In the study, we analyzed various radiomics models for intratumoral, peritumoral, and intratumoral combined peritumoral derived from preoperative CE-MR images for response estimation after TACE. Our study confirms that the radiomics models combining intratumoral and peritumoral are better than the intratumoral radiomics model in predicting TACE response of HCC patients. Furthermore, we developed and validated a combined nomogram that integrated the clinical-radiological risk factors and the optimal T-PTR (3mm) rad-score for predicting HCC response to TACE, demonstrating satisfactory performance, calibration, and clinical utility. The proposed radiomics approach successfully predicted therapeutic efficacy of TACE and may facilitate individualized treatment decision-making for patients with HCC.
Patients with HCC receiving TACE have various treatment efficacy and clinical outcomes[7, 8]. Treatment response after first TACE course has been proved to be an important and robust prognostic factor for clinical outcomes, which also impacts the individual therapeutic strategy in HCC patients[32]. In recent years, several studies have explored CT or MRI-based radiomics for the prediction of tumor response of HCC after TACE[33–37]. Chen et al.[33] developed a CT-based radiomics model on tumoral region for predicting TACE response, and their model merely showed moderate predictive performance with the AUCs of 0.760 and 0.720 in the training and testing cohorts, respectively. MRI-based radiomics might be more promising for response estimation due to better soft-tissue contrast and resolution in MR images. According to the related studies of Sun[34], Kuang[35], Kong[36], and Liu[37], they constructed radiomics models based on preoperative single MRI sequence or multiparametric MRI (MP-MRI) to predict tumor response of HCC patients receiving TACE, and the AUCs ranged from 0.692 to 0.866 in the validation cohort; however, their studies only focused on the intratumoral radiomics features of the HCC. Pathologically, peritumoral parenchyma is representative of cancerous heterogeneity, and the crucial information can be indicated by changes in the area surrounding tumors, such as biological aggressiveness, microinvasion, and micrometastasis[20, 23, 38]; thus, accurate evaluation of the neighboring tissue around tumors may also be useful in predicting treatment response and prognosis of TACE in patients with HCC.
Previous studies have reported that the regions on intratumoral and 3 mm peritumoral, intratumoral and 5 mm peritumoral, and intratumoral and 10 mm peritumoral can provide valuable information for prognosis prediction in HCC[23, 24, 39]. In addition, according to the practice guidelines for the pathological diagnosis of primary liver cancer (2015 update)[40], the liver tissues within 10 mm surrounding the tumor are defined as the adjacent areas around the cancer, where the probability of MVI is high. Therefore, our study focused on predicting treatment response, and we selected the most stable and predictive radiomics features from intratumoral, 3 mm, 5 mm and 10 mm peritumoral regions based on preoperative CE-MR images for radiomics models construction, which can quantitatively assess the heterogeneity and invasiveness of intratumoral and peritumoral tissues in a non-invasive way. In the present study, the PTR (3 mm) and PTR (5 mm) radiomics models showed comparable performance compared with the TR model, which indicated that peritumoral tissues were possess of a clinical value in assessing treatment response. Hu et al.[41] reached a similar result that the intratumoral radiomics model based on enhanced CT images obtained the equivalent efficacy compared with peritumoral radiomics model in predicting the response to neoadjuvant radiotherapy and chemotherapy in esophageal cancer with the AUCs of 0.730 and 0.734, respectively. The radiomics features contributed to the peritumoral models construction in the study were most derived from the AP images. This finding was in agreement with previous studies, in which the presence of peritumoral enhancement in AP images indicated more aggressive biological behavior[23, 42]. Additionally, the PTR (10 mm) radiomics model showed lower predictive performance than the PTR (3 mm) and PTR (5 mm) models. This may be due to the decrease in the number of microsatellites in the farther peritumoral area and inclusion of the peritumoral normal hepatic parenchyma[39]. We further combined intratumoral and peritumoral rad-scores to establish T-PTR (3 mm), T-PTR (5 mm), and T-PTR (10 mm) radiomics models for predicting TACE response, and the T-PTR (3 mm) radiomics model achieved the best-performing performance among the seven radiomics models mentioned above. A similar study reported by Kim et al.[39] found that the radiomics model with intratumoral and 3 mm border extension showed improved performance than the radiomics model with intratumoral and 5 mm border extension for predicting ER of HCC after curative resection. The T-PTR (3 mm) radiomics model demonstrated better predictive performance compared with the TR radiomics model, which indicated that peritumoral radiomics might potentially enhance the ability of intratumoral radiomics for TACE response prediction. This might be interpreted that arterial peritumoral enhancement and irregular margin presented in the peritumoral area are independent predictors of prognosis in HCC patients[23, 39]. Only one of the published studies, conducted MRI-based radiomics on intratumoral and peritumoral regions for the prediction of TACE prognosis[23]. In their study, the radiomics models based on the entire tumor volumetric of AP (APETV), PVPETV, and the border extensions of 1 mm, 3 mm, and 5 mm on the PVP (PVPB1, PVPB3, and PVPB5) were constructed to predict RFS of HCC patients after TACE. The best C-index results for PVPETV and PVPB3 radiomics models were 0.727 and 0.714 in the validation dataset, respectively. However, the above research analyzed the image information of whole areas including intratumoral and peritumoral regions, and did not explore the individual contribution of the area around the tumor to the prediction model; thus, it was unable to determine the significance of the separate peritumoral region in predicting recurrence or prognosis. Compared with the previous study[23], our study may have the following advantages: first, radiomics features derived from three-phase enhanced MR images might more fully reflect tumor heterogeneity and vascularization patterns, which is helpful for efficacy estimation; second, the individual peritumoral (3 mm, 5 mm, and 10 mm) radiomics models were constructed, and the proper and valuable peritumoral distance was determined; third, intratumoral combined peritumoral radiomics analysis may contain more prognostic information, and potentially provide a more accurate and effective approach of individualized efficacy prediction for HCC patients.
