Our study capitalized on MRI-based deep transfer learning techniques integrated with radiomic features, utilizing LR, RF, DT, and SVM machine learning algorithms to construct four predictive models effectively. Among these, the SVM model exhibited superior performance. By deploying the SVM model as the conclusive output and incorporating clinical independent risk factors, we formulated an encompassing predictive model. This model attained AUC values of 0.946 and 0.920 within the training and test datasets, respectively. Despite histopathology remaining the gold standard for tumor staging, the acquisition of tumor tissue samples predominantly depends on invasive surgical procedures or biopsies, potentially leading to inherent sampling errors and observer variation in pathological analysis. Research highlights that between 7–15% of patients encounter uncertainty in pathological diagnosis, emphasizing the critical need for imaging examinations to more comprehensively evaluate tumor heterogeneity[17, 18]. In this context, non-invasive MRI imaging technology assumes a pivotal role in the diagnostic staging of rectal cancer[19–22]. Nevertheless, the present constraints of imaging modalities open avenues for advanced image feature extraction and analysis methodologies. Employing high-throughput analysis at the voxel level of MRI images, radiomics, and deep learning facilitate the non-invasive prediction of rectal cancer's pathological staging, extracting profound features including morphology, histology, and functionality. This methodology, being more objective than conventional approaches, has demonstrated gratifying predictive results. Our 3D CNN predictive model, predicated on axial high-resolution T2-weighted imaging (T2WI), exhibited significant diagnostic effectiveness across both training and test datasets, affirming the model's robustness as an efficacious, non-invasive, pre-surgical technique for forecasting rectal cancer staging.
ResNet is currently one of the most acclaimed architectures in the deep learning landscape. Research employing the ResNet50 model has effectively distinguished between glioblastomas and solitary brain metastases. This achievement was realized through the development of a model utilizing 2D CNN, which attained an AUC of 0.835 in external test sets[23]. Furthermore, some researchers have incorporated the ResNet34 architecture into the convolutional blocks of the U-Net network to classify the tumor's enhanced, non-enhanced, and necrotic regions. This novel method has markedly enhanced model efficiency while also expediting the speed of model convergence and deepening the training process[24]. These studies have leveraged the ResNet architecture to develop 2D classification models. Contrarily, our investigation employed a distinct approach by executing random cropping around tumor contours instead of inputting full images. This technique efficiently refined the model’s depth and performance by extracting pertinent tumor features and eliminating superfluous ones. Prior studies involved processing MRI images that were converted into JPG format scene images. Although this method showed effectiveness, it neglected the inter-layer connectivity inherent in MRI images, thereby constraining the scope for model enhancement. As a result, we transitioned from 2D CNN to fully embracing 3D CNN, aiming to thoroughly leverage the correlations present in MRI images across three-dimensional spaces, encompassing vertical, horizontal, and axial dimensions. This innovative tactic is anticipated to furnish the medical image processing domain with more detailed and accurate information, thereby offering more robust support for disease diagnosis and analytical processes. Furthermore, by filtering out irrelevant voxels, the 3D network not only bolsters the precision of model classification but also simplifies the training process[25, 26].
Tumor marker detection is characterized by its minimally invasive approach and the potential for frequent short-term assessments, making strategic utilization of tumor marker testing invaluable for the screening, diagnosis, staging, and prognosis of cancer[27–30]. Research has identified over ten serum tumor markers associated with colorectal cancer, among which CEA and CA19-9 are the most widely utilized. CEA, a high-molecular-weight glycoprotein produced by normal rectal cells, functions as an intercellular adhesion molecule facilitating the aggregation of colorectal cancer cells. Conversely, CA19-9, a high-molecular-weight glycolipid, primarily influences cell adhesion functions, playing a pivotal role in the progression of tumors[31]. This study observed elevated levels of CEA and CA19-9 in patients with stage T3 colorectal cancer compared to those in stage T2, suggesting that higher levels of these markers may indicate enhanced proliferative capabilities of tumor cells, lower levels of differentiation, and greater malignancy severity. The significantly higher malignancy in stage T3 colorectal cancer as opposed to T2 implies a broader disparity in differentiation levels. Previous research corroborates the utility of CEA and CA19-9 in diagnosing colorectal cancer, prognosticating outcomes, and monitoring recurrence[27, 28]. LIN et al.[32], in their investigation using radiomics nomograms for the preoperative prediction of colorectal cancer T staging, demonstrated that both CEA (OR = 4.08, 95%CI:1.859.00) and CA19-9 (OR = 5.83, 95%CI:1.3325.62) were statistically significant in univariate analyses (P < 0.05), with CEA emerging as an independent risk factor in multivariate analysis (P = 0.044), findings that mirror our own. Earlier studies have noted a correlation between tumor invasiveness and size[33], suggesting that larger tumors possess greater invasive potential, deeper infiltration into the intestinal wall, and subsequently, a higher T staging. In our analysis, theLD of tumors in the T2 stage was found to be smaller than in T3, with LD proving to be an independent risk factor for distinguishing between these stages (OR = 5.117, 95%CI:1.159 ~ 22.584), a consistency with prior reports.
While this study yields significant insights, it is not without its limitations. Firstly, due to the data being derived from a single center, the population distribution and study scope are somewhat narrow. Consequently, our findings may be influenced by specific regional and demographic traits, limiting their applicability across diverse regions or populations. To augment the generalizability and reliability of our findings, future research should be conducted in a multicenter setting to validate and broaden our observations. Secondly, this study utilized a retrospective analysis methodology, potentially introducing selection bias. Despite endeavors to mitigate bias within the study design, the inherent constraints of retrospective studies may still impinge on the interpretation of our findings.Furthermore, our advancements in employing tumor markers such as CEA and CA19-9, alongside MRI imaging characteristics for colorectal cancer staging, introduce a novel paradigm for non-invasive diagnosis. Nevertheless, forthcoming research should investigate an expanded spectrum of biomarkers and the potential amalgamation of cutting-edge imaging technologies to elevate the precision and efficiency of colorectal cancer staging. Moreover, the incorporation of novel machine learning models and algorithms, particularly for analyzing complex medical imaging data, could yield more profound insights into early tumor diagnosis and treatment strategies. In conclusion, future research endeavors should focus on enlarging sample sizes, adopting prospective study designs, and delving into a broader range of biomarkers and imaging parameters. This approach will provide a more thorough and accurate foundation for diagnosing and treating colorectal cancer.