Prognostic Impact of Lymphatic Invasion, Venous Invasion, Perineural Invasion, and Tumor Budding in Rectal Cancer Treated With Neoadjuvant Chemoradiotherapy Followed by Total Mesorectal Excision

LncRNAs: Emerging biomarkers and therapeutic targets in rectal cancer

According to findings, long non-coding RNAs (lncRNAs) have an important function in the onset and growth of various cancers, including rectal cancer (RC). RC offers unique issues in terms of diagnosis, treatment, and results, needing a full understanding of the cellular mechanisms that cause it to develop. This thorough study digs into the various functions that lncRNAs perform in RC, giving views into their multiple roles as well as possible therapeutic consequences. The function of lncRNAs in RC cell proliferation, apoptosis, migratory and infiltrating capacities, epithelial-mesenchymal shift, and therapy tolerance are discussed. Various lncRNA regulatory roles are investigated in depth, yielding information on their effect on essential cell functions such as angiogenesis, death, immunity, and growth. Systemic lncRNAs are currently acknowledged as potential indications for the initial stages of identification of cancer, with the ability to diagnose as well as forecast. Besides adding to their diagnostic utility, lncRNAs offer therapeutic opportunities as actors, contributing to the expanding landscape of cancer research. Moreover, the investigation looks into the assessment and predictive utility of lncRNAs as RC markers. The article also offers insight into lncRNAs as chemoresistance and drug resistance facilitators in the setting of RC.

Clinicopathological factors predict residual lymph node metastasis in locally advanced rectal cancer with ypT0-2 after neoadjuvant chemoradiotherapy

PURPOSE

Residual lymph node metastases (RLNM) remained a great concern in the implementation of organ-preserving strategies and led to poor prognosis in locally advanced rectal cancer (LARC). In this study, we aimed to identify the clinicopathological factors correlated with RLNM in LARC patients with ypT0-2 after neoadjuvant chemoradiotherapy (NCRT).

METHODS

We retrospectively analyzed 417 patients histologically diagnosed middle-low LARC after NCRT and total mesorectal excision (TME), whose pathological staging was ypT0-2. All patients received pelvic magnetic resonance imaging (MRI) before NCRT. The radiation doses were 50-50.6 Gy for the planning gross tumor volume and 41.8-45 Gy for the planning target volume, respectively. A nomogram for predicting RLNM was constructed using a binary logistic regression. Nomogram performance was assessed by receiver operating characteristic (ROC) curve, calibration curve, decision curve analysis (DCA) and clinical impact curve (CIC).

RESULTS

After surgery, 191 patients (45.8%) were ypT0, 43 patients (10.3%) were ypT1 and 183 patients (43.9%) were ypT2, and a total of 49 patients (11.8%) were found the presence of RLNM. Multivariable analyses identified MRI-defined mesorectal fascia (MRF)-positive, high-grade histopathology at biopsy, advanced ypT-category, and the presence of perineural invasion (PNI) as the predictive factors. The nomogram, incorporating all these predictors, showed good discrimination and calibration efficacy, with the areas under the ROC curve of 0.690 (95% CI: 0.610-0.771). Both DCA and CIC demonstrated that this nomogram has good clinical usefulness.

CONCLUSION

The nomogram model can predict RLNM in patients with ypT0-2 tumors. It can help select suitable patients for performing organ-preserving strategies after NCRT.

Differential clinical outcomes after 3 versus 5 years in a comparison of preoperative chemotherapy with and without radiotherapy in locally advanced rectal cancer: A national cohort propensity score-matched study

Background

Preoperative chemotherapy alone might be a good alternative to preoperative chemoradiotherapy for patients with locally advanced rectal cancer, yet long-term real-world data from the same cohort are lacking.

Methods

Patients diagnosed with stage II-III rectal adenocarcinoma from 2011 to 2015 were randomly sampled from the SEER-Plus database to evaluate the superiority of preoperative chemoradiotherapy versus preoperative chemotherapy alone.

Findings

A total of 1314 eligible patients were enrolled, with a median follow-up of 74.0 months. At 3-year follow-up, neither overall survival (OS) nor cancer-specific survival (CSS) was significantly different between the two treatment groups. At 5-year follow-up, CSS was similar across groups (HR 0.768, 95% CI 0.532-1.108; P = 0.156), but the 5-year OS was significantly better in the preoperative chemoradiotherapy group than in the preoperative chemotherapy group (HR 0.682, 95% CI 0.538-0.866; P = 0.002). Besides, the landmark analysis indicated a direct contrast in the CSS within 3 years (HR 1.101, 95% CI 0.598-2.029; P = 0.756) versus that at 3-5 years (HR 0.597, 95% CI 0.377-0.948; P = 0.027). The landmark analysis also showed directly contrasting OS outcomes within 3 years (HR 0.761, 95% CI 0.533-1.086; P = 0.130) versus those at 3-5 years (HR 0.621, 95% CI 0.451-0.857; P = 0.003).

