MRI of Rectal Cancer: Tumor Staging, Imaging Techniques, and Management

Rectal cancer - French intergroup clinical practice guidelines for diagnosis, treatment, and follow-up (TNCD, SNFGE, FFCD, GERCOR, UNICANCER, SFCD, SFED, SFRO, ACHBT, SFP, RENAPE, SNFCP, AFEF, SFR, and GRECCAR)

BACKGROUND

This article summarizes the French intergroup guidelines regarding rectal adenocarcinoma (RA) management published in September 2023, available on the French Society of Gastroenterology website.

METHODS

This work was supervised by French medical and surgical societies involved in RA management. Recommendations were rated from A to C according to the literature until September 2023.

RESULTS

Based on the pretreatment work-up, RA treatment was divided into four groups. T1N0 can be treated by endoscopic or surgical excision alone if there is no risk factor for lymph node involvement. For T2N0, radical surgery with total mesorectal excision is recommended, but rectal conservation is possible for small tumors (<4cm) after complete/subcomplete response following chemoradiotherapy. For T12N+ or T3+any N, total neoadjuvant treatment (TNT) followed by radical surgery is the gold standard, but rectal conservation is possible for small tumors after complete/subcomplete response following TNT. T3N2 or T+any N are an indication for TNT followed by radical surgery. Immunotherapy shows promise for dMMR/MSI RA. For metastatic tumors, recommendations are based on less robust evidence and chemotherapy plays a major role.

CONCLUSION

These guidelines aim at providing a personalized therapeutic strategy and are constantly being optimized. Each case should be discussed by a multidisciplinary team.

Quantitative Dynamic Contrast-Enhanced Magnetic Resonance Imaging and Positron Emission Tomography (PET) for Distinguishing Metastatic Lymph Nodes from Nonmetastatic Among Patients with Rectal Cancer: A Systematic Review and Meta-Analysis

Objective  The objective of this research was to assess the proficiency of quantitative dynamic contrast-enhanced magnetic resonance imaging (QDCE-MRI) and positron emission tomography (PET) imaging in distinguishing between metastatic and nonmetastatic lymph nodes in cases of rectal carcinoma. Method  This meta-analysis was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses standards. Two independent reviewers systematically searched databases including PubMed, Embase, Web of Science, and the Cochrane Library. The research took place in July 2022, with no restriction on the initial date of publication. For the analysis, we utilized Stata software (version 16.0), Review Manager (version 5.3), and the Open Meta-Analyst computational tool. Results  A total of 19 studies consisting of 1,451 patients were included in the current meta-analysis. The differences between metastatic and nonmetastatic lymph node parameters were significant by using short axis and Ktrans (6.9 ± 4 vs. 5.4 ± 0.5, 0.22 ± 0.1 vs. 0.14 ± 0.1, respectively). Contrast-enhanced MRI (CE-MRI) showed 73% sensitivity, 71% specificity, and 79% accuracy in detecting metastatic lymph nodes among rectal cancer patients based on six included studies ( n  = 530). The overall sensitivity, specificity, and accuracy of QDCE-MRI using Ktrans was calculated to be 80, 79, and 80%, respectively. Furthermore, PET-computed tomography (CT) showed a sensitivity of 80%, specificity of 91%, and accuracy of 86% in distinguishing metastatic lymph nodes. Quality utility analysis showed that using CE-MRI, QDCE-MRI, and PET-CT would increase the posttest probability to 69, 73, and 85%, respectively. Conclusion  QDCE-MRI demonstrates a commendable sensitivity and specificity, but slightly overshadowed by the higher specificity of PET-CT at 91%, despite comparable sensitivities. However, the heterogeneity in PET-CT sensitivity across studies and its high specificity indicate variability that can influence clinical decision-making. Thus, combining these imaging techniques and perhaps newer methods like PET/MRI could enhance diagnostic accuracy, reduce variability, and improve patient management strategies in rectal cancer.

Added value of histogram analysis of intravoxel incoherent motion and diffusion kurtosis imaging for the evaluation of complete response to neoadjuvant therapy in locally advanced rectal cancer

OBJECTIVE

To evaluate how intravoxel incoherent motion (IVIM) and diffusion kurtosis imaging (DKI) histogram analysis contribute to assessing complete response (CR) to neoadjuvant therapy (NAT) in locally advanced rectal cancer (LARC).

MATERIAL AND METHODS

In this prospective study, participants with LARC, who underwent NAT and subsequent surgery, with adequate MR image quality, were enrolled from November 2021 to March 2023. Conventional MRI (T2WI and DWI), IVIM, and DKI were performed before NAT (pre-NAT) and within two weeks before surgery (post-NAT). Image evaluation was independently performed by two experienced radiologists. Pathological complete response (pCR) was used as the reference standard. An IVIM-DKI-added model (a combination of IVIM and DKI histogram parameters with T2WI and DWI) was constructed. Receiver operating characteristic (ROC) curves were generated to evaluate the diagnostic performance of conventional MRI and the IVIM-DKI-added model.

RESULTS

A total of 59 participants (median age: 58.00 years [IQR: 52.00, 62.00]; 38 [64%] men) were evaluated, including 21 pCR and 38 non-pCR cases. The histogram parameters of DKI, including skewness of kurtosis post-NAT (post-KSkewness) and root mean squared of change ratio of diffusivity (Δ%DDKI-root mean squared), were entered into the IVIM-DKI-added model. The area under the ROC curve (AUC) of the IVIM-DKI-added model for assessing CR to NAT was significantly higher than that of conventional MRI (0.855 [95% CI: 0.749-0.960] vs 0.685 [95% CI: 0.565-0.806], p < 0.001).

CONCLUSION

IVIM and DKI provide added value in the evaluation of CR to NAT in LARC.

KEY POINTS

Question The current conventional imaging evaluation system lacks adequacy for assessing CR to NAT in LARC. Findings Significantly improved diagnostic performance was observed with the histogram analysis of IVIM and DKI in conjunction with conventional MRI. Clinical relevance IVIM and DKI provide significant value in evaluating CR to NAT in LARC, which bears significant implications for reducing surgical complications and facilitating organ preservation.

Multiparametric MRI-Based Deep Learning Models for Preoperative Prediction of Tumor Deposits in Rectal Cancer and Prognostic Outcome

RATIONALE AND OBJECTIVES

To investigate the predictive value of a deep learning model based on multiparametric MRI (mpMRI) for tumor deposit (TD) in rectal cancer (RC) patients and to analyze their prognosis.

MATERIALS AND METHODS

Data from 529 RC patients who underwent radical surgery at two centers were retrospectively collected. 379 patients from center one were randomly divided into a training set (n = 265) and an internal validation (invad) set (n = 114) in a 7:3 ratio. 150 patients from center two were included in the external validation (exvad) set. Univariate and multivariate analyses were performed to identify independent clinical predictors and to construct a clinical model. Preoperative mpMRI images were utilized to extract deep features through the ResNet-101 model. Following feature selection, a deep learning model was developed. A nomogram was created by combining the clinical model with the deep learning model. The clinical applicability of each model was assessed using ROC curves, decision curve analysis (DCA), clinical impact curves (CIC), and deLong test. Kaplan-Meier survival analysis was conducted to evaluate prognostic outcome among patients.

RESULTS

Among the 529 patients, 142 (26.8%) were TD positive. In the training set, clinical model was constructed based on clinical independent predictors (cT and cN). 30 deep features were selected to calculate the deep learning radscore (DLRS) and develop the deep learning (DL) model. The AUC values for the clinical model were 0.724, 0.836, and 0.763 in the training set, invad set, and exvad set, respectively. The AUC values for the DL model were 0.903, 0.853, and 0.874, respectively. The nomogram achieved higher AUC values of 0.925, 0.919, and 0.9, respectively. The DeLong test indicated that the predictive performance of the nomogram was superior to both the DL model and the clinical model in training and invad sets. Kaplan-Meier survival analysis showed that both the DL model and the nomogram effectively stratified patients into high-risk and low-risk groups for 3-year DFS (p < 0.05).

CONCLUSION

The nomogram, which integrates mpMRI-based deep radiomic features and clinical characteristics, effectively predicts preoperative TD status in RC. Both the DL model and the nomogram can effectively stratify patients' 3-year DFS risk.

Body MRI Approach: Guide for Common Indications

The slide presentation demonstrates an approach to body MRI for common indications in the abdomen and pelvis, incorporating clinical information, knowledge of imaging patterns, and various scoring paradigms.

Application of a combined clinical prediction model based on enhanced T1-weighted image(T1WI) full volume histogram in peripheral nerve invasion (PNI) and lymphatic vessel invasion (LVI) in rectal cancer

PURPOSE

This study aims to use a combined clinical prediction model based on enhanced T1-weighted image(T1WI) full volume histogram to predict preoperative peripheral nerve invasion (PNI) and lymphatic vessel invasion (LVI) in rectal cancer.

METHODS

We included a total of 68 PNI patients and 80 LVI patients who underwent surgical resection and pathological confirmation of rectal cancer. According to the PNI/LVI status, patients were divided into PNI positive group (n = 39), the PNI negative group (n = 29), LVI positive group (n = 48), and the LVI negative group (n = 32). External validation included a total of 42 patients with nerve and vascular invasion in patients with surgically resected and pathologically confirmed rectal cancer at another healthcare facility, with a PNI positive group (n = 32) and a PNI-negative group (n = 10) as well as an LVI positive group (n = 35) and LVI-negative group (n = 7). All patients underwent 3.0T magnetic resonance T1WI enhanced scanning. We use Firevoxel software to delineate the region of interest (ROI), extract histogram parameters, and perform univariate analysis, LASSO regression, and multivariate logistic regression analysis in sequence to screen for the best predictive factors. Then, we constructed a clinical prediction model and plotted it into a column chart for personalized prediction. Finally, we evaluate the performance and clinical practicality of the model based on the area under curve (AUC), calibration curve, and decision curve.

RESULTS

Multivariate logistic regression analysis found that variance and the 75th percentile were independent risk factors for PNI, while maximum and variance were independent risk factors for LVI. The clinical prediction model constructed based on the above factors has an AUC of 0.734 (95% CI: 0.591-0.878) for PNI in the training set and 0.731 (95% CI: 0.509-0.952) in the validation set; The training set AUC of LVI is 0.701 (95% CI: 0.561-0.841), and the validation set AUC is 0.685 (95% CI: 0.439-0.932). External validation showed an AUC of 0.722 (95% CI: 0.565-0.878) for PNI; and an AUC of 0.706 (95% CI: 0.481-0.931) for LVI.

CONCLUSIONS

This study indicates that the combination of enhanced T1WI full volume histogram and clinical prediction model can be used to predict the perineural and lymphovascular invasion status of rectal cancer before surgery, providing valuable reference information for clinical diagnosis.

