Magnetic Resonance Imaging Evaluation of the Accuracy of Various Lymph Node Staging Criteria in Rectal Cancer: A Systematic Review and Meta-Analysis

Local excision of early rectal cancer: A multi-centre experience of transanal endoscopic microsurgery from the United Kingdom

BACKGROUND

Total mesorectal excision remains the gold standard for the management of rectal cancer however local excision of early rectal cancer is gaining popularity due to lower morbidity and higher acceptance by the elderly and frail patients.

AIM

To investigate the results of local excision of rectal cancer by transanal endoscopic microsurgery (TEMS) approach carried out at three large cancer centers in the United Kingdom.

METHODS

TEMS database was retrospectively reviewed to assess demographics, operative findings and post operative clinical and oncological outcomes. This is a retrospective review of the prospective databases, which included all patients operated with TEMS approach, for early rectal cancer (Node-negative T1-T2), selected T3 in unfit/frail patients.

RESULTS

Two hundred and twenty-two patients underwent TEMS surgery. This included 144 males (64.9%) and 78 females (35.1%), Median age was 71 years. The median distance of the tumours from the anal verge 4.5 cm. Median tumour size was 2.6 cm. The most frequent operative position of the patient was lithotomy (32.3%), Full-thickness rectal wall excision was done in 204 patients. Median operating time was 90 minutes. Average blood loss was minimal. There were two 90-day mortalities. Complete excision of the tumour with free microscopic margins by > 1mm were accomplished in 171 patients (76.7%). Salvage total mesorectal excision was performed in 42 patients (19.8%). Median disease-free survival was 65 months (range: 3-146 months) (82.8%), and median overall survival was 59 months (0-146 months).

CONCLUSION

TEMS provides a promising option for early rectal cancers (Large adenomas-cT1/cT2N0), and selected therapy-responding cancers. Full-thickness complete excision of the tumour is mandatory to avoid jeopardising the oncological outcomes.

MRI for Rectal Cancer: Updates and Controversies-AJR Expert Panel Narrative Review

Rectal MRI is a critical tool in the care of patients with rectal cancer, having established roles for primary staging, restaging, and surveillance. The comprehensive diagnostic and prognostic information provided by MRI helps to optimize treatment decision-making. However, challenges persist in the standardization and interpretation of rectal MRI, particularly in the context of rapidly evolving treatment paradigms, including growing acceptance of nonoperative management. In this AJR Expert Panel Narrative Review, we address recent advances and key areas of contention relating to the use of MRI for rectal cancer. Our objectives include: to discuss concepts regarding anatomic localization of rectal tumors; review the evolving rectal cancer treatment paradigm and implications for MRI assessment; discuss updates and controversies regarding rectal MRI for locoregional staging, restaging, and surveillance; review current rectal MRI acquisition protocols; and discuss challenges in homogenizing and optimizing acquisition parameters.

The Role of Predictive and Prognostic MRI-Based Biomarkers in the Era of Total Neoadjuvant Treatment in Rectal Cancer

The role of magnetic resonance imaging (MRI) in rectal cancer management has significantly increased over the last decade, in line with more personalized treatment approaches. Total neoadjuvant treatment (TNT) plays a pivotal role in the shift from traditional surgical approach to non-surgical approaches such as 'watch-and-wait'. MRI plays a central role in this evolving landscape, providing essential morphological and functional data that support clinical decision-making. Key MRI-based biomarkers, including circumferential resection margin (CRM), extramural venous invasion (EMVI), tumour deposits, diffusion-weighted imaging (DWI), and MRI tumour regression grade (mrTRG), have proven valuable for staging, response assessment, and patient prognosis. Functional imaging techniques, such as dynamic contrast-enhanced MRI (DCE-MRI), alongside emerging biomarkers derived from radiomics and artificial intelligence (AI) have the potential to transform rectal cancer management offering data that enhance T and N staging, histopathological characterization, prediction of treatment response, recurrence detection, and identification of genomic features. This review outlines validated morphological and functional MRI-derived biomarkers with both prognostic and predictive significance, while also exploring the potential of radiomics and artificial intelligence in rectal cancer management. Furthermore, we discuss the role of rectal MRI in the 'watch-and-wait' approach, highlighting important practical aspects in selecting patients for non-surgical management.

