In the current study, we not only investigate the prognostic value of pretreatment multiparametric MRI-based radiomics signature in patients with LARC, but also successfully develop and validate a radiomics nomogram, which was powerful in risk stratification and was able to predict DFS, DMFS and OS better than the current clinicopathological model. More importantly, the proposed radiomics nomogram might help identify which patients are expected to benefit from AC, indicating that it might be helpful for the future management of LARC.
Personalized medicine in cancer patients implies identifying imaging biomarkers to predict survival. To this end, texture analysis of different modalities has been used in LARC patients, with varying degrees of success. In a series of 56 patients with LARC, Jalil et al. identified the mean of positive pixels (MPP) of T2WI as an independent predictor of DFS and OS (20). Similarly, Lovinfosse et al. showed that the texture parameter coarseness of baseline 18F-FDG PET/CT was associated with disease-specific survival (DSS) and with DFS of LARC (28). Chee et al. also found a significant correlation between homogeneous texture features and higher DFS in 95 patients with LARC (29). All these prior studies have typically focused on few radiomics features, which seems intuitive and might underestimated the significance of radiomics. Therefore, construction of multifactor panels is a more common approach to overcome this challenge in outcome estimation.
Radiomics hypothesizes that the intratumor heterogeneity, which was difficult to detect visually, could be exhibited on the spatial distribution of voxel intensities. For the construction of radiomics signature, 3930 candidate features were reduced to only four predictors. Intriguingly, all selected features were GLSZM/GLRLM-related features, which could take into account the interaction between neighboring pixels and were well suited to measure different aspects of textural heterogeneity within the tumor (30). Moreover, wavelet features, which could reflect multi-frequency information at different scales unrecognized by the naked eye to quantify tumor heterogeneity, were the majority used in our optimal radiomics signature (3/4), similar to other MRI-based radiomics studies (16, 31).
As demonstrated in the present study, the identified radiomics signature from joint T2W, ADC, and cT1W images performed better than those from either of them alone, and was also an independent risk factor of DFS in the patients with LARC, with C-index of 0.750 and 0.752 in the training and validation cohort, respectively. A possible explanation is that the multi-sequences used in our study reflected different aspects of tumor, such as tumor intensity, cellularity, and vascularization, and a combination of them might reflect much more comprehensive information of the tumors and improve prognostication. Moreover, the performance of our radiomics signature was superior to those derived from CT or PET/CT images, with the C-index about 0.650 (32, 33). This has been confirmed by our study that the Radscore values were significant higher in the patients with progressive disease. That is, tumors with higher intratumoral heterogeneity, are more likely to be resistant to NCRT and have a poorer prognosis (18).
Furthermore, the radiomic nomogram, incorporating radiomics signature and clinicopathological factors, had significantly better ability to predict DFS than the clinicopathological model, with a higher C-index of 0.803 and positive NRI and lowest AIC, consistent with the results of Meng and Jeon et al (21, 22). The results of time-independent ROC analysis further support this conclusion. This may be because the clinicopathological features only reflect specific tumor information, while multiparametric MRI-based radiomics can comprehensively and quantifiably characterize the tumor phenotype, and thus provide a robust way to characterize the intratumoral heterogeneity noninvasively. Additionally, the radiomics nomogram’s good ability to predict DMFS and OS, also confirmed its prognostic value, which could be used to stratify patients into corresponding high and low risks groups.
Currently, AC, given after TME, has been recommend for most LARC patients, and has proved to be a powerful a robust tool against DM. However, not all patients will benefit from chemotherapy efficacy. Given these, it would be of great importance to identify patients with AC benefit. Previous studies have developed valuable radiomics models derived from different modalities, to identify patients with various cancers who will benefit from different therapies (34–36). Our findings are consistent with above statement that high-risk patients could benefit from AC and more intensive observation and aggressive treatment regimens should be considered in these cases. These findings provided a novel tool for guiding AC.
Nevertheless, some notes should be emphasized. Firstly, pN and tumor differentiation were identified as independent prognostic factors for DFS in our study, partly consistent with previous studies (22, 28, 33). Even so, a single considered strong risk factor, could hardly assess the comprehensive outcome of individual patients. In contrast, the nomogram, taking into account multiple risk factors, could provide more informative metrics and an easy-to-use tool for clinicians. Secondly, whole-tumor VOIs, rather than signal largest slice ROIs, could provide a robust way to characterize the heterogeneity of the entire lesion, and reduce concerns of selection bias. Lastly, although lacked external validation, the clinical utility of radiomic nomogram was assessed by DCA, confirming the incremental value of signature to clinicopathological model for individualized estimation.
Some limitations of our study should be acknowledged. The first is the limited sample size and the retrospective nature of data collection. Further prospective studies involving a larger population are needed to validate our model. Second, study data were collected from a single center. Although we have performed four additional divisions with good results, external validation should be warranted in the future. Third, the follow-up duration was not long enough, thus, we constructed the radiomics model primarily base on DFS. Finally, various modalities, pathological imaging, genomic sequencing, and molecular biomarkers, as well as some MRI morphologic characteristics, should be investigated and construct a more stable and accurate model, for tailored treatment into the era of personalized medicine.