Beyond the tumor region: Peritumoral radiomics enhances prognostic accuracy in locally advanced rectal cancer

BACKGROUND

The peritumoral region possesses attributes that promote cancer growth and progression. However, the potential prognostic biomarkers in this region remain relatively underexplored in radiomics.

AIM

To investigate the prognostic value and importance of peritumoral radiomics in locally advanced rectal cancer (LARC).

METHODS

This retrospective study included 409 patients with biopsy-confirmed LARC treated with neoadjuvant chemoradiotherapy and surgically. Patients were divided into training (n = 273) and validation (n = 136) sets. Based on intratumoral and peritumoral radiomic features extracted from pretreatment axial high-resolution small-field-of-view T2-weighted images, multivariate Cox models for progression-free survival (PFS) prediction were developed with or without clinicoradiological features and evaluated with Harrell’s concordance index (C-index), calibration curve, and decision curve analyses. Risk stratification, Kaplan-Meier analysis, and permutation feature importance analysis were performed.

RESULTS

The comprehensive integrated clinical-radiological-omics model (Model_ICRO) integrating seven peritumoral, three intratumoral, and four clinicoradiological features achieved the highest C-indices (0.836 and 0.801 in the training and validation sets, respectively). This model showed robust calibration and better clinical net benefits, effectively distinguished high-risk from low-risk patients (PFS: 97.2% vs 67.6% and 95.4% vs 64.8% in the training and validation sets, respectively; both P < 0.001). Three most influential predictors in the comprehensive Model_ICRO were, in order, a peritumoral, an intratumoral, and a clinicoradiological feature. Notably, the peritumoral model outperformed the intratumoral model (C-index: 0.754 vs 0.670; P = 0.015); peritumoral features significantly enhanced the performance of models based on clinicoradiological or intratumoral features or their combinations.

CONCLUSION

Peritumoral radiomics holds greater prognostic value than intratumoral radiomics for predicting PFS in LARC. The comprehensive model may serve as a reliable tool for better stratification and management postoperatively.

Predictive value of magnetic resonance imaging parameters combined with tumor markers for rectal cancer recurrence risk after surgery

BACKGROUND

An increasing number of studies to date have found preoperative magnetic resonance imaging (MRI) features valuable in predicting the prognosis of rectal cancer (RC). However, research is still lacking on the correlation between preoperative MRI features and the risk of recurrence after radical resection of RC, urgently necessitating further in-depth exploration.

AIM

To investigate the correlation between preoperative MRI parameters and the risk of recurrence after radical resection of RC to provide an effective tool for predicting postoperative recurrence.

METHODS

The data of 90 patients who were diagnosed with RC by surgical pathology and underwent radical surgical resection at the Second Affiliated Hospital of Bengbu Medical University between May 2020 and December 2023 were collected through retrospective analysis. General demographic data, MRI data, and tumor markers levels were collected. According to the reviewed data of patients six months after surgery, the clinicians comprehensively assessed the recurrence risk and divided the patients into high recurrence risk (37 cases) and low recurrence risk (53 cases) groups. Independent sample t-test and χ² test were used to analyze differences between the two groups. A logistic regression model was used to explore the risk factors of the high recurrence risk group, and a clinical prediction model was constructed. The clinical prediction model is presented in the form of a nomogram. The receiver operating characteristic curve, Hosmer-Lemeshow goodness of fit test, calibration curve, and decision curve analysis were used to evaluate the efficacy of the clinical prediction model.

RESULTS

The detection of positive extramural vascular invasion through preoperative MRI [odds ratio (OR) = 4.29, P = 0.045], along with elevated carcinoembryonic antigen (OR = 1.08, P = 0.041), carbohydrate antigen 125 (OR = 1.19, P = 0.034), and carbohydrate antigen 199 (OR = 1.27, P < 0.001) levels, are independent risk factors for increased postoperative recurrence risk in patients with RC. Furthermore, there was a correlation between magnetic resonance based T staging, magnetic resonance based N staging, and circumferential resection margin results determined by MRI and the postoperative recurrence risk. Additionally, when extramural vascular invasion was integrated with tumor markers, the resulting clinical prediction model more effectively identified patients at high risk for postoperative recurrence, thereby providing robust support for clinical decision-making.

CONCLUSION

The results of this study indicate that preoperative MRI detection is of great importance for predicting the risk of postoperative recurrence in patients with RC. Monitoring these markers helps clinicians identify patients at high risk, allowing for more aggressive treatment and monitoring strategies to improve patient outcomes.

Stage pT0-T1 rectal cancers: emphasis on submucosal high intensity on high-resolution T2-weighted imaging and other morphological features

BACKGROUND

Identification and staging of rectal cancer are mainly based on the difference in signal intensity (SI) between the tumor and normal structures of the intestinal wall on T2-weighted imaging. However, differentiating stage pT0-T1 from pT2 rectal tumors is difficult using routine magnetic resonance imaging (MRI) sequences.

PURPOSE

To summarize and explore whether MRI findings from routine imaging can help differentiate pT0-T1 from pT2 rectal tumors.

MATERIAL AND METHODS

A total of 110 patients with pT0-T2 rectal cancer underwent preoperative pelvic MRI examinations and tumor resection without preoperative chemoradiotherapy. MRI findings of rectal lesions (including tumor location, shape, longitudinal length, maximum cross-section, submucosal high intensity [SHI], extramural fibrotic scarring, wall shrinkage, lesion-to-wall signal intensity ratio, and presence of lymph node with short axis over 3 mm) and clinical characteristics were analyzed by univariate and multivariate analyses to screen the independent factors associated with pathological results.

RESULTS

Of all the lesions, 32 tumors were proved to be pT0-T1 and 78 tumors were pT2. Univariate and multivariate logistic regression analyses revealed that tumor shape (odds ratio [OR] = 24.607, P < 0.001), SHI (OR = 6.129, P = 0.002), and extramural fibrotic scarring (OR = 0.110, P = 0.007) were independent factors distinguishing pT0-T1 tumors from pT2 tumors. If the rectal lesion has a cauliflower-like shape with SHI and no extramural fibrotic scarring, it is more likely to be a pT0-T1 tumor.

CONCLUSION

The imaging features obtained from the routine MRI sequence showed potential value for differentiating pT0-T1 from pT2 rectal tumors.

Development and evaluation of the mrTE scoring system for MRI-detected tumor deposits and extramural venous invasion in rectal cancer

PURPOSE

Tumor deposits (TDs) and extramural venous invasion (EMVI) in locally advanced rectal cancer (LARC) are MRI-detectable markers that reflect the invasive and metastatic potential of tumors. However, both mrTDs and mrEMVI are closely associated with peritumoral vascular signals, and they show a high degree of statistical correlation. We developed a novel scoring system that integrates mrTDs and mrEMVI into a single parameter, simplifying the assessment process and capturing the prognostic value of both factors simultaneously.

METHODS

We retrospectively included LARC patients who received neoadjuvant chemoradiotherapy at five centers and proposed a novel MRI-based scoring system, mrTE (derived from mrTDs and mrEMVI), to integrate the prognostic significance of mrEMVI and mrTDs in rectal cancer. The prognostic value of different mrTE scores was evaluated using Kaplan-Meier curves and the Cox model. The predictive accuracy of the new scoring system was evaluated using the integrated area under the ROC curve (iAUC).

RESULTS

A total of 1188 patients with LARC were included in the evaluation cohort to assess the reliability of the novel imaging scoring system. Based on the mrTE scores ranging from 0 to 2, the patients were categorized into three groups. The 3-year disease-free survival rates for the groups were 88.1%, 78.1%, and 51.9% (score 1 vs 0: HR 2.00, 95% CI 1.36-2.93, p < 0.001; score 2 vs 0: HR 4.75, 95% CI 3.61-6.26, p < 0.001). The mrTE scoring system demonstrated superior performance in predicting DFS compared to other clinical and imaging markers, with a higher predictive accuracy (iAUC = 0.707).

CONCLUSIONS

The mrTE scoring system simplifies the clinical assessment of relevant MR markers and has proven to be an effective tool for predicting the prognosis of LARC patients.

Risk prediction models for dysphagia after radiotherapy among patients with head and neck cancer: a systematic review and meta-analysis

Background

Predictive models can identify patients at risk and thus enable personalized interventions. Despite the increasing number of prediction models used to predict the risk of dysphagia after radiotherapy in patients with head and neck cancer (HNC), there is still uncertainty about the effectiveness of these models in clinical practice and about the quality and applicability of future studies. The aim of this study was to systematically evaluate and analyze all predictive models used to predict dysphagia in patients with HNC after radiotherapy.

Methods

PubMed, Cochrane Library, EMbase and Web of Science databases were searched from database establishment to August 31, 2024. Data from selected studies were extracted using predefined tables and the quality of the predictive modelling studies was assessed using the PROBAST tool. Meta-analysis of the predictive performance of the model was performed using the "metafor" package in R software.

Results

Twenty-five models predicting the risk of dysphagia after radiotherapy in patients with HNC were included, covering a total of 8,024 patients. Common predictors include mean dose to pharyngeal constrictor muscles, treatment setting, and tumor site. Of these models, most were constructed based on logistic regression, while only two studies used machine learning methods. The area under the receiver operating characteristic curve (AUC) reported values for these models ranged from 0.57 to 0.909, with 13 studies having a combined AUC value of 0.78 (95% CI: 0.74-0.81). All studies showed a high risk of bias as assessed by the PROBAST tool.

Conclusion

Most of the published prediction models in this study have good discrimination. However, all studies were considered to have a high risk of bias based on PROBAST assessments. Future studies should focus on large sample size and rigorously designed multicenter external validation to improve the reliability and clinical applicability of prediction models for dysphagia after radiotherapy for HNC.

Systematic review registration

https://www.crd.york.ac.uk/prospero, identifier CRD42024587252.

Pathological prognostic factors of rectal cancer based on diffusion-weighted imaging, intravoxel incoherent motion, and diffusion kurtosis imaging

OBJECTIVES

To explore diffusion-weighted imaging (DWI), intravoxel incoherent motion (IVIM), and diffusion kurtosis imaging (DKI) for assessing pathological prognostic factors in patients with rectal cancer.

MATERIALS AND METHODS

A total of 162 patients (105 males; mean age of 61.8 ± 13.1 years old) scheduled to undergo radical surgery were enrolled in this prospective study. The pathological prognostic factors included histological differentiation, lymph node metastasis (LNM), and extramural vascular invasion (EMVI). The DWI, IVIM, and DKI parameters were obtained and correlated with prognostic factors using univariable and multivariable logistic regression. Their assessment value was evaluated using receiver operating characteristic (ROC) curve analysis.