Interpretation

In patients with locally advanced rectal cancer under real-world treatment practices, the addition of preoperative radiotherapy to chemotherapy improves survival outcomes at 3-5 years' follow-up but not at 3-year follow-up.

Prognostic impact of tumor budding in rectal cancer after neoadjuvant therapy: a systematic review and meta-analysis

BACKGROUND

Tumor budding (TB) is a negative prognostic factor in colorectal cancer; however, its prognostic impact following neoadjuvant therapy for patients with rectal cancer remains unclear. This study aims to assess the prognostic impact of TB and the correlation between TB and other pathological features in patients with rectal cancer after neoadjuvant therapy.

METHODS

A comprehensive search of PubMed, Embase, Cochrane, Scopus, CNKI, Wanfang, and ClinicalKey databases was conducted for studies on the prognosis of TB in rectal cancer after neoadjuvant therapy from the inception of the databases to January 2023, and the final literature included was determined using predefined criteria. Quality assessment of the studies included, extraction of general and prognostic information from them, and meta-analyses were carried out progressively.

RESULTS

A total of 11 studies were included, and the results of the meta-analysis showed that high-grade tumor budding (TB-1) increased the risk of poor 5-year disease-free survival (HR = 1.75, 95% CI 1.38-2.22, P < 0.00001), 5-year overall survival (HR = 1.77, 95% CI 1.21-2.59, P = 0.003), local recurrence (OR = 4.15, 95% CI 1.47-11.75, P = 0.007), and distant metastasis (OR = 5.36, 95% CI 2.51-11.44, P < 0.0001) in patients with rectal cancer after neoadjuvant therapy. TB-1 was significantly associated with poor differentiation and lymphatic, perineural, and venous invasion.

CONCLUSION

Tumor budding is significantly correlated with unfavorable prognosis and poor pathological characteristics following neoadjuvant therapy for rectal cancer. We anticipate more high-quality, prospective studies in the future to confirm our findings.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42022377564.

Predicting tumor deposits in rectal cancer: a combined deep learning model using T2-MR imaging and clinical features

BACKGROUND

Tumor deposits (TDs) are associated with poor prognosis in rectal cancer (RC). This study aims to develop and validate a deep learning (DL) model incorporating T2-MR image and clinical factors for the preoperative prediction of TDs in RC patients.

METHODS AND METHODS

A total of 327 RC patients with pathologically confirmed TDs status from January 2016 to December 2019 were retrospectively recruited, and the T2-MR images and clinical variables were collected. Patients were randomly split into a development dataset (n = 246) and an independent testing dataset (n = 81). A single-channel DL model, a multi-channel DL model, a hybrid DL model, and a clinical model were constructed. The performance of these predictive models was assessed by using receiver operating characteristics (ROC) analysis and decision curve analysis (DCA).

RESULTS

The areas under the curves (AUCs) of the clinical, single-DL, multi-DL, and hybrid-DL models were 0.734 (95% CI, 0.674-0.788), 0.710 (95% CI, 0.649-0.766), 0.767 (95% CI, 0.710-0.819), and 0.857 (95% CI, 0.807-0.898) in the development dataset. The AUC of the hybrid-DL model was significantly higher than the single-DL and multi-DL models (both p < 0.001) in the development dataset, and the single-DL model (p = 0.028) in the testing dataset. Decision curve analysis demonstrated the hybrid-DL model had higher net benefit than other models across the majority range of threshold probabilities.

CONCLUSIONS

The proposed hybrid-DL model achieved good predictive efficacy and could be used to predict tumor deposits in rectal cancer.

CRITICAL RELEVANCE STATEMENT

The proposed hybrid-DL model achieved good predictive efficacy and could be used to predict tumor deposits in rectal cancer.

KEY POINTS

• Preoperative non-invasive identification of TDs is of great clinical significance. • The combined hybrid-DL model achieved good predictive efficacy and could be used to predict tumor deposits in rectal cancer. • A preoperative nomogram provides gastroenterologist with an accurate and effective tool.

Artificial intelligence and radiomics in magnetic resonance imaging of rectal cancer: a review

Rectal cancer (RC) is one of the most common tumours worldwide in both males and females, with significant morbidity and mortality rates, and it accounts for approximately one-third of colorectal cancers (CRCs). Magnetic resonance imaging (MRI) has been demonstrated to be accurate in evaluating the tumour location and stage, mucin content, invasion depth, lymph node (LN) metastasis, extramural vascular invasion (EMVI), and involvement of the mesorectal fascia (MRF). However, these features alone remain insufficient to precisely guide treatment decisions. Therefore, new imaging biomarkers are necessary to define tumour characteristics for staging and restaging patients with RC. During the last decades, RC evaluation via MRI-based radiomics and artificial intelligence (AI) tools has been a research hotspot. The aim of this review was to summarise the achievement of MRI-based radiomics and AI for the evaluation of staging, response to therapy, genotyping, prediction of high-risk factors, and prognosis in the field of RC. Moreover, future challenges and limitations of these tools that need to be solved to favour the transition from academic research to the clinical setting will be discussed.