Artificial Intelligence Imaging Diagnosis Using Super-Resolution and Three-Dimensional Shape for Lymph Node Metastasis of Low Rectal Cancer: A Pilot Study From a Single Center

BACKGROUND

Although accurate preoperative diagnosis of lymph node metastasis is essential for optimizing treatment strategies for low rectal cancer, the accuracy of present diagnostic modalities has room for improvement.

OBJECTIVE

The study aimed to establish a high-precision diagnostic method for lymph node metastasis of low rectal cancer using artificial intelligence.

DESIGN

A retrospective observational study.

SETTINGS

A single cancer center and a college of engineering in Japan.

PATIENTS

Patients with low rectal adenocarcinoma who underwent proctectomy, bilateral lateral pelvic lymph node dissection, and contrast-enhanced multidetector row CT (slice ≤1 mm) between July 2015 and August 2021 were included in the present study. All pelvic lymph nodes from the aortic bifurcation to the upper edge of the anal canal were extracted, regardless of whether within or beyond the total mesenteric excision area, and pathological diagnoses were annotated for training and validation.

MAIN OUTCOME MEASURES

Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy.

RESULTS

A total of 596 pathologically negative nodes and 43 positive nodes from 52 patients were extracted and annotated. Four diagnostic methods, with and without using super-resolution images and with and without using 3-dimensional shape data, were performed and compared. The super-resolution + 3-dimensional shape data method had the best diagnostic ability for the combination of sensitivity, negative predictive value, and accuracy (0.964, 0.966, and 0.968, respectively), whereas the super-resolution only method had the best diagnostic ability for the combination of specificity and positive predictive value (0.994 and 0.993, respectively).

LIMITATIONS

Small number of patients at a single center and the lack of external validation.

CONCLUSIONS

Our results enlightened the potential of artificial intelligence for the method to become another game changer in the diagnosis and treatment of low rectal cancer. See Video Abstract .

DIAGNSTICO POR IMGENES CON INTELIGENCIA ARTIFICIAL MEDIANTE SUPERRESOLUCIN Y FORMA D PARA LA METSTASIS EN LOS GANGLIOS LINFTICOS DEL CNCER DE RECTO BAJO UN ESTUDIO PILOTO DE UN SOLO CENTRO

ANTECEDENTES:Aunque el diagnóstico preoperatorio preciso de metástasis en los ganglios linfáticos es esencial para optimizar las estrategias de tratamiento para el cáncer de recto bajo, la precisión de las modalidades de diagnóstico actuales tiene margen de mejora.OBJETIVO:Establecer un método de diagnóstico de alta precisión para las metástasis en los ganglios linfáticos del cáncer de recto bajo utilizando inteligencia artificial.DISEÑO:Un estudio observacional retrospectivo.AJUSTE:Un único centro oncológico y una facultad de ingeniería en Japón.PACIENTES:En el presente estudio se incluyeron pacientes con adenocarcinoma rectal bajo sometidos a proctectomía, disección bilateral de ganglios linfáticos pélvicos laterales y tomografía computarizada con múltiples detectores con contraste (corte ≤1 mm) entre julio de 2015 y agosto de 2021. Se resecaron todos los ganglios linfáticos pélvicos desde la bifurcación aórtica hasta el borde superior del canal anal, independientemente de si estaban dentro o más allá del área de escisión mesentérica total, y se registraron los diagnósticos patológicos para entrenamiento y validación.PRINCIPALES MEDIDAS DE RESULTADO:Sensibilidad, especificidad, valor predictivo positivo, valor predictivo negativo y precisión.RESULTADOS:Se extrajeron y registraron un total de 596 ganglios patológicamente negativos y 43 positivos de 52 pacientes. Se realizaron y compararon cuatro métodos de diagnóstico, con y sin imágenes de súper resolución y sin datos de imagen en 3D. El método de superresolución + datos de imagen en 3D tuvo la mejor capacidad de diagnóstico para la combinación de sensibilidad, valor predictivo negativo y precisión (0,964, 0,966 y 0,968, respectivamente), mientras que el método de súper resolución solo tuvo la mejor capacidad de diagnóstico para la combinación de especificidad y valor predictivo positivo (0,994 y 0,993, respectivamente).LIMITACIONES:Pequeño número de pacientes en un solo centro y falta de validación externa.CONCLUSIONES:Nuestros resultados iluminan el potencial de la inteligencia artificial para que el método se convierta en otro elemento de cambio en el diagnóstico y tratamiento del cáncer de recto bajo. (Traducción ---Dr. Fidel Ruiz Healy ).