RESULTS

Multivariable logistic regression analyses showed that higher mean kurtosis (MK) (odds ratio (OR) = 194.931, p < 0.001) and lower apparent diffusion coefficient (ADC) (OR = 0.077, p = 0.025) were independently associated with poorer differentiation tumors. Higher perfusion fraction (f) (OR = 575.707, p = 0.023) and higher MK (OR = 173.559, p < 0.001) were independently associated with LNMs. Higher f (OR = 1036.116, p = 0.024), higher MK (OR = 253.629, p < 0.001), lower mean diffusivity (MD) (OR = 0.125, p = 0.038), and lower ADC (OR = 0.094, p = 0.022) were independently associated with EMVI. The area under the ROC curve (AUC) of MK for histological differentiation was significantly higher than ADC (0.771 vs. 0.638, p = 0.035). The AUC of MK for LNM positivity was higher than f (0.770 vs. 0.656, p = 0.048). The AUC of MK combined with MD (0.790) was the highest among f (0.663), MK (0.779), MD (0.617), and ADC (0.610) in assessing EMVI.

CONCLUSION

The DKI parameters may be used as imaging biomarkers to assess pathological prognostic factors of rectal cancer before surgery.

CLINICAL RELEVANCE STATEMENT

Diffusion kurtosis imaging (DKI) parameters, particularly mean kurtosis (MK), are promising biomarkers for assessing histological differentiation, lymph node metastasis, and extramural vascular invasion of rectal cancer. These findings suggest DKI's potential in the preoperative assessment of rectal cancer.

KEY POINTS

Mean kurtosis outperformed the apparent diffusion coefficient in assessing histological differentiation in resectable rectal cancer. Perfusion fraction and mean kurtosis are independent indicators for assessing lymph node metastasis in rectal cancer. Mean kurtosis and mean diffusivity demonstrated superior accuracy in assessing extramural vascular invasion.

Amide proton transfer-weighted MRI for assessing rectal adenocarcinoma T-staging and perineural invasion: a prospective study

OBJECTIVE

To investigate the value of the pre-operative amide proton transfer-weighted (APTw) MRI to assess the prognostic factors in rectal adenocarcinoma (RA).

METHODS

This prospective study ran from January 2022 to September 2023 and consecutively enrolled participants with RA who underwent pre-operative MRI and radical surgery. The APTw signal intensity (SI) values of RA with various tumor (T), node (N) stages, perineural invasion (PNI), and tumor grade were compared by Mann-Whitney U-test or t-test. The receiver operating characteristic curve was used to evaluate the diagnostic performance of the APTw SI values.

RESULTS

A total of 51 participants were enrolled (mean age, 58 years ± 10 [standard deviation], 26 men). There were 24 in the T1-T2 stage and 9 with positive PNI. The APTw SI max, 99th, and 95th values were significantly higher in T3-T4 stage tumor than in T1-T2; the median (interquartile range) (M (IQR)) was (4.0% (3.6-4.9%) vs 3.4% (2.9- 4.3%), p = 0.017), (3.7% (3.2-4.1%) vs 3.2% (2.8-3.8%), p = 0.013), and (3.3% (2.8-3.8%) vs 2.9% (2.3-3.5%), p = 0.033), respectively. These indicators also differed significantly between the PNI groups, with the M (IQR) (4.5% (3.6-5.7%) vs 3.7% (3.2-4.2%), p = 0.017), (4.1% (3.4-4.8%) vs 3.3% (3.0-3.9%), p = 0.022), and (3.7% (2.7-4.2%) vs 2.9% (2.6-3.5%), p = 0.045), respectively.

CONCLUSION

Pre-operative APTw MRI has potential value in the assessment of T-staging and PNI determination in RA.

CLINICAL RELEVANCE STATEMENT

Pre-operative amide proton transfer-weighted MRI provides a quantitative method for noninvasive assessment of T-staging and PNI in RA aiding in precision treatment planning.

KEY POINTS

The efficacy of APTw MRI in RA needs further investigation. T3-T4 stage and PNI positive APTw signal intensities were higher than T1-T2 and non-PNI, respectively. APTw MRI provides a quantitative method for assessment of T staging and PNI in RA.

Mastering rectal cancer MRI: From foundational concepts to optimal staging

MRI plays a critical role in the local staging, restaging, surveillance, and risk stratification of patients, ensuring they receive the most tailored therapy. As such, radiologists must be familiar not only with the key MRI findings that influence management decisions but also with the appropriate MRI protocols and structured reporting. Given the complexity of selecting the optimal therapy for each patient-which often requires multidisciplinary discussions-radiologists should be well-versed in relevant treatment strategies and surgical terms, understanding their significance in guiding patient care. In this manuscript, we review the most common treatment options for managing patients with rectal adenocarcinoma, emphasizing key MRI principles and protocol characteristics for accurate staging. We also highlight important anatomical landmarks and essential factors to be described during baseline assessment. Additionally, we discuss crucial information for restaging and surveillance.

PEDRA-EFB0: colorectal cancer prognostication using deep learning with patch embeddings and dual residual attention

In computer-aided diagnosis systems, precise feature extraction from CT scans of colorectal cancer using deep learning is essential for effective prognosis. However, existing convolutional neural networks struggle to capture long-range dependencies and contextual information, resulting in incomplete CT feature extraction. To address this, the PEDRA-EFB0 architecture integrates patch embeddings and a dual residual attention mechanism for enhanced feature extraction and survival prediction in colorectal cancer CT scans. A patch embedding method processes CT scans into patches, creating positional features for global representation and guiding spatial attention computation. Additionally, a dual residual attention mechanism during the upsampling stage selectively combines local and global features, enhancing CT data utilization. Furthermore, this paper proposes a feature selection algorithm that combines autoencoders and entropy technology, encoding and compressing high-dimensional data to reduce redundant information and using entropy to assess the importance of features, thereby achieving precise feature selection. Experimental results indicate the PEDRA-EFB0 model outperforms traditional methods on colorectal cancer CT metrics, notably in C-index, BS, MCC, and AUC, enhancing survival prediction accuracy. Our code is freely available at https://github.com/smile0208z/PEDRA .

Deep learning algorithms for predicting pathological complete response in MRI of rectal cancer patients undergoing neoadjuvant chemoradiotherapy: a systematic review

PURPOSE

This systematic review examines the utility of deep learning algorithms in predicting pathological complete response (pCR) in rectal cancer patients undergoing neoadjuvant chemoradiotherapy (nCRT). The primary goal is to evaluate the performance of MRI-based artificial intelligence (AI) models and explore factors affecting their diagnostic accuracy.

METHODS

The review followed PRISMA guidelines and is registered with PROSPERO (CRD42024628017). Literature searches were conducted in PubMed, Embase, and Cochrane Library using keywords such as "artificial intelligence," "rectal cancer," "MRI," and "pathological complete response." Articles involving deep learning models applied to MRI for predicting pCR were included, excluding non-MRI data and studies without AI applications. Data on study characteristics, MRI sequences, AI model details, and performance metrics were extracted. Quality assessment was performed using the PROBAST tool.

RESULTS

Out of 512 initial records, 26 studies met the inclusion criteria. Most studies demonstrated promising diagnostic performance, with AUC values for external validation typically exceeding 0.8. The use of T2W and diffusion-weighted imaging (DWI) MRI phases enhanced model accuracy compared to T2W alone. Larger datasets generally correlated with improved model performance. However, heterogeneity in model designs, MRI protocols, and the limited integration of clinical data were noted as challenges.

CONCLUSION

AI-enhanced MRI demonstrates significant potential in predicting pCR in rectal cancer, particularly with T2W + DWI sequences and larger datasets. While integrating clinical data remains controversial, standardizing methodologies and expanding datasets will further enhance model robustness and clinical utility.

Identification of PET/CT radiomic signature for classification of locally recurrent rectal cancer: A network-based feature selection approach

Background

The modern approach to treating rectal cancer, which involves total mesorectal excision directed by imaging assessments, has significantly enhanced patient outcomes. However, locally recurrent rectal cancer (LRRC) continues to be a significant clinical issue. Identifying LRRC through imaging is complex, due to the mismatch between fibrosis and inflammatory pelvic tissue. This work aimed to develop a machine learning model for predicting LRRC using radiomic features extracted from 18F-FDG Positron Emission Tomography/Computed Tomography (PET/CT).

Methods

CT and PET images of PET/CT examinations were retrospectively collected from 44 patients, with 29 cases of recurrence (66 %) and 15 cases with no local recurrence (34 %). The whole analysis was conducted separately for CT and PET images to evaluate their different predictive power. Radiomic features were extracted from suspected lesion volumes identified by physicians and the most relevant radiomic features were selected to predict the presence or absence of LRRC. Feature selection was performed using a novel approach derived from gene expression analysis, based on the DNetPRO algorithm. The prediction was done using a Support Vector Classifier (SVC) with a 10-fold cross-validation. The efficiency of the pipeline in predicting LRRC was evaluated according to the sensitivity, specificity, Balanced Accuracy Score (BAS) and Matthews's Correlation Coefficient (MCC).

Results

CT features were found to be the most predictive, showing a sensitivity of 0.80, a specificity of 0.82, a BAS of 0.81 and an MCC of 0.61. PET features obtained a sensitivity of 0.93, a specificity of 0.61, a BAS of 0.77 and a MCC of 0.52. The combination of PET and CT features obtained a sensitivity of 0.80, a specificity of 0.75, a BAS of 0.77 and a MCC of 0.53.

Conclusions

To the best of our knowledge, the DNetPRO algorithm was applied for the first time to medical image analysis and proved suitable for the selection of radiomic features with the highest predictive power, a crucial step in a radiomic pipeline. Our results confirmed the efficiency of radiomic features in predicting LRRC, with CT features outperforming PET features in identifying the characteristic texture of LRRC. The combination of both yielded no performance improvement.

A novel MRI-based radiomics for preoperative prediction of lymphovascular invasion in rectal cancer

BACKGROUND

To develop and validate a clinical-radiomics model for preoperative prediction of lymphovascular invasion (LVI) in rectal cancer.

METHODS

This retrospective study included data from 239 patients with pathologically confirmed rectal adenocarcinoma from two centers, all of whom underwent MRI examinations. Cases from the first center (n = 189) were randomly divided into a training set and an internal validation set at a 7:3 ratio, while cases from the second center (n = 50) constituted the external validation set. The clinical features and MRI imaging characteristics of the patients in the training set were analyzed. Univariate and multivariate logistic regression analyses were used to identify independent risk factors for LVI in rectal cancer, and these risk factors were then used to construct a clinical model. Regions of interest (ROIs) were delineated on T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) sequences for feature extraction. After feature reduction and selection, the most strongly correlated features were identified, and their respective regression coefficients were calculated to construct the radiomics model. Finally, a combined clinical-radiomics model was built using a weighted linear combination of features and was visualized as a nomogram. The predictive performance of each model was quantified using receiver operating characteristics (ROC) curves and the area under the curve (AUC) in both training and validation sets, with DeLong analysis being used to compare model performance. Decision curve analysis (DCA) was used to evaluate the clinical utility of each model in the validation sets.

