In MTM related cohort, 74.2% (118/159) of patients were diagnosed pathologically with the MTM subtype. The baseline characteristics stratified by MTM status are shown in Table 1. Among all variables, age < 65 years and Edmondson-Steiner grade III–IV were found to be more prevalent in the MTM group than in the non-MTM group (p = 0.032, < 0.001). Other variables showed a similar distribution between the two groups. In HAIC related cohorts, a total of 752 treatment-naïve patients with HCC (80 females and 672 males; mean age, 54.2 ± 11.8 years) met the inclusion criteria. The clinicopathologic characteristics of the HCC patients who underwent HAIC in the three cohorts are outlined in Table 2. At the final follow-up, the mortality rates were 61.1% (299/489) in the TC, 71.3% (87/122) in the VC, and 35.5% (50/141) in the EVC. The baseline characteristics of the abovementioned two cohorts are shown in sTable 2.

Based on the segmented liver images, a total of 5610 pre-defined radiomic features and 4132 DL features were extracted from each phase of CECT. After feature selection, 10 in AP and 12 in PP were selected as significant pre-defined radiomic features. Among all ML classifiers, XGBoost outperformed other 3 classifiers and was selected to build radiomics scores. Most of the selected pre-defined radiomic features were GLCM features, which might be related to the heterogeneity of HCC. Besides, all DL features in AP and PP were chosen to build DL scores for further analysis. Prognostic performance comparison between various of models and staging system was shown in Table 3.

The baseline characteristics of patients with MTM subtype were listed in sTable 3. In the TC, the deep learning radiomics (DLR) risk score was lower in the MTM group than in the non-MTM group (mean, 0.834 ± 0.097 vs. 0.177 ± 0.089; p < 0.001). Multivariable analysis showed that an AFP level > 400 ng/ml and the DLR risk score were independent indicators for the MTM subtype. The comparison of predictive performance among four different models (clinical, radiomics, DLR, and DLR-Cli) in three cohorts and the AUC, SENS, SPEC, PPV, and NPV data of each model are shown in sTable 4. Among all models, the DLR-Cli model showed optimal discrimination, achieving AUCs of 0.967 in the TC, 0.912 in the IVC and 0.773 in the EVC, respectively. The results of the DeLong test indicated a significant difference in performance between the clinical model and the DLR-Cli model (p < 0.001 in TC, p < 0.001 in IVC and p < 0.001 in EVC).

Multivariate analysis showed that preoperative parameters, including PVTT (HR, 1.42) and DLR risk score (HR, 0.11), and postoperative parameters, including OR, HAIC sessions and MTM score, were independent risk factors for poor OS (sTable 5). The detailed performance of MDL for OS listed in sTable 6. These independently associated risk factors were used to develop the MDLRN (Fig. 2A, B), described by the formula: HR = 1.38 × PVTT + 0.54 × OR + 0.74 × HAIC sessions + 2.44 × MTM + 0.10 × DLR score. For each tumour grade, a higher total point value indicated a worse OS. The bootstrapped calibration curves plotted with 1-, 3- and 5-year OS were well matched with the idealized 45° line for the MDLRN in the three cohorts (Fig. 2C–H). To add clinical convenience, a user-friendly online application (https://prehaicnomogramforhcc.shinyapps.io/DynNomapp/) was developed.

Development of prognostic nomogram for OS. A The pre-nomogram was established using diagnostic factors for patients who had not received HAIC treatment and had preoperative HAIC data. B The post-nomogram was established using multiple factors for patients who had undergone HAIC treatment and had both pre- and post -HAIC data. C–E calibration curves plotted with 1-, 3- and 5-year overall survival (OS) were well matched with the idealized 45° line for the pre-nomogram in training cohort, internal testing cohort and external testing cohort. F–H calibration curves plotted with 1-, 3- and 5-year OS were well matched with the idealized 45°line for the post-nomogram in training cohort, internal testing cohort and external testing cohort

The AUCs of the preoperative MDLRN for predicting the OS of HCC patients who underwent HAIC in the TC, IVC and EVC were 0.80, 0.71 and 0.74, respectively. In addition, the AUCs of the postoperative MDLRN for predicting OS in the TC, IVC and EVC were 0.84, 0.78 and 0.79, respectively. In this study, we found that the MDLRN improved the prognostic prediction of HCC patients who underwent HAIC compared with rival models and staging systems (AJCC [American Joint Committee on Cancer], BCLC [Barcelona Clinic Liver Cancer] stage, CLIP [Cancer of the Liver Italian Program] classification, HKLC [Hong Kong Liver Cancer] stage) in the three cohorts (Fig. 3).