A Prognostic Model Based on the Log Odds Ratio of Positive Lymph Nodes Predicts Prognosis of Patients with Rectal Cancer

OBJECTIVE

This study aimed to compare the prognostic value of rectal cancer by comparing different lymph node staging systems, and a nomogram was constructed based on superior lymph node staging.

METHODS

Overall, 8700 patients with rectal cancer was obtained from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2015. The area under the curve (AUC), the C index, and the Akaike informativeness criteria (AIC) were used to examine the predict ability of various lymph node staging methods. Prognostic indicators were assessed using univariate and multivariate COX regression, and further correlation nomograms were created after the data were randomly split into training and validation cohorts. To evaluate the effectiveness of the model, the C index, calibration curves, decision curves (DCA), and receiver operating characteristic curve (ROC) were used. We ran Kaplan-Meier survival analyses to look for variations in risk classification.

RESULTS

While compared to the N-stage positive lymph node ratio (LNR), the log odds ratio of positive lymph nodes (LODDS) had the highest predictive effectiveness. Multifactorial COX regression analyses were used to create nomograms for overall survival (OS) and cancer-specific survival (CSS). The C indices of OS and CSS for this model were considerably higher than those for TNM staging in the training cohort. The created nomograms demonstrated good efficacy based on ROC, rectification, and decision curves. Kaplan-Meier survival analysis revealed notable variations in patient survival across various patient strata.

CONCLUSIONS

Compared to AJCC staging, the LODDS-based nomograms have a more accurate predictive effectiveness in predicting OS and CSS in patients with rectal 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.

Can physician judgment enhance model trustworthiness? A case study on predicting pathological lymph nodes in rectal cancer

Explainability is key to enhancing the trustworthiness of artificial intelligence in medicine. However, there exists a significant gap between physicians' expectations for model explainability and the actual behavior of these models. This gap arises from the absence of a consensus on a physician-centered evaluation framework, which is needed to quantitatively assess the practical benefits that effective explainability should offer practitioners. Here, we hypothesize that superior attention maps, as a mechanism of model explanation, should align with the information that physicians focus on, potentially reducing prediction uncertainty and increasing model reliability. We employed a multimodal transformer to predict lymph node metastasis of rectal cancer using clinical data and magnetic resonance imaging. We explored how well attention maps, visualized through a state-of-the-art technique, can achieve agreement with physician understanding. Subsequently, we compared two distinct approaches for estimating uncertainty: a standalone estimation using only the variance of prediction probability, and a human-in-the-loop estimation that considers both the variance of prediction probability and the quantified agreement. Our findings revealed no significant advantage of the human-in-the-loop approach over the standalone one. In conclusion, this case study did not confirm the anticipated benefit of the explanation in enhancing model reliability. Superficial explanations could do more harm than good by misleading physicians into relying on uncertain predictions, suggesting that the current state of attention mechanisms should not be overestimated in the context of model explainability.

Diagnostic performance of Node-RADS score for mesorectal lymph node metastasis in rectal cancer

PURPOSE

To explore the diagnostic performance of the Node-RADS scoring system on preoperative assessment of mesorectal lymph node metastasis (LNM) status in rectal cancer, in comparison with the ESGAR category and size of lymph node (LN).