RESULTS

In the 239 patients, the combined model outperformed the clinical and radiomics models in predicting LVI in rectal cancer. The combined model showed excellent predictive performance in the training, internal validation, and external validation sets, with AUCs of 0.90 (0.88-0.97), 0.88 (0.78-0.99), and 0.88 (0.78-0.95), respectively. The sensitivity values were 75.9%, 68.8%, and 80.0%, respectively, and the specificity values were 90.3%, 92.7%, and 88.6%. DCA results indicated that the nomogram of the combined model had superior clinical utility compared with the clinical and radiomics models.

CONCLUSIONS

The clinical-radiomics nomogram serves as a valuable tool for non-invasive preoperative prediction of LVI status in patients with rectal cancer.

Development and validation of a multi-parametric MRI deep-learning model for preoperative lymphovascular invasion evaluation in rectal cancer

Background

Lymphovascular invasion (LVI) is an independent prognostic factor for patients with rectal cancer (RC). Recent studies have shown that deep learning (DL)-based magnetic resonance imaging (MRI) has potential in evaluating the treatment response of RC patients, but the role of MRI-based DL in assessing RC LVI remains unclear. This study sought to develop and validate a DL model to evaluate the LVI status of RC patients preoperatively based on MRI, and to test its performance at an external center.

Methods

The data of 489 patients with surgically confirmed RC were retrospectively collected from two centers. The training set and the internal validation set comprised 320 patients and 80 patients, respectively, from The Second Affiliated Hospital of Harbin Medical University; while the external testing set comprised 89 patients from Xinjiang Production and Construction Corps Tenth Division Beitun Hospital. All the patients underwent MRI examinations before surgery. Two separate image models were constructed based on the three-dimensional (3D) residual network (ResNet)-18 architecture, using only T2-weighted image (T2WI) data and diffusion-weighted image (DWI) data, respectively, to assess LVI, and a combined model was developed that integrated T2WI, DWI, and clinical factors to assess LVI. The performance of the T2WI- and DWI-based models, and the combination model was evaluated using the area under the curve (AUC) and the DeLong test. The clinical utility of these models was assessed by calibration curve analysis and decision curve analysis (DCA).

Results

The T2WI- and DWI-based DL models demonstrated robust capabilities in evaluating LVI in RC in both the internal validation set and the external test set. For the T2WI-based model, the AUC values reached 0.795 and 0.764 in the internal validation set and the external test set, respectively. For the DWI-based model, the AUC values reached 0.822 and 0.825 in the internal validation set and the external test set, respectively. The combined model exhibited superior performance, achieving AUC values of 0.899 and 0.848 in the internal validation set and the external test set, respectively. In the external test set, all three DL models exhibited robust calibration. The DCA also showed that the DWI-based model and the combined model offered a significantly greater overall net benefit in evaluating LVI than the T2WI-based model.

Conclusions

The multi-parametric MRI DL model demonstrated excellent performance in evaluating the LVI status of patients with RC. This model could serve as a complementary method for the non-invasive assessment of LVI in RC.

A systematic review of oncosurgical and quality of life outcomes following pelvic exenteration for locally advanced and recurrent rectal cancer

INTRODUCTION

Pelvic exenteration (PE) is now the standard of care for locally advanced (LARC) and locally recurrent (LRRC) rectal cancer. Reports of the significant short-term morbidity and survival advantage conferred by R0 resection are well established. However, longer-term outcomes are rarely addressed. This systematic review focuses on long-term oncosurgical and quality of life (QoL) outcomes following PE for rectal cancer.

METHODS

A systematic review of the PubMed®, Cochrane Library, MEDLINE® and Embase® databases was conducted, in accordance with the PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) guidelines. Studies were included if they reported long-term outcomes following PE for LARC or LRRC. Studies with fewer than 20 patients were excluded.

FINDINGS

A total of 25 papers reported outcomes for 5,489 patients. Of these, 4,744 underwent PE for LARC (57.5%) or LRRC (42.5%). R0 resection rates ranged from 23.2% to 98.4% and from 14.9% to 77.8% respectively. The overall morbidity rates were 17.8-87.0%. The median survival ranged from 12.5 to 140.0 months. None of these studies reported functional outcomes and only four studies reported QoL outcomes. Numerous different metrics and timepoints were utilised, with QoL scores frequently returning to baseline by 12 months.

CONCLUSIONS

This review demonstrates that PE is safe, with a good prospect of R0 resection and acceptable mortality rates in selected patients. Morbidity rates remain high, highlighting the importance of shared decision making with patients. Longer-term oncological outcomes as well as QoL and functional outcomes need to be addressed in future studies. Development of a core outcomes set would facilitate better reporting in this complex and challenging patient group.

Association Analysis Between Intratumoral and Peritumoral MRI Radiomics Features and Overall Survival of Neoadjuvant Therapy in Rectal Cancer

BACKGROUND

The use of peritumoral features to determine the survival time of patients with rectal cancer (RC) is still imprecise.

PURPOSE

To explore the correlation between intratumoral, peritumoral and combined features, and overall survival (OS).

STUDY TYPE

Retrospective.

POPULATION

One hundred sixty-six RC patients (53 women, 113 men; average age: 55 ± 12 years) who underwent radical resection after neoadjuvant therapy.

FIELD STRENGTH/SEQUENCE

3 T; T2WI sagittal, T1WI axial, T2WI axial with fat suppression, and high-resolution T2WI axial sequences, enhanced T1WI axial and sagittal sequences with fat suppression.

ASSESSMENT

Radiologist A segmented 166 patients, and radiologist B randomly segmented 30 patients. Intratumoral and peritumoral features were extracted, and features with good stability (ICC ≥0.75) were retained through intra-observer analysis. Seven classifiers, including Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Random Forest (RF), Extremely randomized trees (ET), eXtreme Gradient Boosting (XGBoost), and LightGBM (LGBM), were applied to select the classifier with the best performance. Next, the Rad-score of best classifier and the clinical features were selected to establish the models, thus, nomogram was built to identify the association with 1-, 3-, and 5-year OS.

STATISTICAL TESTS

LASSO, regression analysis, ROC, DeLong method, Kaplan-Meier curve. P < 0.05 indicated a significant difference.

RESULTS

Only Node (irregular tumor nodules in the surrounding mesentery) and ExtraMRF (lymph nodes outside the perirectal mesentery) were significantly different in 20 clinical features. Twelve intratumoral, 3 peritumoral, and 14 combined features related to OS were selected. LR, SVM, and RF classier showed the best efficacy in the intratumoral, peritumoral, and combined model, respectively. The combined model (AUC = 0.954 and 0.821) had better survival association than the intratumoral model (AUC = 0.833 and 0.813) and the peritumoral model (AUC = 0.824 and 0.687).

DATA CONCLUSION

The proposed peritumoral model with radiomics features may serve as a tool to improve estimated survival time.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 4.

Risk factors for local recurrence in patients with clinical stage II/III low rectal cancer: A multicenter retrospective cohort study in Japan

Background

Identifying risk factors for local recurrence (LR) is pivotal in optimizing rectal cancer treatment. Total mesorectal excision (TME) and lateral lymph node dissection (LLND) are the standard treatment for advanced low rectal cancer in Japan. However, large-scale studies to evaluate risk factors for LR are limited.

Methods

Data from 1479 patients with clinical stage II/III low rectal cancer below the peritoneal reflection, surgically treated between January 2010 and December 2011 across 69 hospitals, were analyzed. Fine-Gray multivariable regression modeling was used to identify risk factors associated with LR. Two models were developed: one using preoperative factors only, and the other incorporating operative and postoperative factors.

Results

Across the entire cohort, the 5-year cumulative incidence of LR was 12.3% (95% confidence interval, 10.7-14.1). The multivariable analysis associated LR with various preoperative (body mass index, distance from anal verge, cN category, and histological subtype), treatment-related (neoadjuvant therapy, and LLND), and postoperative (pT, pN, and resection margins) risk factors. For patients without neoadjuvant treatment, LR risk was unacceptably high with two or three preoperative risk factors (body mass index ≥25 kg/m2, distance from anal verge ≤4.0 cm, non-well/moderately differentiated adenocarcinoma). The 5-year cumulative incidence of LR was 24.7% in patients treated without LLND and 22.9% in patients treated with LLND.

Conclusion

This large multicenter cohort study identified some risk factors for LR in the setting where upfront TME was predominant, offering insights to optimize rectal cancer treatment.

Restriction Spectrum Imaging and Diffusion Kurtosis Imaging for Assessing Proliferation Status in Rectal Carcinoma

OBJECTIVES

To investigate the application of the three-compartment restriction spectrum imaging (RSI) model, diffusion kurtosis imaging (DKI), and diffusion-weighted imaging (DWI) in predicting Ki-67 status in rectal carcinoma.

METHODS

A total of 80 rectal carcinoma patients, including 47 high-proliferation (Ki-67 > 50%) cases and 33 low-proliferation (Ki-67 ≤ 50%) cases, underwent pelvic MRI were enrolled. Parameters derived from RSI (f1, f2, and f3), DKI (MD and MK), and DWI (ADC) were calculated and compared between the two groups. Logistic regression (LR) analysis was conducted to identify independent predictors and assess combined diagnosis. Area under the receiver operating characteristic curve (AUC), DeLong analysis, and calibration curve analyses were performed to evaluate diagnostic performance.

RESULTS

The patients with high-proliferation rectal carcinoma exhibited significantly higher f1 and MK values and significantly lower ADC, MD, f2, and f3 values than those with low-proliferation rectal carcinoma (P < 0.05). LR analysis showed that MD, MK, and f2 were independent predictors for Ki-67 status in rectal carcinoma. Moreover, the combination of these three parameters achieved an optimal diagnostic efficacy (AUC = 0.877, sensitivity = 80.85%, specificity = 84.85%) that was significantly better than that obtained using ADC (AUC = 0.783, Z = 2.347, P = 0.019), f2 (AUC = 0.732, Z = 2.762, P = 0.006), and f3 (AUC = 0.700, Z = 3.071, P = 0.002). The combined diagnosis also showed good performance (AUC = 0.859) in the internal validation analysis based on 1000 bootstrap samples, while the calibration curve demonstrated that the combined diagnosis provided good stability.

CONCLUSION

RSI, DKI, and DWI can effectively differentiate between patients with high- and low-proliferation rectal carcinoma. Furthermore, the MD, MK, and f2 imaging parameters may be a novel and promising combination biomarker for examining Ki-67 status in rectal carcinoma.