Discriminatory performance of all models and systems in thee cohorts. Graphs show time-dependent areas under the receiver operating characteristic (ROC) curve at various time points (top) for established models and staging systems. AJCC = American Joint Committee on Cancer, BCLC = Barcelona Clinic Liver Cancer, CLIP = Cancer of the Liver Italian Program, HKLC = Hong Kong Liver Cancer

The learned feature maps of MobileNetV1 are shown in Fig. 4 and detailed patient information in sTable 7. To better explore the hidden patterns the network learned, heatmaps were divided into prediction groups for 1/2/3/ > 3-year death/survival. According to their imaging features, the examples were divided into MTM and non-MTM subtypes. Overall, the whole intensity of the feature map in the predicted non-MTM group was lower than that in the predicted MTM group, which seems to indicate the natural pathological characteristics of HCC. Moreover, heatmaps showed that the better survival group had a high intensity, which indicated that the MTM subtype was an important factor for prognostic analysis.

The learned feature maps of MobileNetV1 for 1-, 2-, 3-, > 3 years OS. The 1-year OS non-MTM image come from a patient with 56 years old, Portal Vein Tumor Thrombus (PVTT), Stable Disease (SD) and 6 HAIC session. The 1-year OS MTM image come from a patient with 40 years old, Portal Vein Tumor Thrombus (PVTT), Stable Disease (SD) and 2 HAIC session. The 2-years OS non-MTM image come from a patient with 60 years old, Portal Vein Tumor Thrombus (PVTT), Stable Disease (SD) and 5 HAIC session. The 2-years OS MTM image come from a patient with 63 years old, No Portal Vein Tumor Thrombus (PVTT), Stable Disease (SD) and 6 HAIC session. The 3-years OS non-MTM image come from a patient with 47 years old, Portal Vein Tumor Thrombus (PVTT), Partial Response (PR) and 3 HAIC session. The 3-years OS MTM image come from a patient with 58 years old, No Portal Vein Tumor Thrombus (PVTT), Progressive Disease (PD) and 2 HAIC session. The > 3-years OS non-MTM image come from a patient with 26 years old, Portal Vein Tumor Thrombus (PVTT), Partial Response (PR) and 6 HAIC session. The > 3-years OS MTM image come from a patient with 58 years old, No Portal Vein Tumor Thrombus (PVTT), Partial Response (PR) and 8 HAIC session

To facilitate the clinical application of the MDLRN, we divided the HCC patients who underwent HAIC into two risk groups, including a high-risk group and a low-risk group, according to MDLRN risk scores. We identified the HR cut-off values for the pre- and post-MDLRN (−1.40 and −0.26) in the TC and verified them in the ITC and ETC, respectively. This pragmatic visualization of the risk level could help decide the HAIC strategy for HCC patients. According to the cut-off risk scores for the pre-MDLRN, in the TC, the 1-, 3- and 5-year OS were 89.0%, 52.9% and 34.3% in the low-risk group, respectively, which were better than the corresponding rates in the high-risk groups (37.2%, 5.5% and 5.5%) (p < 0.001) (Fig. 5A). Similarly, the cumulative 1-, 3-, and 5-year OS rates among the high-risk and low-risk groups were also significantly different in the other two test cohorts (both, p < 0.001) (Fig. 5B, C). According to the cut-off risk scores for the post-MDLRN, in the TC, the 1-, 3- and 5-year OS were 91.2%, 55.4% and 25.1% in the low-risk group, respectively, which were better than the rates in the high-risk group (35.7%, 3.8% and 3.8%, respectively) (p < 0.001) (Fig. 5D). Similarly, the cumulative 1-, 3-, and 5-year OS rates among the high-risk and low-risk groups were also significantly different in the other two test cohorts (both, p < 0.001) (Fig. 5E, F). In brief, more deaths were more commonly found during the follow-up period in high-risk patients than in low-risk patients; a higher proportion of low-risk patients received potentially curative therapy (liver transplant, repeat liver resection, or ablation) than high-risk patients.

Comparing the survival among different risk level groups based on the two prognostic models. According to the risk scores from the pre-nomogram, the HCC patients were divided into high-, and low-risk groups A–C. Kaplan–Meier (KM) curves for the overall survival (OS) of HCC patients in these two risk level groups in A the training cohort, B the internal test cohort, C the first external test cohort. KM analysis of the risk scores for OS among the high-,and low-risk groups based on the post-nomogram (post-HAICN) in D the training cohort, E the internal test cohort, F the external test cohort