METHODS

Preoperative clinical and MRI data of 154 rectal adenocarcinoma patients treated with radical resection surgery were retrospectively analyzed. The differences in the clinical, pathological and imaging characteristics between the pN- and pN + groups were surveyed. The correlations of Node-RADS score and ESGAR category to pN stage, LNM number and lymph node ratio (LNR) were investigated. The performances on assessing pathological LNM were compared among individual approaches. A nomogram combined the imaging and clinical features was also established and evaluated.

RESULTS

Significant differences in CEA, tumor maximum diameter, tumor location, LN short-axis diameter, Node-RADS score and ESGAR category were found between the pN- and pN + groups. Node-RADS correlated significantly with pN stage, LNM number, and LNR (r = 0.665, 0.685, and 0.675, p < 0.001). Node-RADS had the highest AUC (0.862) for predicting pN + status, surpassing ESGAR (AUC = 0.797, p = 0.040) and LN size (AUC = 0.762, p = 0.015). The nomogram had the best diagnostic performance (AUC = 0.901), significantly outperforming Node-RADS alone (p = 0.037).

CONCLUSIONS

The Node-RADS scoring system is comparable to the ESGAR category and surpasses short-axis diameter in preoperatively predicting LNM in rectal cancer. Integrating imaging and clinical features will lead to an enhancement in diagnostic performance. Moreover, a clear relationship was demonstrated between the Node-RADS score and the quantity-dependent pathological characteristics of LNM.

Imaging and Metabolic Diagnostic Methods in the Stage Assessment of Rectal Cancer

Rectal cancer (RC) is a prevalent malignancy with significant morbidity and mortality rates. The accurate staging of RC is crucial for optimal treatment planning and patient outcomes. This review aims to summarize the current literature on imaging and metabolic diagnostic methods used in the stage assessment of RC. Various imaging modalities play a pivotal role in the initial evaluation and staging of RC. These include magnetic resonance imaging (MRI), computed tomography (CT), and endorectal ultrasound (ERUS). MRI has emerged as the gold standard for local staging due to its superior soft tissue resolution and ability to assess tumor invasion depth, lymph node involvement, and the presence of extramural vascular invasion. CT imaging provides valuable information about distant metastases and helps determine the feasibility of surgical resection. ERUS aids in assessing tumor depth, perirectal lymph nodes, and sphincter involvement. Understanding the strengths and limitations of each diagnostic modality is essential for accurate staging and treatment decisions in RC. Furthermore, the integration of multiple imaging and metabolic methods, such as PET/CT or PET/MRI, can enhance diagnostic accuracy and provide valuable prognostic information. Thus, a literature review was conducted to investigate and assess the effectiveness and accuracy of diagnostic methods, both imaging and metabolic, in the stage assessment of RC.

T2WI-based texture analysis predicts preoperative lymph node metastasis of rectal cancer

BACKGROUND

To prospectively develop and validate the T2WI texture analysis model based on a node-by-node comparison for improving the diagnostic accuracy of lymph node metastasis (LNM) in rectal cancer.

METHODS

A total of 381 histopathologically confirmed lymph nodes (LNs) were collected. LNs texture features were extracted from MRI-T2WI. Spearman's rank correlation coefficient and the least absolute shrinkage and selection operator were used for feature selection to construct the LN rad-score. Then the clinical risk factors and LN texture features were combined to establish combined predictive model. Model performance was assessed by the area under the receiver operating characteristic (ROC) curve (AUC). Decision curve analysis (DCA) and nomogram were used to evaluate the clinical application of the model.