In this study, during the construction of the clinical-radiological model, ALP value, tumor size, and satellite nodule were independent predictors associated with treatment response of HCC after TACE. Previous researches on TACE clarified that a higher ALP value was an independent risk factor for unfavourable OS[43, 44]. Our study showed that abnormal ALP value was a significant predictor for poor response of HCC. Serum ALP levels are usually elevated in patients with liver diseases and thus may reflect the status of liver injury. In addition, ALP has already been included in the Chinese University Prognostic Index, a HCC staging system that assigns a score of 3 when ALP is > 200 IU/L, indicating the potential role of ALP in predicting the prognosis of HCC patients[45]. Tumor size has been broadly recognized as a major predictive factor of treatment response for TACE[10, 32]. Larger tumors usually have more satellite lesions or daughter nodules making it difficult for TACE to achieve CR[46]. In our study, maximal tumor size > 5 cm was a significant predictive factor for NR, a result similar to the study by Jeong et al.[47], who reported that tumors > 5 cm were independently associated with failure to achieve CR after TACE. Several studies reported that satellite nodule surrounding the main tumor was closely related to tumor grade, MVI, and ER after curative resection and OS of TACE therapy[22, 37, 48]. Our study demonstrated that the presence of satellite nodule was inclined to show NR to TACE, which was consistent with a recent study reported by Li et al.[11], their study showed that satellite nodule was statistically significant between the OR and NR groups. This may be interpreted that the development of satellite nodule favors vascular invasion and also tumor recurrence[48].
We ultimately developed a combined nomogram by integrating the T-PTR (3 mm) rad-score with clinical-radiological risk indicators (ALP value, tumor size, and satellite nodule) for treatment response prediction. The combined nomogram achieved good calibration and the strongest predictive performance based on AUCs in the training (nomogram vs. radiomics model vs. clinical-radiological model, 0.910 vs. 0.884 vs. 0.789) and validation (nomogram vs. radiomics model vs. clinical-radiological model, 0.918 vs. 0.911 vs. 0.782) cohorts. The novel combined prediction nomogram was evaluated by a decision curve to clarify the clinical usefulness, which could offer insight into clinical outcomes on the basis of threshold probability, from which the net benefit could be derived[36]. Our results clearly demonstrated that the combined nomogram could obtain more net benefit than either the treat-all-patients or the treat-none-patients strategies across a wide range of threshold probabilities. Therefore, our novel nomogram may provide a reliable and efficient tool that enables visualized and personalized decision-making for the treatment management of patients with HCC.
This study has several limitations. First, this was a retrospective study with a limited patient cohort at a single center, which may introduce selection bias. A larger database from multiple centers is further needed to externally validate the robustness and reproducibility of the prediction models. Second, ROIs were delineated manually by radiologists, and thus is time-consuming and prone to error and user variability; thus, it’s essential to develop an automatic and reliable liver tumor segmentation tool. Third, it should be noted that the MP-MRI data are not included in this study. In the future, we will attempt to develop a radiomics approach based on MP-MRI for response evaluation in HCC patients after TACE. Finally, we did not include genomics hallmarks related with TACE response, which may provide additional valuable information for therapeutic efficacy prediction. A future direction to consider would be identification of genetic features of HCCs that can be incorporated into the prediction schema.