Colorectal cancer is the third most common malignancy among all [1]. Recently, the recommended treatment of locally advanced rectum cancer (LARC) is “total neoadjuvant therapy,” which consists of long-term chemoradiotherapy (CRT) or short-term radiotherapy (RT) and consolidation or induction chemotherapy in suitable patients, followed by total mesorectal excision (TME) or “watch-and-wait” [2–4]. Pathological complete response (pCR) is observed at a rate of 15%–30% and it is important to truly detect these patients before surgery, who may be candidates for “watch-and-wait” [5].

Endoscopy and imaging are the most common methods for assessing treatment response. True prediction of clinical complete response (cCR) by endoscopy is approximately 85%, yet it does not discern extraluminal lesions [6]. The most suitable imaging method is magnetic resonance imaging (MRI) as it does not emit ionizing radiation and shows soft tissue with high resonance. If fibrosis develops inside the tumor after CRT, the viable tumor tissue under the fibrotic scar may not be detected with MRI [7, 8]. Diverse treatment responses necessitate an individualized treatment approach [9]. It is critical to develop markers that will predict unresponsive or more sensitive patients. Tumor heterogeneity, recognized as a potential mechanism contributing to resistance to CRT, arises from the phenomenon wherein neoplastic cells, stemming from a common progenitor undergo various mutations. This process leads to the emergence of distinct subpopulations within the tumor [10]. Furthermore, tumor cells may have molecular interactions with adipocytes in the peritumoral tissue and mesorectum, which support tumor progression and metastasis [11, 12]. Alongside their limitations, current diagnostic methods lack the capacity to comprehensively assess both intratumoral heterogeneity and peritumoral interactions that influence tumor behavior.

Radiomics is an innovative method developed with the aim of providing additional information that may affect the clinical approach derived from the reflections of the underlying pathophysiological features and intratumoral heterogeneity from medical images [13].

Radiomics studies in LARC have emphasized the noninvasive prediction of patients with complete response (CR) through preCRT and/or postCRT MRI [14] scans. Radiomics analysis was performed by segmenting only the tumor region in most of these studies. A study conducted by Shaish et al. in 2020, where they compared radiomics analyzes with different segmentations, is the first study to examine the predictive rate of pCR analysis with mesorectum segmentation. Segmented regions were tumor-only, mesorectum-only and tumor+mesorectum. The highest result was obtained from the radiomics analysis of mesorectum-only segmentation for pCR evaluation. In another study conducted in 2022, radiomics analysis from mesorectal fatty tissue segmentation was examined [15, 16]. Based on these studies, analysis of mesorectal tissue seems to be informative for pCR. In addition to the mesorectum, the pelvic extramesorectal region, which is encompassed within the clinical target volume (CTV) of RT due to the occasional presence of nodal metastases may also provide valuable insights of disease behaviour. Regarding the issue, we aimed to compare CR predictive rates of radiomics analysis operated with features extracted from segmentations of tumor-only, tumor+mesorectum, and CTV.

In our research, we focused on comparing radiomics analyzes for predicting CR based on preCRT MRI images derived from the segmentation of the tumor, tumor+mesorectum, and CTV regions in LARC patients who underwent neoadjuvant therapy. Our findings revealed that the highest AUC was achieved when analyzing solely the tumor segmentation.

The AUC values in the training set for analyzing three segmentations were as follows: 0.84 for tumor-only, 0.81 for tumor+mesorectum and 0.77 for CTV. In the test set, these AUC values were found to be 0.85 for tumor-only, 0.83 for tumor+mesorectum, and 0.72 for CTV segmentations, respectively. Regarding sensitivity, the test set yielded values of 76% for tumor-only, 90% for tumor+mesorectum, and 76% for CTV segmentations. In terms of specificity, the test set exhibited 71% for tumor-only, 67% for tumor+mesorectum, and 62% for CTV segmentations.

Radiomics for LARC has been evaluated in many studies. Models have been developed for the prediction of pCR, survival, and recurrence using preCRT and/or postCRT MRI images [14]. In the realm of rectal cancer, where locally advanced stages can still yield prolonged survival outcomes, there is a growing emphasis on personalized treatment strategies aimed at minimizing side effects. The ability to predict pCR based on preCRT MRI data has emerged as a valuable guiding marker for tailoring individualized treatment approaches.

Studies to date have mostly focused on pCR prediction through radiomics analysis of tumor region segmentations from preCRT MRI images. It is noteworthy that the behavior of rectal tumors is not solely dictated by the tumor itself; it is profoundly influenced by the transformation of peritumoral stromal tissue and the resultant alterations in the microenvironment [11, 12]. Consequently, the mesorectal tissue is deemed to harbor crucial insights into the disease. However, only two studies incorporated the mesorectum within the segmentation region [15, 16]. In 2020, Shaish et al. conducted a comparative analysis of radiomics, exploring various segmentation approaches encompassing tumor, mesorectum, and tumor+mesorectum regions. Their study reported that analysis from tumor+mesorectum segmentation achieved the highest AUC score (AUC: 0.80) for tumor regression scoring, underlining its significance. Subsequently, in 2022, Jayaprakasam et al. conducted analysis with mesorectal fatty tissue segmentation and achieved a AUC result of 0.89.