RESULTS

A total of 107 texture features were extracted from LN-MRI images. After selection and dimensionality reduction, the radiomics prediction model consisting of 8 texture features showed well-predictive performance in the training and validation cohorts (AUC, 0.676; 95% CI 0.582-0.771) (AUC, 0.774; 95% CI 0.648-0.899). A clinical-radiomics prediction model with the best performance was created by combining clinical and radiomics features, 0.818 (95% CI 0.742-0.893) for the training and 0.922 (95% CI 0.863-0.980) for the validation cohort. The LN Rad-score in clinical-radiomics nomogram obtained the highest classification contribution and was well calibrated. DCA demonstrated the superiority of the clinical-radiomics model.

CONCLUSION

The lymph node T2WI-based texture features can help to improve the preoperative prediction of LNM.

Magnetic resonance imaging-based lymph node radiomics for predicting the metastasis of evaluable lymph nodes in rectal cancer

BACKGROUND

Lymph node (LN) staging in rectal cancer (RC) affects treatment decisions and patient prognosis. For radiologists, the traditional preoperative assessment of LN metastasis (LNM) using magnetic resonance imaging (MRI) poses a challenge.

AIM

To explore the value of a nomogram model that combines Conventional MRI and radiomics features from the LNs of RC in assessing the preoperative metastasis of evaluable LNs.

METHODS

In this retrospective study, 270 LNs (158 nonmetastatic, 112 metastatic) were randomly split into training (n = 189) and validation sets (n = 81). LNs were classified based on pathology-MRI matching. Conventional MRI features [size, shape, margin, T2-weighted imaging (T2WI) appearance, and CE-T1-weighted imaging (T1WI) enhancement] were evaluated. Three radiomics models used 3D features from T1WI and T2WI images. Additionally, a nomogram model combining conventional MRI and radiomics features was developed. The model used univariate analysis and multivariable logistic regression. Evaluation employed the receiver operating characteristic curve, with DeLong test for comparing diagnostic performance. Nomogram performance was assessed using calibration and decision curve analysis.

RESULTS

The nomogram model outperformed conventional MRI and single radiomics models in evaluating LNM. In the training set, the nomogram model achieved an area under the curve (AUC) of 0.92, which was significantly higher than the AUCs of 0.82 (P < 0.001) and 0.89 (P < 0.001) of the conventional MRI and radiomics models, respectively. In the validation set, the nomogram model achieved an AUC of 0.91, significantly surpassing 0.80 (P < 0.001) and 0.86 (P < 0.001), respectively.

CONCLUSION

The nomogram model showed the best performance in predicting metastasis of evaluable LNs.

Nodal staging with MRI after neoadjuvant chemo-radiotherapy for locally advanced rectal cancer: a fast and reliable method

OBJECTIVES

In patients with locally advanced rectal carcinoma (LARC), negative nodal status after neoadjuvant chemoradiotherapy (nCRT) may allow for rectum-sparing protocols rather than total mesorectal excision; however, current MRI criteria for nodal staging have suboptimal accuracy. The aim of this study was to compare the diagnostic accuracy of different MRI dimensional criteria for nodal staging after nCRT in patients with LARC.

MATERIALS AND METHODS

Patients who underwent MRI after nCRT for LARC followed by surgery were retrospectively included and divided into a training and a validation cohort of 100 and 39 patients, respectively. Short-, long-, and cranial-caudal axes and volume of the largest mesorectal node and nodal status based on European Society of Gastrointestinal Radiology consensus guidelines (i.e., ESGAR method) were assessed by two radiologists independently. Inter-reader agreement was assessed in the training cohort. Histopathology was the reference standard. ROC curves and the best cut-off were calculated, and accuracies compared with the McNemar test.

RESULTS

The study population included 139 patients (median age 62 years [IQR 55-72], 94 men). Inter-reader agreement was high for long axis (κ = 0.81), volume (κ = 0.85), and ESGAR method (κ = 0.88) and low for short axis (κ = 0.11). Accuracy was similar (p > 0.05) for long axis, volume, and ESGAR method both in the training (71%, 74%, and 65%, respectively) and in the validation (83%, 78%, and 75%, respectively) cohorts.

CONCLUSION

Accuracy of the measurement of long axis and volume of the largest lymph node is not inferior to the ESGAR method for nodal staging after nCRT in LARC.