Tumor budding in gastric cancer

The tumor, nodes, metastasis (TNM) staging system has long been the gold standard for the classification and prognosis of solid tumors. However, the TNM staging system is not without limitations. Prognostic heterogeneity exists within patients at the same stage. Therefore, the pursuit of other biomarkers with the potential to classify patients with cancer has never stopped. One of them, tumor budding (TB), has gained much success in colorectal cancer. In recent years, TB in gastric cancer has attracted much attention from researchers, beginning to reveal the molecular and biological aspects of this phenomenon in gastric cancer, and has emerged as a promising prognostic biomarker in gastric cancer, predicting disease progression and unfavorable survival. Therefore, it is time and essential to provide a holistic overview of TB in gastric cancer, which has not been achieved and is the aim of this review.

Prognostic prediction based on histopathologic features of tumor microenvironment in colorectal cancer

Purpose

To automatically quantify colorectal tumor microenvironment (TME) in hematoxylin and eosin stained whole slide images (WSIs), and to develop a TME signature for prognostic prediction in colorectal cancer (CRC).

Methods

A deep learning model based on VGG19 architecture and transfer learning strategy was trained to recognize nine different tissue types in whole slide images of patients with CRC. Seven of the nine tissue types were defined as TME components besides background and debris. Then 13 TME features were calculated based on the areas of TME components. A total of 562 patients with gene expression data, survival information and WSIs were collected from The Cancer Genome Atlas project for further analysis. A TME signature for prognostic prediction was developed and validated using Cox regression method. A prognostic prediction model combined the TME signature and clinical variables was also established. At last, gene-set enrichment analysis was performed to identify the significant TME signature associated pathways by querying Gene Ontology database and Kyoto Encyclopedia of Genes and Genomes database.

Results

The deep learning model achieved an accuracy of 94.2% for tissue type recognition. The developed TME signature was found significantly associated to progression-free survival. The clinical combined model achieved a concordance index of 0.714. Gene-set enrichment analysis revealed the TME signature associated genes were enriched in neuroactive ligand-receptor interaction pathway.

Conclusion

The TME signature was proved to be a prognostic factor and the associated biologic pathways would be beneficial to a better understanding of TME in CRC patients.

MRI-Based Artificial Intelligence in Rectal Cancer

Rectal cancer (RC) accounts for approximately one-third of colorectal cancer (CRC), with death rates increasing in patients younger than 50 years old. Magnetic resonance imaging (MRI) is routinely performed for tumor evaluation. However, the semantic features from images alone remain insufficient to guide treatment decisions. Functional MRIs are useful for revealing microstructural and functional abnormalities and nevertheless have low or modest repeatability and reproducibility. Therefore, during the preoperative evaluation and follow-up treatment of patients with RC, novel noninvasive imaging markers are needed to describe tumor characteristics to guide treatment strategies and achieve individualized diagnosis and treatment. In recent years, the development of artificial intelligence (AI) has created new tools for RC evaluation based on MRI. In this review, we summarize the research progress of AI in the evaluation of staging, prediction of high-risk factors, genotyping, response to therapy, recurrence, metastasis, prognosis, and segmentation with RC. We further discuss the challenges of clinical application, including improvement in imaging, model performance, and the biological meaning of features, which may also be major development directions in the future. EVIDENCE LEVEL: 5 TECHNICAL EFFICACY: Stage 2.

Tumor Stroma Ratio and Its Significance in Locally Advanced Colorectal Cancer

Colorectal cancer is the third leading cause of cancer-related death, and its incidence is rising in the younger patient population. In the past decade, research has unveiled several processes (underlying tumorigenesis, many of which involve interactions between tumor cells and the surrounding tissue or tumor microenvironment (TME). Interactions between components of the TME are mediated at a sub-microscopic level. However, the endpoint of those interactions results in morphologic changes which can be readily assessed at microscopic examination of biopsy and resection specimens. Among these morphologic changes, alteration to the tumor stroma is a new, important determinant of colorectal cancer progression. Different methodologies to estimate the proportion of tumor stroma relative to tumor cells, or tumor stroma ratio (TSR), have been developed. Subsequent validation has supported the prognostic value, reproducibility and feasibility of TSR in various subgroups of colorectal cancer. In this manuscript, we review the literature surrounding TME in colorectal cancer, with a focus on tumor stroma ratio.