Accelerated T2W Imaging with Deep Learning Reconstruction in Staging Rectal Cancer: A Preliminary Study

Deep learning reconstruction (DLR) has exhibited potential in saving scan time. There is limited research on the evaluation of accelerated acquisition with DLR in staging rectal cancers. Our first objective was to explore the best DLR level in saving time through phantom experiments. Resolution and number of excitations (NEX) adjusted for different scan time, image quality of conventionally reconstructed T2W images were measured and compared with images reconstructed with different DLR level. The second objective was to explore the feasibility of accelerated T2W imaging with DLR in image quality and diagnostic performance for rectal cancer patients. 52 patients were prospectively enrolled to undergo accelerated acquisition reconstructed with highly-denoised DLR (DLR_H40sec) and conventional reconstruction (ConR2min). The image quality and diagnostic performance were evaluated by observers with varying experience and compared between protocols using κ statistics and area under the receiver operating characteristic curve (AUC). The phantom experiments demonstrated that DLR_H could achieve superior signal-to-noise ratio (SNR), detail conspicuity, sharpness, and less distortion within the least scan time. The DLR_H40sec images exhibited higher sharpness and SNR than ConR2min. The agreements with pathological TN-stages were improved using DLR_H40sec images compared to ConR2min (T: 0.846vs. 0.771, 0.825vs. 0.700, and 0.697vs. 0.512; N: 0.527vs. 0.521, 0.421vs. 0.348 and 0.517vs. 0.363 for junior, intermediate, and senior observes, respectively). Comparable AUCs to identify T3-4 and N1-2 tumors were achieved using DLR_H40sec and ConR2min images (P > 0.05). Consequently, with 2/3-time reduction, DLR_H40sec images showed improved image quality and comparable TN-staging performance to conventional T2W imaging for rectal cancer patients.

State-of-the-art performance of deep learning methods for pre-operative radiologic staging of colorectal cancer lymph node metastasis: a scoping review

OBJECTIVES

To assess the current state-of-the-art in deep learning methods applied to pre-operative radiologic staging of colorectal cancer lymph node metastasis. Specifically, by evaluating the data, methodology and validation of existing work, as well as the current use of explainable AI in this fast-moving domain.

DESIGN

Scoping review.

DATA SOURCES

Academic databases MEDLINE, Embase, Scopus, IEEE Xplore, Web of Science and Google Scholar were searched with a date range of 1 January 2018 to 1 February 2024.

ELIGIBILITY CRITERIA

Includes any English language research articles or conference papers published since 2018 which have applied deep learning methods for feature extraction and classification of colorectal cancer lymph nodes on pre-operative radiologic imaging.

DATA EXTRACTION AND SYNTHESIS

Key results and characteristics for each included study were extracted using a shared template. A narrative synthesis was then conducted to qualitatively integrate and interpret these findings.

RESULTS

This scoping review covers 13 studies which met the inclusion criteria. The deep learning methods had an area under the curve score of 0.856 (0.796 to 0.916) for patient-level lymph node diagnosis and 0.904 (0.841 to 0.967) for individual lymph node assessment, given with a 95% confidence interval. Most studies have fundamental limitations including unrepresentative data, inadequate methodology, poor model validation and limited explainability techniques.

CONCLUSIONS

Deep learning methods have demonstrated the potential for accurately diagnosing colorectal cancer lymph nodes using pre-operative radiologic imaging. However, several methodological and validation flaws such as selection bias and lack of external validation make it difficult to trust the results. This review has uncovered a research gap for robust, representative and explainable deep learning methods that are end-to-end from automatic lymph node detection to the diagnosis of lymph node metastasis.

Accuracy of Baseline Magnetic Resonance Imaging for Staging Rectal Cancer Patients Proceeding Directly to Surgery

BACKGROUND AND OBJECTIVES

High-resolution magnetic resonance imaging (MRI) accuracy for staging preoperative rectal cancer varies across studies. We examined MRI accuracy for T- and N-staging of rectal cancer compared with final histopathology of the resected specimen in a large Australian cohort who did not receive neoadjuvant therapy or radiation.

METHODS

Retrospective analysis of prospectively-collected clinical data from 153 rectal adenocarcinomas locally staged by high-resolution MRI between January 2012 and December 2019 that did not undergo chemoradiotherapy or radiation before surgery. T- and N-stage agreement between MRI and final histopathology was assessed using Kappa statistic. Agreement at each T-stage was evaluated using log-linear modeling. N-staging accuracy was examined using positive and negative predictive values.

RESULTS

Overall agreement between MRI and final histopathology for T-stage and N-stage was 55% and 65%, respectively. Kappa statistic found higher agreement between MRI and final histopathology for T-staging (κ = 0.33) versus N-staging (κ = 0.18). MRI correctly assessed 91% of T1 tumors, 43% of T2 tumors, 65% of T3 tumors, and 80% of T4 tumors. MRI accuracy was higher for N-negative tumors (74.1%) than for N-positive tumors (44.4%).

CONCLUSION

MRI is moderately accurate at staging T1, T3, and T4 rectal tumors but caution when staging tumors as T2 is advised. Greater accuracy for staging N-negative versus N-positive tumors is indicated.

Comparative effectiveness of two abbreviated rectal MRI protocols in assessing tumor response to neoadjuvant chemoradiotherapy in patients with rectal cancer

The present study aimed to compare the effectiveness of two abbreviated magnetic resonance imaging (MRI) protocols in assessing the response to neoadjuvant chemoradiotherapy (CRT) in patients with rectal cancer. Data from the examinations of 62 patients with rectal cancer who underwent neoadjuvant CRT and standard contrast-enhanced rectal MRI were retrospectively evaluated. Standard contrast-enhanced T2-weighted imaging (T2-WI), post-contrast T1-weighted imaging (T1-WI) and diffusion-weighted imaging (DWI) MRI, as well as two abbreviated protocols derived from these images, namely protocol AB1 (T2-WI and DWI) and protocol AB2 (post-contrast fat-suppressed (FS) T1-WI and DWI), were assessed. Measurements of lesion length and width, lymph node short-axis length, tumor staging, circumferential resection margin (CRM), presence of extramural venous invasion (EMVI), luminal mucin accumulation (MAIN), mucinous response, mesorectal fascia (MRF) involvement, and MRI-based tumor regression grade (mrTRG) were obtained. The reliability and compatibility of the AB1 and AB2 protocols in the evaluation of tumor response were analyzed. The imaging performed according to the AB1 and AB2 protocols revealed significant decreases in lesion length, width and lymph node size after CRT. These protocols also showed reductions in lymph node positivity, CRM, MRF, EMVI.Furthermore, both protocols were found to be reliable in determining lesion length and width. Additionally, compliance was observed between the protocols in determining lymph node size and positivity, CRM involvement, and EMVI after CRT. In conclusion, the use of abbreviated MRI protocols, specifically T2-WI with DWI sequences or post-contrast FS T1-WI with DWI sequences, is effective for evaluating tumor response in patients with rectal cancer following neoadjuvant CRT. The AB protocols examined in this study yielded similar results in terms of lesion length and width, lymph node positivity, CRM involvement, EMVI, MAIN, and MRF involvement.

Glucose chemical exchange saturation transfer MRI for predicting the histological grade of rectal cancer: a comparative study with amide proton transfer-weighted and diffusion-weighted imaging

BACKGROUND

To evaluate the utility of glucose chemical exchange saturation transfer (glucoCEST) MRI with non-contrast injection in predicting the histological grade of rectal cancer.

METHODS

This prospective analysis included 60 patients with preoperative rectal cancer who underwent pelvic glucoCEST, amide proton transfer-weighted imaging (APTWI), and diffusion-weighted imaging (DWI). In total, 21 low-grade and 39 high-grade cases were confirmed by postoperative pathology. The MTRasym (1.2 ppm), MTRasym (3.5 ppm), and apparent diffusion coefficient (ADC) values of lesions between the low-grade and high-grade groups were compared. The area under the receiver operating characteristic curve (AUC) was generated to evaluate the diagnostic performance of each technique. Logistic regression (LR) analysis was applied to determine independent predictors and for multi-parameter combined diagnosis.

RESULTS

Elevated MTRasym (1.2 ppm), MTRasym (3.5 ppm) values and lower ADC values were observed in the high-grade group compared with low-grade cases (all p < 0.01). The AUCs of MTRasym (1.2 ppm), MTRasym (3.5 ppm), and ADC for differentiating between low- and high-grade rectal cancer cases were 0.792, 0.839, and 0.855, respectively. The diagnostic performance of the combination of the three indexes was improved (AUC, 0.969; sensitivity, 95.24%; specificity, 87.18%). The good consistency and reliability of the combination of independent predictors were demonstrated by calibration curve analysis and DCA.

CONCLUSION

The glucoCEST MRI without contrast injection, APTWI, and DWI all facilitate the assessment of histological grade in rectal cancer, and the combination of the three can effectively discriminate between high- and low-grade rectal cancer, which is expected to be a promising imaging marker.

CRITICAL RELEVANCE STATEMENT

The glucose chemical exchange saturation transfer MRI method facilitates the assessment of histological grade in rectal cancer and offers additional information to improve the diagnostic performance of amide proton transfer-weighted imaging, and diffusion-weighted imaging.

KEY POINTS

Glucose chemical exchange saturation transfer imaging could differentiate histological grade. Amide proton transfer-weighted and diffusion-weighted were associated with histological grade. The combination of different parameters showed the best diagnostic performance.

Multifrequency Magnetic Resonance Elastography Detects Small Abdominal Lymph Node Metastasis by High Stiffness

OBJECTIVES

Response Evaluation Criteria in Solid Tumors (RECIST) 1.1 is a clinical and research standard for evaluating malignant tumors and lymph node metastasis. However, quantitative analysis of nodal status is limited to measurement of short axis diameter (SAD), and metastatic lymph nodes below 10 mm in SAD are often not detected. The purpose of this study was to evaluate the value of multifrequency magnetic resonance elastography (MRE) when added to RECIST 1.1 for detection of lymph node metastasis.

MATERIALS AND METHODS

Twenty-five benign and 82 metastatic lymph nodes were prospectively examined by multifrequency MRE at 1.5 T using tomoelastography postprocessing at 30, 40, 50, and 60 Hz (total scan time of 4 minutes). Shear wave speed as a surrogate of soft tissue stiffness was provided in m/s. Positron emission tomography-computed tomography was used as reference standard for identification of abdominal lymph node metastasis from histologically confirmed primary tumors. The diagnostic performance of MRE was compared with that of SAD according to RECIST 1.1 and evaluated by receiver operating characteristic curve analysis using generalized linear mixed models and binary logistic mixed models. Sensitivity, specificity, and predictive values were calculated for different cutoffs.

RESULTS

Metastatic lymph nodes (1.90 ± 0.57 m/s) were stiffer than benign lymph nodes (0.98 ± 0.20 m/s, P < 0.001). An area under the curve of 0.95 for a cutoff of 1.32 m/s was calculated. Using a conservative approach with 1.0 specificity, we found sensitivity (SAD/MRE/MRE + SAD, 0.56/0.84/0.88), negative predictive values (0.41/0.66/0.71), and overall accuracy (0.66/0.88/0.91) to be improved using MRE and even higher for combined MRE and SAD.