CLINICAL RELEVANCE STATEMENT

In MRI restaging of rectal cancer, measurement of the long axis or volume of largest mesorectal lymph node after preoperative chemoradiotherapy is a faster and reliable alternative to ESGAR criteria for nodal staging.

KEY POINTS

• Current MRI criteria for nodal staging in locally advanced rectal cancer after chemo-radiotherapy have suboptimal accuracy and are time-consuming. • Measurement of long axis or volume of the largest mesorectal lymph node on MRI showed good accuracy for assessment of loco-regional nodal status in locally advanced rectal cancer. • MRI measurement of the long axis and volume of largest mesorectal lymph node after chemo-radiotherapy could be a faster and reliable alternative to ESGAR criteria for nodal staging.

Magnetic resonance imaging for assessment of rectal cancer nodes after chemoradiotherapy: A single center experience

BACKGROUND

Accurate nodal restaging is becoming clinically more important in patients with locally advanced rectal cancer (LARC) with the emergence of organ-preserving treatment after a good response to neoadjuvant chemoradiotherapy (nCRT).

PURPOSE

To evaluate the accuracy of MRI in identifying negative N status (ypN0 patients) in LARC after nCRT.

MATERIAL AND METHODS

191 patients with LARC underwent MRI before and 6-8 weeks after nCRT and subsequent total mesorectal excision. Short-axis diameter of mesorectal lymph nodes was evaluated on the high resolution T2-weighted images to compare MRI restaging with histopathology..

RESULTS

146 and 45 patients had a negative N status (ypN0) and positive N status (ypN + ), respectively. On restaging MRI, the 70 % reduction in size of the largest node was associated with an area under the curve (AUC) of 0.818 to predict ypN0 stage, with a sensitivity of 93.3 % and a negative predictive value (NPV) of 95.4 %. No nodes were observed in 38 pts (37 pts ypN0 and 1 patient ypN + ), with sensitivity and NPV of nodes disappearance for ypN0 stage of 93.3 % and 92.5 % respectively. A 2.2 mm cut-off in short-axis diameter was associated with an AUC of 0.83 for the prediction of ypN0 nodal stage, with sensitivity and NPV of 79,5% and 91.1 % respectively.

CONCLUSION

A reduction in size of 70 % of the largest limph-node on MRI at rectal cancer restaging has high sensitivity and NPV for prediction of ypN0 stage after nCRT. The high NPV of node disappearance and of a ≤ 2.2 mm short-axis diameter is confirmed.

Extranodal Extension at Pretreatment MRI and the Prognostic Value for Patients with Rectal Cancer

Background Detection of extranodal extension (ENE) at pathology is a poor prognostic indicator for rectal cancer, but whether ENE can be identified at pretreatment MRI is, to the knowledge of the authors, unknown. Purpose To evaluate the performance of pretreatment MRI in detecting ENE using a matched pathologic reference standard and to assess its prognostic value in patients with rectal cancer. Materials and Methods This single-center study included a prospective development data set consisting of participants with rectal adenocarcinoma who underwent pretreatment MRI and radical surgery (December 2021 to January 2023). MRI characteristics were identified by their association with ENE-positive nodes (χ2 test and multivariable logistic regression) and the performance of these MRI features was assessed (area under the receiver operating characteristic curve [AUC]). Interobserver agreement was assessed by Cohen κ coefficient. The prognostic value of ENE detected with MRI for predicting 3-year disease-free survival was assessed by Cox regression analysis in a retrospective independent validation cohort of patients with locally advanced rectal cancer (December 2019 to July 2020). Results The development data set included 147 participants (mean age, 62 years ± 11 [SD]; 87 male participants). The retrospective cohort included 110 patients (mean age, 60 years ± 9; 79 male participants). Presence of vessel interruption and fusion (both P < .001), heterogeneous internal structure, and the broken-ring and tail signs (odds ratio range, 4.10-23.20; P value range, <.001 to .002) were predictors of ENE at MRI, and together achieved an AUC of 0.91 (95% CI: 0.88, 0.93) in detecting ENE. Interobserver agreement was moderate for the presence of vessel interruption and fusion (κ = 0.46 for both) and substantial for others (κ = 0.61-0.67). The presence of ENE at pretreatment MRI was independently associated with worse 3-year disease-free survival (hazard ratio, 3.00; P = .02). Conclusion ENE can be detected at pretreatment MRI, and its presence was associated with worse prognosis for patients with rectal cancer. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Eberhardt in this issue.