In datasets characterized by non-uniform distributions, such as the dataset in this study, the commonly employed parameter for assessing outcomes is the AUC. Consequently, AUC results were employed in our investigation for comparative analysis. It is important to acknowledge, however, that certain literature sources posit that metrics such as recall (sensitivity) and precision (PPV) may carry enhanced significance in certain contexts [20]. Accordingly, our study aimed to contextualize our findings by incorporating these metrics into our analysis.

Based on the AUC results, radiomics analysis of tumor segmentation exhibited the most favorable performance in both training and test datasets. Nevertheless, it is noteworthy that segmentation of tumor+mesorectum demonstrated superior accuracy, sensitivity, and PPV in the test, and superior sensitivity in the training set. This observation leads us to assert that the incorporation of mesorectal segmentation into future response assessment studies for LARC may harbor considerable potential.

In the context of the “watch-and-wait” protocol, an imperative prerequisite for patients to qualify as cCR is the identification of a negative lymph node on MRI [19]. Notably, while not encountered within our patient cohort, cases featuring ypT0N1 status have been reported in literature [21]. It is pertinent to emphasize that relying on radiomics based solely on tumor segmentation may prove insufficient in predicting CR. This aspect assumes significance as radiomics is being explored as a potential imaging-based biomarker guiding the selection of optimal neoadjuvant treatment regimens. To this end, a solitary study has undertaken radiomics analysis inclusive of lymph node segmentation on preCRT MRI scans. The outcomes of this study revealed an AUC of 0.91, underscoring the feasibility of accurately detecting a nodal CR from preCRT MRI [22] images.

Considering these findings, we posited that radiomics analysis with CTV region segmentation, encompassing both tumor and pelvic lymph node regions, could yield meaningful insights. However, the resultant AUC of 0.71 was found to be lower than that achieved through tumor-only and tumor+mesorectum segmentations. It is conceivable that, the presence of the tumor-free rectal region within the delineated CTV during segmentation, may have affected the obtained results negatively. In subsequent studies, it is advisable to consider a multi-sequence approach and to exclude the tumor-free rectum from the CTV region during the segmentation process.

Local recurrence rate after TME and neoadjuvant treatments generally falls within the range of 5%–10%, varying across different studies. A substantial proportion of such recurrences manifest within the extramesorectal lateral pelvic nodal region. The implementation of lateral pelvic nodal dissection remains a subject of controversy within the existing literature [23]. Consequently, while analysis with mesorectal segmentation has yielded the most favorable outcomes in terms of accuracy and sensitivity, there is a rationale to consider the assessment of the lateral pelvic nodal region within response evaluation studies with radiomics analysis, as it may offer valuable guidance for tailoring individualized treatment strategies.

It is important to note that, focus of most studies lies in the inclusion of patients who have attained a pCR, excluding patients with cCR. This practice carries the accidental risk of omitting a segment of patients who have indeed achieved a CR from the evaluative framework. In an effort to mitigate this potential limitation and maintain consistency, our study encompassed patients who exhibited cCR and have, to date, remained free from any relapse during the follow-up period.

Furthermore, it is worth highlighting that our study adopted a distinctive approach by not restricting the patient cohort to data acquired from a single MRI device, unlike most studies. Instead, we performed radiomics analysis using heterogeneous MRI data from two devices, resulting in an AUC value of 0.84.

Several limitations are worth acknowledging in our study. Firstly, its retrospective nature may be considered a potential limitation. Nonetheless, it is essential to emphasize that patient evaluations were conducted prospectively. Furthermore, the absence of an assessment regarding multiple readers and the lack of an interobserver reproducibility evaluation can be perceived as a limitation. Nevertheless, it is plausible that for volumes characterized by such standardized boundaries, such as CTV, the interreader variations are likely to be less pronounced compared to tumor segmentation. This aspect, however, could serve as a topic for future investigations. Moreover, it should be acknowledged that the models developed in our study were not subjected to evaluation using an external validation dataset. The primary rationale behind this decision lies in the objective of our study, which primarily aimed to compare outcomes across analyzes of different region segmentations rather than constructing a predictive model. Therefore, external validation was not prioritized for the specific research objectives of this study.

In our research, we conducted a comparative analysis of radiomics assessments based on preCRT MRI images, focusing on three distinct segmentation areas: tumor-only, tumor combined with the mesorectum, and CTV. Although the highest AUC result was achieved with radiomics analysis of tumor segmentation, our investigation revealed that the highest accuracy and sensitivity were observed with radiomics analysis of combined tumor and mesorectum segmentations. Our findings align with previous studies, suggesting that the mesorectum contains valuable insights for the prediction of CR.

Finally, although the lowest predictive performance was observed with the analysis of CTV segmentation, predictive results of this region could be improved within further trials, considering the fact that lateral pelvic nodal region is also a possible local metastasis area and should be involved in CR prediction analyzes of LARC. There is a need for further research, encompassing larger patient cohorts and involving multiple readers with different segmentation methods to explore the inclusion of the mesorectum and pelvic nodal regions within the segmentation area for the prediction of CR as well as distant metastasis and prognosis in LARC.