CONCLUSIONS

Multifrequency MRE improves metastatic abdominal lymph node detection by 25% based on higher tissue stiffness-even for lymph nodes with an SAD ≤10 mm. Stiffness information is quick to obtain and would be a promising supplement to RECIST.

Changing of gamma-H2AX in peripheral blood mononuclear cells during concurrent chemoradiation in locally advanced rectal cancer patients: a potential response predictor

Background

The most detrimental effect of DNA damage from radiation is DNA double-strand breaks, making it critical to identify reliable biomarkers for treatment response in cancer therapy. Gamma-H2AX (γ-H2AX), a marker of DNA double-strand breaks, was evaluated in this study as a potential biomarker for treatment response in locally advanced rectal cancer patients undergoing preoperative concurrent chemoradiation (CCRT).

Methods

Thirty patients with locally advanced rectal cancer received preoperative CCRT. Peripheral blood mononuclear cells (PBMCs) were collected at five time points: baseline, 24 hours after the first radiation fraction, mid-treatment, end of treatment, and six weeks post-CCRT. γ-H2AX levels were measured in these samples. MRI was used to assess treatment response based on magnetic resonance tumor regression grade (mrTRG). Patients were classified as responders or non-responders based on mrTRG. T-test and repeated measures analysis of variance (ANOVA) evaluated dynamic changes in γ-H2AX levels, and a multilevel linear regression model analyzed the relationship between γ-H2AX levels and treatment response.

Results

Nineteen out of thirty patients (63.33%) were classified as responders. Significant dynamic changes in γ-H2AX levels were observed between non-responders and responders (P=0.01). The multilevel linear regression model showed a trend towards increased γ-H2AX levels in responders [1.17, 95% confidence interval (CI): -0.02 to 2.34, P=0.053]. Significant differences in γ-H2AX levels were observed from baseline to mid-treatment, end of treatment, and six weeks post-CCRT. Pathologic complete response (pCR) after CCRT was associated with significantly higher γ-H2AX ratios compared to those without pCR (P=0.04). However, no significant difference was identified in the multilevel linear regression model.

Conclusions

γ-H2AX may have potential as a biomarker for treatment response in locally advanced rectal cancer patients undergoing preoperative CCRT, although further validation is required.

Synthetic double inversion recovery imaging for rectal cancer T staging evaluation: imaging quality and added value to T2-weighted imaging

OBJECTIVE

To assess the image quality of synthetic double inversion recovery (SyDIR) imaging and enhance the value of T2-weighted imaging (T2WI) in evaluating T stage for rectal cancer patients.

METHODS

A total of 112 pathologically confirmed rectal cancer patients were retrospectively selected after undergoing MRI, including synthetic MRI. The image quality of T2WI and SyDIR imaging was compared based on signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), overall picture quality, presence of motion artifacts, lesion edge sharpness, and conspicuity. The concordance between MRI and pathological staging results, using T2WI alone and the combination of T2WI and SyDIR for junior and senior radiologists, was assessed using the Kappa test. The area under the receiver operating characteristic curve (AUC) was used to assess the diagnostic efficacy of extramural infiltration in rectal cancer patients.

RESULTS

No significant differences in imaging quality were observed between conventional T2WI and SyDIR (p = 0.07-0.53). The combination of T2WI and SyDIR notably improved the staging concordance between MRI and pathology for both junior (kappa value from 0.547 to 0.780) and senior radiologists (kappa value from 0.738 to 0.834). In addition, the integration of T2WI and SyDIR increased the AUC for diagnosing extramural infiltration for both junior (from 0.842 to 0.918) and senior radiologists (from 0.917 to 0.938).

CONCLUSION

The combination of T2WI and SyDIR increased the consistency of T staging between MRI and pathology, as well as the diagnostic performance of extramural infiltration, which would benefit treatment selection.

CRITICAL RELEVANCE STATEMENT

SyDIR sequence provides additional diagnostic value for T2WI in the T staging of rectal cancer, improving the agreement of T staging between MRI and pathology, as well as the diagnostic performance of extramural infiltration.

KEY POINTS

Synthetic double inversion recovery (SyDIR) and T2WI have comparable image quality. SyDIR provides rectal cancer anatomical features for extramural infiltration detections. The combination of T2WI and SyDIR improves the accuracy of T staging in rectal cancer.

Bio-inspired circular soft actuators for simulating defecation process of human rectum

Soft robots have found extensive applications in the medical field, particularly in rehabilitation exercises, assisted grasping, and artificial organs. Despite significant advancements in simulating various components of the digestive system, the rectum has been largely neglected due to societal stigma. This study seeks to address this gap by developing soft circular muscle actuators (CMAs) and rectum models to replicate the defecation process. Using soft materials, both the rectum and the actuators were fabricated to enable seamless integration and attachment. We designed, fabricated, and tested three types of CMAs and compared them to the simulated results. A pneumatic system was employed to control the actuators, and simulated stool was synthesized using sodium alginate and calcium chloride. Experimental results indicated that the third type of actuator exhibited superior performance in pressure generation, enabling the area contraction to reach a maximum value of 1. The successful simulation of the defecation process highlights the potential of these soft actuators in biomedical applications, providing a foundation for further research and development in the field of soft robotics.

Application value of SOMATOM Force computed tomography in assisting the preoperative localization of colorectal cancer resection surgery

BACKGROUND

The objective of this study was to assess the application value of SOMATOM Force computed tomography (CT) in assisting the preoperative localization of colorectal cancer resection surgery.

METHOD

Retrospectively, the medical data of 120 inpatients with colorectal cancer were collected. The Kappa consistency test was used to evaluate diagnostic consistency in the localization and staging of colorectal cancer. The diagnostic value of preoperative SOMATOM Force CT detection was analyzed.

RESULTS

In 120 colorectal cancer patients, the accuracy of SOMATOM Force CT for preoperative localization, T staging, and N staging of colorectal cancer were 91.7% (kappa = 0.837), 88.3% (kappa = 0.772) and 91.7% (kappa = 0.773), respectively. Among 45 rectum cancer patients, there were 19 positive cases with circumferential resection margin involvement, and the accuracy of SOMATOM Force CT detection was 86.7% (kappa = 0.767). The sensitivity, specificity, positive predictive value, and negative predictive value of SOMATOM Force CT detection in evaluating the circumferential resection margin involvement of rectum cancer were 78.95%, 96.15%, 93.75%, and 86.21%, respectively.

CONCLUSIONS

There was an important application value of SOMATOM Force CT in assisting the preoperative localization and tumor staging of colorectal cancer resection surgery. There was a good diagnostic value of preoperative SOMATOM Force CT detection in evaluating the circumferential resection margin involvement of rectum cancer.

Combination of intravoxel incoherent motion histogram parameters and clinical characteristics for predicting response to neoadjuvant chemoradiation in patients with locally advanced rectal cancer

OBJECTIVE

To explore the value of histogram parameters derived from intravoxel incoherent motion (IVIM) for predicting response to neoadjuvant chemoradiation (nCRT) in patients with locally advanced rectal cancer (LARC).

METHODS

A total of 112 patients diagnosed with LARC who underwent IVIM-DWI prior to nCRT were enrolled in this study. The true diffusion coefficient (D), pseudo-diffusion coefficient (D*), and microvascular volume fraction (f) calculated from IVIM were recorded along with the histogram parameters. The patients were classified into the pathological complete response (pCR) group and the non-pCR group according to the tumor regression grade (TRG) system. Additionally, the patients were divided into low T stage (yp T0-2) and high T stage (ypT3-4) according to the pathologic T stage (ypT stage). Univariate logistic regression analysis was implemented to identify independent risk factors, including both clinical characteristics and IVIM histogram parameters. Subsequently, models for Clinical, Histogram, and Combined Clinical and Histogram were constructed using multivariable binary logistic regression analysis for the purpose of predicting pCR. The area under the receiver operating characteristic (ROC) curve (AUCs) was employed to evaluate the diagnostic performance of the three models.

RESULTS

The values of D_ kurtosis, f_mean, and f_ median were significantly higher in the pCR group compared with the non-pCR group (all P < 0.05). The value of D*_ entropy was significantly lower in the pCR group compared with the non-pCR group (P < 0.05). The values of D_ kurtosis, f_mean, and f_ median were significantly higher in the low T stage group compared with the high T stage group (all P < 0.05). The value of D*_ entropy was significantly lower in the low T stage group compared with the high T stage group (P < 0.05). The ROC curves indicated that the Combined Clinical and Histogram model exhibited the best diagnostic performance in predicting the pCR patients with AUCs, sensitivity, specificity, and accuracy of 0.916, 83.33%, 85.23%, and 84.82%.

CONCLUSIONS

The histogram parameters derived from IVIM have the potential to identify patients who have achieved pCR. Moreover, the combination of IVIM histogram parameters and clinical characteristics enhanced the diagnostic performance of IVIM histogram parameters.

Novel deep learning radiomics nomogram-based multiparametric MRI for predicting the lymph node metastasis in rectal cancer: A dual-center study

PURPOSE

To develop and evaluate a nomogram that integrates clinical parameters with deep learning radiomics (DLR) extracted from Magnetic Resonance Imaging (MRI) data to enhance the predictive accuracy for preoperative lymph node (LN) metastasis in rectal cancer.

METHODS

A retrospective analysis was conducted on 356 patients diagnosed with rectal cancer. Of these, 286 patients were allocated to the training set, and 70 patients comprised the external validation cohort. Preprocessed T2-weighted and diffusion-weighted imaging performed preoperatively facilitated the extraction of DLR features. Five machine learning algorithms-k-nearest neighbor, light gradient boosting machine, logistic regression, random forest, and support vector machine-were utilized to develop DLR models. The most effective algorithm was identified and used to establish a clinical DLR (CDLR) nomogram specifically designed to predict LN metastasis in rectal cancer. The performance of the nomogram was evaluated using receiver operating characteristic curve analysis.

RESULTS

The logistic regression classifier demonstrated significant predictive accuracy using the DLR signature, achieving an Area Under the Curve (AUC) of 0.919 in the training cohort and 0.778 in the external validation cohort. The integrated CDLR nomogram exhibited robust predictive performance across both datasets, with AUC values of 0.921 in the training cohort and 0.818 in the external validation cohort. Notably, it outperformed both the clinical model, which had AUC values of 0.770 and 0.723 in the training and external validation cohorts, respectively, and the stand-alone DLR model.

CONCLUSION

The nomogram derived from multiparametric MRI data, referred to as the CDLR model, demonstrates strong predictive efficacy in forecasting LN metastasis in rectal cancer.

