MRI-detected extramural vascular invasion (mrEMVI) as the best predictive factor to identify candidates to total neoadjuvant therapy in locally advanced rectal cancer

Evaluating complete response rates and predictors in total neoadjuvant therapy for rectal cancer

BACKGROUND

There is a paradigm shift in the management of locally advanced rectal cancer (LARC) from conventional neoadjuvant treatment to total neoadjuvant therapy (TNT). Despite its growing acceptance, there are limited studies that have examined its effects on disease presentation. In addition, it is important to determine the factors that play a role in complete response (CR). Our previous data from 119 patients revealed that the CR rate was 37%, and low rectal tumors and the absence of extramural vascular invasion (EMVI) were predictors of CR. Unfortunately, there continues to be a lack of data, and reliable markers are still needed to consistently identify the best respondents. Therefore, this study aimed to determine the factors associated with CR. Moreover, this study hypothesized that both predictive factors and the CR ratio might evolve over time because of the growing patient population.

METHODS

This retrospective study included patients who completed TNT for LARC at our tertiary care center between 2015 and 2022. The primary outcome was to determine the predictors of CR. The secondary outcomes were the 2-year disease-free survival (DFS) rate and overall survival (OS) rate. CR consists of patients who sustained clinical CR (cCR) for at least 12 months under watch and wait or had pathologic CR (pCR) after surgery.

RESULTS

Of 339 patients with LARC, 208 (61.3%) successfully completed TNT. Among 208 patients, 57 (27.4%) achieved cCR, and 166 (80.0%) sustained cCR without tumor regrowth after 1 year. The remaining 151 patients (72.6%) underwent surgery, and 42 patients had pCR. The final CR rate was 42.3%. The median age of the patients was 56 years (IQR, 49-66). Moreover, 132 participants (63.5%) were male, whereas 76 participants (36.5%) were female. The median tumor size was 4.95 cm (IQR, 3.60-6.43), with most tumors in the low rectum (119 [57.2%]). Based on the MRI findings, the mesorectal facia (MRF) involvement rate was 25.0% (n = 52), and EMVI was observed in 43 patients (20.7%). Low rectal tumors, the absence of MRF involvement, and the absence of EMVI were predictors of CR. With a median follow-up of 24.7 months, 2-year DFS and OS were significantly higher among patients with CR than among patients with incomplete response (91.3% vs 71.0% [P < .01] and 98.8% vs 90.2% [P = .03], respectively).

CONCLUSION

An increasing CR rate was observed in our updated dataset compared with that in our previous study. In addition to previously identified predictors, low tumor location, and the absence of EMVI, the absence of MRF involvement was determined as a predictor of CR. Our findings offer valuable insights into clinical practice and help clinicians set clear expectations when counseling patients.

Diagnostic accuracy of state-of-the-art rectal MRI sequences for the diagnosis of extramural vascular invasion in locally advanced rectal cancer after preoperative chemoradiotherapy: dos or maybes?

OBJECTIVES

The aim of this study was to determine the accuracy of three state-of-the-art MRI sequences for the detection of extramural venous invasion (EMVI) in locally advanced rectal cancer (LARC) patients after preoperative chemoradiotherapy (pCRT).

METHODS

This retrospective study included 103 patients (median age 66 years old [43-84]) surgically treated with pCRT for LARC and submitted to preoperative contrast-enhanced pelvic MRI after pCRT. T2-weighted, DWI, and contrast-enhanced sequences were evaluated by two radiologists with expertise in abdominal imaging, blinded to clinical and histopathological data. Patients were scored according to the probability of EMVI presence on each sequence using a grading score ranging from 0 (no evidence of EMVI) to 4 (strong evidence of EMVI). Results from 0 to 2 were ranked as EMVI negative and from 3 to 4 as EMVI positive. ROC curves were drawn for each technique, using histopathological results as reference standard.

RESULTS

T2-weighted, DWI, and contrast-enhanced sequences demonstrated an area under the ROC curve (AUC) respectively of 0.610 (95% CI: 0.509-0.704), 0.729 (95% CI: 0.633-0.812), and 0.624 (95% CI: 0.523-0.718). The AUC of DWI sequence was significantly higher than that of T2-weighted (p = 0.0494) and contrast-enhanced (p = 0.0315) sequences.

CONCLUSIONS

DWI is more accurate than T2-weighted and contrast-enhanced sequences for the identification of EMVI following pCRT in LARC patients.

CLINICAL RELEVANCE STATEMENT

MRI protocol for restaging locally advanced rectal cancer after preoperative chemoradiotherapy should routinely include DWI due to its higher accuracy for the diagnosis of extramural venous invasion compared to high-resolution T2-weighted and contrast-enhanced T1-weighted sequences.

KEY POINTS

• MRI has a moderately high accuracy for the diagnosis of extramural venous invasion in locally advanced rectal cancer after preoperative chemoradiotherapy. • DWI is more accurate than T2-weighted and contrast-enhanced T1-weighted sequences in the detection of extramural venous invasion after preoperative chemoradiotherapy of locally advanced rectal cancer. • DWI should be routinely included in the MRI protocol for restaging locally advanced rectal cancer after preoperative chemoradiotherapy.

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.

Extramural vascular invasion nomogram before radical resection of rectal cancer based on magnetic resonance imaging

Purpose

This study verified the value of magnetic resonance imaging (MRI) to construct a nomogram to preoperatively predict extramural vascular invasion (EMVI) in rectal cancer using MRI characteristics.

Materials and methods

There were 55 rectal cancer patients with EMVI and 49 without EMVI in the internal training group. The external validation group consisted of 54 rectal cancer patients with EMVI and 55 without EMVI. High-resolution rectal T2WI, pelvic diffusion-weighted imaging (DWI) sequences, and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) were used. We collected the following data: distance between the lower tumor margin and the anal margin, distance between the lower tumor margin and the anorectal ring, tumor proportion of intestinal wall, mrT stage, maximum tumor diameter, circumferential resection margin, superior rectal vein width, apparent diffusion coefficient (ADC), T2WI EMVI score, DWI and DCE-MRI EMVI scores, demographic information, and preoperative serum tumor marker data. Logistic regression analyses were used to identify independent risk factors of EMVI. A nomogram prediction model was constructed. Receiver operating characteristic curve analysis verified the predictive ability of the nomogram. P &lt; 0.05 was considered significant.

Result

Tumor proportion of intestinal wall, superior rectal vein width, T2WI EMVI score, and carbohydrate antigen 19-9 were significant independent predictors of EMVI in rectal cancer and were used to create the model. The areas under the receiver operating characteristic curve, sensitivities, and specificities of the nomogram were 0.746, 65.45%, and 83.67% for the internal training group, respectively, and 0.780, 77.1%, and 71.3% for the external validation group, respectively.

Data conclusion

A nomogram including MRI characteristics can predict EMVI in rectal cancer preoperatively and provides a valuable reference to formulate individualized treatment plans and predict prognosis.

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.