Exploring Potential Epigenetic Biomarkers for Colorectal Cancer Metastasis

Metastatic progression is a complex, multistep process and the leading cause of cancer mortality. There is growing evidence that emphasises the significance of epigenetic modification, specifically DNA methylation and histone modifications, in influencing colorectal (CRC) metastasis. Epigenetic modifications influence the expression of genes involved in various cellular processes, including the pathways associated with metastasis. These modifications could contribute to metastatic progression by enhancing oncogenes and silencing tumour suppressor genes. Moreover, specific epigenetic alterations enable cancer cells to acquire invasive and metastatic characteristics by altering cell adhesion, migration, and invasion-related pathways. Exploring the involvement of DNA methylation and histone modification is crucial for identifying biomarkers that impact cancer prediction for metastasis in CRC. This review provides a summary of the potential epigenetic biomarkers associated with metastasis in CRC, particularly DNA methylation and histone modifications, and examines the pathways associated with these biomarkers.

Review on Lymph Node Metastases, Sentinel Lymph Node Biopsy, and Lymphadenectomy in Sarcoma

Soft tissue sarcomas (STS) originating from connective tissue rarely affect the lymph nodes. However, involvement of lymph nodes in STS is an important aspect of prognosis and treatment. Currently, there is no consensus on the diagnosis and management of lymph node metastases in STS. The key risk factor for nodal involvement is the histological subtype of sarcoma. Radiological and pathological evaluation seems to be the most effective method of assessing lymph nodes in these neoplasms. Thus, sentinel lymph node biopsy (SLNB), which has been shown to be valuable in the management of melanoma or breast cancer, may also be a beneficial diagnostic option in some high-risk STS subtypes. This review summarizes data on the risk factors and clinical characteristics of lymph node involvement in STS. Possible management and therapeutic options are also discussed.

Organ sparing to cure stage IV rectal cancer: A case report and review of literature

BACKGROUND

Rectal sparing is an option for some rectal cancers with complete or good response after chemoradiotherapy (CRT); however, it has never been evaluated in patients with metastases. We assessed long-term outcomes of a rectal-sparing approach in a liver-first strategy for patients with rectal cancer with resectable liver metastases.

CASE SUMMARY

We examined patients who underwent an organ-sparing approach for rectal cancer with synchronous liver metastases using a liver-first strategy during 2010-2015 (n = 8). Patients received primary chemotherapy and pelvic CRT. Liver surgery was performed during the interval between CRT completion and rectal tumor re-evaluation. Clinical and oncological characteristics and long-term outcomes were assessed.All patients underwent liver metastatic resection with curative intent. The R0 rate was 100%. Six and two patients underwent local excision and a watch-and-wait (WW) approach, respectively. All patients had T3N1 tumors at diagnosis and had good clinical response after CRT. The median survival time was 60 (range, 14-127) mo. Three patients were disease free for 5, 8, and 10 years after the procedure. Five patients developed metastatic recurrence in the liver (n = 5) and/or lungs (n = 2). Only one patient developed local recurrence concurrent with metastatic recurrence 24 mo after the WW approach. Two patients died during follow-up.

CONCLUSION

The results suggest good local control in patients undergoing organ-sparing strategies for rectal cancer with synchronous liver metastasis. Prospective trials are required to validate these data and identify good candidates for these strategies.