Predicting Pathologic Complete Response in Locally Advanced Rectal Cancer with [68Ga]Ga-FAPI-04 PET, [18F]FDG PET, and Contrast-Enhanced MRI: Lesion-to-Lesion Comparison with Pathology

Neoadjuvant therapy in patients with locally advanced rectal cancer (LARC) has achieved good pathologic complete response (pCR) rates, potentially eliminating the need for surgical intervention. This study investigated preoperative methods for predicting pCR after neoadjuvant short-course radiotherapy (SCRT) combined with immunochemotherapy. Methods: Treatment-naïve patients with histologically confirmed LARC were enrolled from February 2023 to July 2023. Before surgery, the patients received neoadjuvant SCRT followed by 2 cycles of capecitabine and oxaliplatin plus camrelizumab. 68Ga-labeled fibroblast activation protein inhibitor ([68Ga]Ga-FAPI-04) PET/MRI, [18F]FDG PET/CT, and contrast-enhanced MRI were performed before treatment initiation and before surgery in each patient. PET and MRI features and the size and number of lesions were also collected from each scan. Each parameter's sensitivity, specificity, and diagnostic cutoff were derived via receiver-operating-characteristic curve analysis. Results: Twenty eligible patients (13 men, 7 women; mean age, 60.2 y) were enrolled and completed the entire trial, and all patients had proficient mismatch repair or microsatellite-stable LARC. A postoperative pCR was achieved in 9 patients (45.0%). In the visual evaluation, both [68Ga]Ga-FAPI-04 PET/MRI and [18F]FDG PET/CT were limited to forecasting pCR. Contrast-enhanced MRI had a low sensitivity of 55.56% to predict pCR. In the quantitative evaluation, [68Ga]Ga-FAPI-04 change in SULpeak percentage, where SULpeak is SUVpeak standardized by lean body mass, had the largest area under the curve (0.929) with high specificity (sensitivity, 77.78%; specificity, 100.0%; cutoff, 63.92%). Conclusion: [68Ga]Ga-FAPI-04 PET/MRI is a promising imaging modality for predicting pCR after SCRT combined with immunochemotherapy. The SULpeak decrease exceeding 63.92% may provide valuable guidance in selecting patients who can forgo surgery after neoadjuvant therapy.

Incorporating patient-specific information for the development of rectal tumor auto-segmentation models for online adaptive magnetic resonance Image-guided radiotherapy

Background and purpose

In online adaptive magnetic resonance image (MRI)-guided radiotherapy (MRIgRT), manual contouring of rectal tumors on daily images is labor-intensive and time-consuming. Automation of this task is complex due to substantial variation in tumor shape and location between patients. The aim of this work was to investigate different approaches of propagating patient-specific prior information to the online adaptive treatment fractions to improve deep-learning based auto-segmentation of rectal tumors.

Materials and methods

243 T2-weighted MRI scans of 49 rectal cancer patients treated on the 1.5T MR-Linear accelerator (MR-Linac) were utilized to train models to segment rectal tumors. As benchmark, an MRI_only auto-segmentation model was trained. Three approaches of including a patient-specific prior were studied: 1. include the segmentations of fraction 1 as extra input channel for the auto-segmentation of subsequent fractions, 2. fine-tuning of the MRI_only model to fraction 1 (PSF_1) and 3. fine-tuning of the MRI_only model on all earlier fractions (PSF_cumulative). Auto-segmentations were compared to the manual segmentation using geometric similarity metrics. Clinical impact was assessed by evaluating post-treatment target coverage.

Results

All patient-specific methods outperformed the MRI_only segmentation approach. Median 95th percentile Hausdorff (95HD) were 22.0 (range: 6.1-76.6) mm for MRI_only segmentation, 9.9 (range: 2.5-38.2) mm for MRI+prior segmentation, 6.4 (range: 2.4-17.8) mm for PSF_1 and 4.8 (range: 1.7-26.9) mm for PSF_cumulative. PSF_cumulative was found to be superior to PSF_1 from fraction 4 onward (p = 0.014).

Conclusion

Patient-specific fine-tuning of automatically segmented rectal tumors, using images and segmentations from all previous fractions, yields superior quality compared to other auto-segmentation approaches.

R2* mapping and reduced field-of-view diffusion-weighted imaging for preoperative assessment of nonenlarged lymph node metastasis in rectal cancer

The aim of the current study is to investigate the diagnostic value of R2* mapping versus reduced field-of-view diffusion-weighted imaging (rDWI) of the primary lesion of rectal cancer for preoperative prediction of nonenlarged lymph node metastasis (NLNM). Eighty-one patients with pathologically confirmed rectal cancer underwent preoperative R2* mapping and rDWI sequences before total mesorectal excisions and accompanying regional lymph node dissections. Two radiologists independently performed whole-tumor measurements of R2* and apparent diffusion coefficient (ADC) parameters on primary lesions of rectal cancer. Patients were divided into positive (NLNM+) and negative (NLNM-) groups based on their pathological analysis. The tumor location, maximum diameter of the tumor, and maximum short diameter of the lymph node were assessed. R2* and ADC, pT stage, tumor grade, status of mesorectal fascia, and extramural vascular invasion were also studied for their potential relationships with NLNM using multivariate logistic regression analysis. The NLNM+ group had significantly higher R2* (43.56 ± 8.43 vs. 33.87 ± 9.57, p < 0.001) and lower ADC (1.00 ± 0.13 vs. 1.06 ± 0.22, p = 0.036) than the NLNM- group. R2* and ADC were correlated to lymph node metastasis (r = 0.510, p < 0.001 for R2*; r = -0.235, p = 0.035 for ADC). R2* and ADC showed good and moderate diagnostic abilities in the assessment of NLNM status with corresponding area-under-the-curve values of 0.795 and 0.636. R2* provided a significantly better diagnostic performance compared with ADC for the prediction of NLNM status (z = 1.962, p = 0.0498). The multivariate logistic regression analysis demonstrated that R2* was a compelling factor of lymph node metastasis (odds ratio = 56.485, 95% confidence interval: 5.759-554.013; p = 0.001). R2* mapping had significantly higher diagnostic performance than rDWI from the primary tumor of rectal cancer in the prediction of NLNM status.

Extracellular gadolinium-based contrast agents

Extracellular gadolinium-based contrast agents (GBCA) are commonly used in magnetic resonance imaging (MRI) because they increase the detection of alterations, improve tissue characterisation and enable a more precise differential diagnosis. GBCAs are considered to be safe but they are not risk-free. When using GBCAs, it is important to be aware of the risks and to know how to react in different situations (pregnancy, breastfeeding, kidney failure) including if complications occur (extravasations, adverse, allergic or anaphylactic reactions). The article describes the characteristics of the gadolinium molecule, the differences in the biochemical structure of these GBCA, their biodistribution and the effect on the MRI signal. It also reviews safety aspects and the most common clinical applications.

Anal and Perianal Masses: The Common, the Uncommon, and the Rare

A variety of tumors involve the anal canal because the anal canal forms the transition between the digestive system and the skin, and this anatomical region is made of a variety of different cells and tissues. Magnetic resonance imaging (MRI) is the modality of choice for diagnosis and local staging of the anal canal and perianal neoplasms. In this pictorial review, we demonstrate the MRI anatomy of the anal canal and perianal region and display the imaging spectrum of tumors in the region along with an overview of its management. Imaging appearances of many tumorlike lesions that can cause diagnostic dilemmas are also demonstrated with pointers to differentiate between them.

The diagnostic value of histogram analysis of DWI and DKI for the mismatch repair status of rectal adenocarcinoma

Objectives

To compare the diagnostic value of histogram analysis derived from diffusion weighted imaging (DWI) and diffusion kurtosis imaging (DKI) in differentiating the mismatch repair (MMR) status of rectal adenocarcinoma.

Methods

DWI and DKI were performed in 124 patients with rectal adenocarcinoma, which were divided into deficient mismatch repair (dMMR) group and proficient mismatch repair (pMMR) group. The patients' general clinical information, pathology and image characteristics were compared. The histogram analysis of apparent diffusion coefficient (ADC), diffusion kurtosis (K) and diffusion coefficient (D)derived from DWI and DKI at b values of 1000 and 2000 s/mm2 were calculated. The diagnostic efficacy of quantitative parameters for MMR in rectal adenocarcinoma was compared.

Results

The mean, 50th, 75th and 90th in ADC quantitative parameters of dMMR group were lower when the b value was 2000 s/mm2 (all P < 0.05). With b value of 1000 s/mm2, the 10th, 25th, and 50th in the dMMR group were lower, and the skewness was higher (all P < 0.05). D values (10th, 25th and 50th) derived from DKI quantitative parameters were lower in the dMMR group. The K values (75th, 90th and Kskewness) were higher in the dMMR group, while Kkurtosis was lower (all P < 0.05). The results of multivariate logistic regression analysis showed that ADC75th(b = 2000 s/mm2), ADCskewness (b = 1000 s/mm2) and Kskewness were the statistical significant parameters (P = 0.014, 0.036 and 0.002, respectively), and the AUC values were 0.713, 0.818 and 0.835, respectively.

Conclusion

Histogram analysis derived from DWI and DKI can be good predictor of MMR. Kskewness is the strongest independent factor for predicting MMR.

The Role of Frailty in the Treatment of Locally Advanced Rectal Cancer

Owing to the gradual aging of today's population, an increase in the prevalence of frailty syndrome has been noticed. This complex state of health, characterized by decreased resilience and tolerance with concurrent increased vulnerability to stressors and adverse health-related factors, has drawn researchers' attention in recent years. Rectal cancer, which constitutes ~30% of all colorectal cancers, is a disease noticeably related to the elderly. In its locally advanced form, it is conventionally treated with trimodal therapy-neoadjuvant chemoradiotherapy followed by total mesorectal excision and adjuvant chemotherapy. Despite its good clinical outcomes and improvement in rectal cancer local control, as evidenced by clinical trials, it remains unclear if all frail patients benefit from that approach since it may be associated with adverse side effects that cannot be handled by them. As old patients, and frail ones even more noticeably, are poorly represented in the clinical trials describing outcomes of the standard treatment, this article aims to review the current knowledge on the trimodal therapy of rectal cancer with an emphasis on novel approaches to rectal cancer that can be implemented for frail patients.

Development and validation of an MRI and clinicopathological factors prediction model for low anterior resection syndrome in anterior resection of middle and low rectal cancer

Objective

To validate the predictive power of newly developed magnetic resonance (MR) morphological and clinicopathological risk models in predicting low anterior resection syndrome (LARS) 6 months after anterior resection of middle and low rectal cancer (MLRC).

