGFI) system has become available for laparoscopic liver resection. This study aims to characterize fluorescence patterns of intrahepatic cholangiocarcinoma (ICC) using the negative counterstaining method in laparoscopic anatomical hepatectomies of ICC.

Minimally invasive surgery (MIS) offers several advantages to patients including minimum blood loss and quick recovery time. However, lack of tactile or haptic feedback and poor visualization of the surgical site often result in some unintentional tissue damage. Visualization aspects further limits the collection of imaged frame contextual details, therefore the utility of computational methods such as tracking of tissue and tools, scene segmentation, and depth estimation are of paramount interest. Here, we discuss an online preprocessing framework that overcomes routinely encountered visualization challenges associated with the MIS. We resolve three pivotal surgical scene reconstruction tasks in a single step; namely, (i) denoise, (ii) deblur, and (iii) color correction. Our proposed method provides a latent clean and sharp image in the standard RGB color space from its noisy, blurred, and raw inputs in a single preprocessing step (end-to-end in one step). The proposed approach is compared against current state-of-the-art methods that perform each of the image restoration tasks separately. Results from knee arthroscopy show that our method outperforms existing solutions in tackling high-level vision tasks at a significantly reduced computation time.© 2023. The Author(s).

In robot-assisted minimally invasive surgery (RMIS), smoke produced by laser ablation and cauterization causes degradation in the visual quality of the operating field, increasing the difficulty and risk of surgery. Therefore, it is important and meaningful to remove fog or smoke from the endoscopic video to maintain a clear visual field.In this paper, we propose a novel method for surgical smoke removal based on the Swin transformer. Our method firstly uses convolutional neural network to extract shallow features, then uses the Swin transformer block to further extract deep features and finally generates smoke-free images.We conduct quantitative and qualitative experiments on the proposed method, and we also validate the desmoking results in the surgical instrument segmentation task. Extensive experiments on synthetic and real dataset show that the proposed approach has good performance and outperforms the state-of-the-art surgical smoke removal methods.Our method effectively removes surgical smoke, improves image quality and reduces the risk of RMIS. It provides a clearer visual field for the surgeon, as well as for subsequent visual tasks, such as instrument segmentation, 3D scene reconstruction and surgery automation.© 2023. CARS.

Automatically segmenting and classifying surgical activities is an important prerequisite to providing automated, targeted assessment and feedback during surgical training. Prior work has focused almost exclusively on recognizing gestures, or short, atomic units of activity such as pushing needle through tissue, whereas we also focus on recognizing higher-level maneuvers, such as suture throw. Maneuvers exhibit more complexity and variability than the gestures from which they are composed, however working at this granularity has the benefit of being consistent with existing training curricula.Prior work has focused on hidden Markov model and conditional-random-field-based methods, which typically leverage unary terms that are local in time and linear in model parameters. Because maneuvers are governed by long-term, nonlinear dynamics, we argue that the more expressive unary terms offered by recurrent neural networks (RNNs) are better suited for this task. Four RNN architectures are compared for recognizing activities from kinematics: simple RNNs, long short-term memory, gated recurrent units, and mixed history RNNs. We report performance in terms of error rate and edit distance, and we use a functional analysis-of-variance framework to assess hyperparameter sensitivity for each architecture.We obtain state-of-the-art performance for both maneuver recognition from kinematics (4 maneuvers; error rate of [Formula: see text]; normalized edit distance of [Formula: see text]) and gesture recognition from kinematics (10 gestures; error rate of [Formula: see text]; normalized edit distance of [Formula: see text]).Automated maneuver recognition is feasible with RNNs, an exciting result which offers the opportunity to provide targeted assessment and feedback at a higher level of granularity. In addition, we show that multiple hyperparameters are important for achieving good performance, and our hyperparameter analysis serves to aid future work in RNN-based activity recognition.

Succinate dehydrogenase enzyme (SDH) is frequently diminished in samples from patients with hepatocellular carcinoma (HCC), and SDH reduction is associated with elevated succinate level and poor prognosis in patients with HCC. However, the underlying mechanisms of how impaired SDH activity promotes HCC remain unclear.

Hepatocellular carcinoma (HCC) treatment remains challenging, particularly for immune checkpoint inhibitors (ICIs) non-response patients. Spatial transcriptome (ST) data and machine learning algorithms offer new insights into understanding HCC heterogeneity and ICIs resistance mechanisms.Utilizing ST data from HCC patients on ICIs treatment, we analyzed pathway activity and immune infiltration. We combined 167 machine learning models to develop a G2M-checkpoint related signature (G2MRS) based on differential gene expression. The four cohorts and in-house cohort was used to validate G2MRS, and KPNA2's role was further examined through in vitro experiments in two different liver cancer cell lines.Our analysis revealed a distinct suppressive immune barrier structure (SIBS) in ICIs non-response patients, associated with upregulated G2M-checkpoint levels. G2MRS, consisting of KPNA2, CENPA, and UCK2, accurately predicted HCC prognosis and ICIs response. Further in vitro experiments demonstrated KPNA2's role in regulating migration, proliferation, and apoptosis in liver cancer.This study highlights the importance of spatial heterogeneity and machine learning in refining HCC prognosis and ICIs response prediction. G2MRS and KPNA2 emerge as promising biomarkers for personalized HCC management.© 2024. The Author(s).

HCC recurrence frequently occurs after curative surgery. Histological microvascular invasion (MVI) predicts recurrence but cannot provide preoperative prognostication, whereas clinical prediction scores have variable performances.