Methods

From May 2018 to January 2021, 236 patients with MLRC admitted to two hospitals (internal and external validation) were included. MR images, clinicopathological data, and LARS scores (LARSS) were collected. Tumor morphology data included longitudinal involvement length, maximum tumor diameter, proportion of tumor to circumference of the intestinal wall, tumor mesorectal infiltration depth, circumferential margin status, and distance between the tumor and anal margins. Pelvic measurements included anorectal angle, mesenterial volume (MRV), and pelvic volume. Univariate and multivariate logistic regression was used to obtain independent risk factors of LARS after anterior resection Then, the prediction model was constructed, expressed as a nomogram, and its internal and external validity was assessed using receiver operating characteristic curves.

Results

The uni- and multivariate analysis revealed distance between the tumor and anal margins, MRV, pelvic volume, and body weight as significant independent risk factors for predicting LARS. From the nomogram, the area under the curve (AUC), sensitivity, and specificity were 0.835, 75.0 %, and 80.4 %, respectively. The AUC, sensitivity, and specificity in the external validation group were 0.874, 83.3 %, and 91.7 %, respectively.

Conclusion

This study shows that MR imaging and clinicopathology presented by a nomogram can strongly predict LARSS, which can then individually predict LARS 6 months after anterior resection in patients with MLRC and facilitate clinical decision-making.

Clinical relevance statement

We believe that our study makes a significant contribution to the literature. This method of predicting postoperative anorectal function by preoperative measurement of MRV provides a new tool for clinicians to study LARS.

Correlating Ultrastructural Changes in the Invasion Area of Colorectal Cancer with CT and MRI Imaging

The cancer invasion of the large intestine, a destructive process that begins within the mucous membrane, causes cancer cells to gradually erode specific layers of the intestinal wall. The normal tissues of the intestine are progressively replaced by a tumour mass, leading to the impairment of the large intestine's proper morphology and function. At the ultrastructural level, the disintegration of the extracellular matrix (ECM) by cancer cells triggers the activation of inflammatory cells (macrophages) and connective tissue cells (myofibroblasts) in this area. This accumulation and the functional interactions between these cells form the tumour microenvironment (TM). The constant modulation of cancer cells and cancer-associated fibroblasts (CAFs) creates a specific milieu akin to non-healing wounds, which induces colon cancer cell proliferation and promotes their survival. This review focuses on the processes occurring at the "front of cancer invasion", with a particular focus on the role of the desmoplastic reaction in neoplasm development. It then correlates the findings from the microscopic observation of the cancer's ultrastructure with the potential of modern radiological imaging, such as computer tomography (CT) and magnetic resonance imaging (MRI), which visualizes the tumour, its boundaries, and the tissue reactions in the large intestine.

Feasibility of Three-Dimension Chemical Exchange Saturation Transfer MRI for Predicting Tumor and Node Staging in Rectal Adenocarcinoma: An Exploration of Optimal ROI Measurement

To investigate the feasibility of predicting rectal adenocarcinoma (RA) tumor (T) and node (N) staging from an optimal ROI measurement using amide proton transfer weighted-signal intensity (APTw-SI) and magnetization transfer (MT) derived from three-dimensional chemical exchange saturation transfer(3D-CEST). Fifty-eight RA patients with pathological TN staging underwent 3D-CEST and DWI. APTw-SI, MT, and ADC values were measured using three ROI approaches (ss-ROI, ts-ROI, and wt-ROI) to analyze the TN staging (T staging, T1-2 vs T3-4; N staging, N - vs N +); the reproducibility of APTw-SI and MT was also evaluated. The AUC was used to assess the staging performance and determine the optimal ROI strategy. MT and APTw-SI yielded good excellent reproducibility with three ROIs, respectively. Significant differences in MT were observed (all P < 0.05) from various ROIs but not in APTw-SI and ADC (all P > 0.05) in the TN stage. AUCs of MT from ss-ROI were 0.860 (95% CI, 0.743-0.937) and 0.852 (95% CI, 0.735-0.932) for predicting T and N staging, which is similar to ts-ROI (T staging, 0.856 [95% CI, 0.739-0.934]; N staging, 0.831 [95% CI, 0.710-0.917]) and wt-ROI (T staging, 0.833 [95% CI, 0.712-0.918]; N staging, 0.848 [95% CI, 0.729-0.929]) (all P > 0.05). MT value of 3D-CEST has excellent TN staging predictive performance in RA patients with all three kinds of ROI methods. The ss-ROI is easy to operate and could be served as the preferred ROI approach for clinical and research applications of 3D-CEST imaging.

Deep-learning features based on F18 fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) to predict preoperative colorectal cancer lymph node metastasis

AIM

The objective of this study was to create and authenticate a prognostic model for lymph node metastasis (LNM) in colorectal cancer (CRC) that integrates clinical, radiomics, and deep transfer learning features.

MATERIALS AND METHODS

In this study, we analyzed data from 119 CRC patients who underwent F18 fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) scanning. The patient cohort was divided into training and validation subsets in an 8:2 ratio, with an additional 33 external data points for testing. Initially, we conducted univariate analysis to screen clinical parameters. Radiomics features were extracted from manually drawn images using pyradiomics, and deep-learning features, radiomics features, and clinical features were selected using Least Absolute Shrinkage and Selection Operator (LASSO) regression and Spearman correlation coefficient. We then constructed a model by training a support vector machine (SVM), and evaluated the performance of the prediction model by comparing the area under the curve (AUC), sensitivity, and specificity. Finally, we developed nomograms combining clinical and radiological features for interpretation and analysis.

RESULTS

The deep learning radiomics (DLR) nomogram model, which was developed by integrating deep learning, radiomics, and clinical features, exhibited excellent performance. The area under the curve was (AUC = 0.934, 95% confidence interval [CI]: 0.884-0.983) in the training cohort, (AUC = 0.902, 95% CI: 0.769-1.000) in the validation cohort, and (AUC = 0.836, 95% CI: 0.673-0.998) in the test cohort.

CONCLUSION

We developed a preoperative predictive machine-learning model using deep transfer learning, radiomics, and clinical features to differentiate LNM status in CRC, aiding in treatment decision-making for patients.

Mucinous Rectal Adenocarcinoma-Challenges in Magnetic Resonance Imaging Interpretation

ABSTRACT

Mucinous rectal cancer (MRC) is defined by the World Health Organization as an adenocarcinoma with greater than 50% mucin content. Classic teaching suggests that it carries a poorer prognosis than conventional rectal adenocarcinoma. This poorer prognosis is thought to be related to mucin dissecting through tissue planes at a higher rate, thus increasing the stage of disease at presentation. Developments in immunotherapy have bridged much of this prognostic gap in recent years. Magnetic resonance imaging is the leading modality in assessing the locoregional spread of rectal cancer. Mucinous rectal cancer carries unique imaging challenges when using this modality. Much of the difficulty lies in the inherent increased T2-weighted signal of mucin on magnetic resonance imaging. This creates difficulty in differentiating mucin from the adjacent background fat, making the detection of both the primary disease process as well as the locoregional spread challenging. Computed tomography scan can act as a valuable companion modality as mucin tends to be more apparent in the background fat. After therapy, diagnostic challenges remain. Mucin is frequently present, and distinguishing cellular from acellular mucin can be difficult. In this article, we will discuss each of these challenges and present examples of such situations and strategies that can be used to overcome them.

Machine learning‑based radiomics models accurately predict Crohn's disease‑related anorectal cancer

The radiological diagnosis of Crohn's disease (CD)-related anorectal cancer is difficult; it is often found in advanced stages and has a poor prognosis because of the difficulty of curative surgery. However, there are no studies on predicting the diagnosis of CD-related cancer. The present study aimed to develop a predictive model to diagnose CD cancerous lesions more accurately in a way that can be interpreted by clinicians. Patients with CD who developed anorectal CD lesions at Hyogo Medical University (Nishinomiya, Japan) between March 2009 and June 2022 were included in the present study. T2-weighted and T1-weighted magnetic resonance (MR) images were utilized for our analysis. Images of anorectal lesions were segmented using open-source 3D Slicer software, and radiomic features were extracted using PyRadiomics. Six machine learning models were investigated and compared: i) Support vector machine; ii) naive Bayes; iii) random forest; iv) light gradient boosting machine; v) extremely randomized trees; vi) and regularized greedy forest (RGF). SHapley Additive exPlanations (SHAP) values were calculated to assess the extent to which each radiomic feature contributed to the model's predictions compared to baseline, represented as the average of the model's predictions for all test data. The T2-weighted images of 28 patients with anorectal cancer and 40 non-cancer patients were analyzed and the contrast-enhanced T1-weighted images of 22 cancer and 40 non-cancer patients. The model with the highest area under the curve (AUC) was the RGF-based model constructed using T2-weighted image features, achieving an AUC of 0.944 (accuracy, 0.862; recall, 0.830). The SHAP-based model explanation suggested a strong association between the diagnosis of CD-related anorectal cancer and features such as complex lesion texture; greater pixel separation within the same coronal cross-section; larger, randomly distributed clumps of pixels with the same signal intensity; and a more spherical lesion shape on T2-weighted images. The MRI radiomics-based RGF model demonstrated outstanding performance in predicting CD-related anorectal cancer. These results may affect the diagnosis and surveillance strategies of CD-related colorectal cancer.

Multiparametric MRI-based radiomics nomogram for the preoperative prediction of lymph node metastasis in rectal cancer: A two-center study

PURPOSE

To develop a radiomic nomogram based on multiparametric magnetic resonance imaging for the preoperative prediction of lymph node metastasis (LNM) in rectal cancer.

METHODS

This retrospective study included 318 patients with pathologically proven rectal adenocarcinoma from two hospitals. Radiomic features were extracted from T2-weighted imaging, diffusion-weighted imaging, and contrast-enhanced T1-weighted imaging scans of the training cohort, and the radsore model was then constructed. The combined model was obtained by integrating the Radscore and clinical models. The area under the receiver operating characteristic curve (AUC) was used to assess the diagnostic effectiveness of each model, and the best-performing model was used to develop the nomogram.

RESULTS

The Radscore and clinical models exhibited similar diagnostic efficacy (DeLong's test, P > 0.05). The AUC of the combined model was significantly higher than those of the clinical and Radscore models in the training cohort (AUC: 0.837 vs. 0.763 and 0.787, P: 0.02120 and 0.02309) and the external validation cohort (AUC: 0.880 vs. 0.797 and 0.779, P: 0.02310 and 0.02471). However, the diagnostic performance of the three models was comparable in the internal validation cohort (P > 0.05). Thus, among the three models, the combined model exhibited the highest diagnostic efficiency. The calibration curve exhibited satisfactory consistency between the nomogram predictions and the actual results. DCA confirmed the considerable clinical usefulness of the nomogram.

CONCLUSION

The radiomics nomogram can accurately and noninvasively predict LNM in rectal cancer before surgery, serving as a convenient visualization tool for informing treatment decisions, including the choice of surgical approach and the need for neoadjuvant therapy.