Development and Validation of a Radiomics Model Based on Lymph-Node Regression Grading After Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer

PURPOSE

The response to neoadjuvant chemoradiotherapy (nCRT) varies among patients with locally advanced rectal cancer (LARC), and the treatment response of lymph nodes (LNs) to nCRT is critical in implementing a watch-and-wait strategy. A robust predictive model may help personalize treatment plans to increase the chance that patients achieve a complete response. This study investigated whether radiomics features based on prenCRT magnetic resonance imaging nodes could predict treatment response in preoperative LARC LNs.

METHODS AND MATERIALS

The study included 78 patients with clinical stage T3-T4, N1-2, and M0 rectal adenocarcinoma who received long-course neoadjuvant radiotherapy before surgery. Pathologists evaluated 243 LNs, of which 173 and 70 were assigned to training and validation cohorts, respectively. For each LN, 3641 radiomics features were extracted from the region of interest in high-resolution T2WI magnetic resonance imaging before nCRT. The least absolute shrinkage and selection operator regression model was used for feature selection and radiomics signature building. A prediction model based on multivariate logistic analysis, combining radiomics signature and selected LN morphologic characteristics, was developed and visualized by drawing a nomogram. The model's performance was assessed by receiver operating characteristic curve analysis and calibration curves.

RESULTS

The radiomics signature consists of 5 selected features that were effectively discriminated within the training cohort (area under the curve [AUC], 0.908; 95% CI, 0.857%-0.958%) and the validation cohort (AUC, 0.865; 95% CI, 0.757%-0.973%). The nomogram, which consisted of radiomics signature and LN morphologic characteristics (short-axis diameter and border contours), showed better calibration and discrimination in the training and validation cohorts (AUC, 0.925; 95% CI, 0.880%-0.969% and AUC, 0.918; 95% CI, 0.854%-0.983%, respectively). The decision curve analysis confirmed that the nomogram had the highest clinical utility.

CONCLUSIONS

The nodal-based radiomics model effectively predicts LNs treatment response in patients with LARC after nCRT, which could help personalize treatment plans and guide the implementation of the watch-and-wait approach in these patients.

Use of a radiomics-clinical model based on magnetic diffusion-weighted imaging for preoperative prediction of lymph node metastasis in rectal cancer patients

Rectal cancer is the eighth most prevalent malignancy worldwide with a 3.2% mortality rate and 3.9% incidence rate. Radiologists still have difficulty in correctly diagnosing lymph node metastases that have been suspected preoperatively. To assess the effectiveness of a model combining clinical and radiomics features for the preoperative prediction of lymph node metastasis in rectal cancer. We retrospectively analyzed data from 104 patients with rectal cancer. All patients were selected as samples for the training (n = 72) and validation cohorts (n = 32). Lymph nodes (LNs) in diffusion-weighted images were analyzed to obtain 842 radiomic characteristics, which were then used to draw the region of interest. Logistic regression, least absolute shrinkage and selection operator, and between-group and within-group correlation analyses were combined to establish the radiomic score (rad-score). Receiver operating characteristic curves were used to estimate the prediction accuracy of the model. A calibration curve was constructed to test the predictive ability of the model. A decision curve analysis was performed to analyze the model's value in clinical application. The area under the curve for the radiomics-clinical, clinical, and radiomics models was 0.856, 0.810, and 0.781, respectively, in the training cohort and 0.880, 0.849, and 0.827, respectively, in the validation cohort. The calibration curve and DCA showed that the radiomics-clinical prediction model had good prediction accuracy, which was higher than that of the other models. The radiomics-clinical model showed a favorable predictive performance for the preoperative prediction of LN metastasis in patients with rectal cancer.

Utility of Machine Learning Algorithms in Predicting Preoperative Lymph Node Metastasis in Patients With Rectal Cancer Based on Three-Dimensional Endorectal Ultrasound and Clinical and Laboratory Data

BACKGROUND

We aimed to investigate the value of a machine learning (ML) algorithm in the preoperative prediction of lymph node metastasis in patients with rectal cancer.

METHODS

Based on the histopathological results, 126 rectal cancer patients were divided into two groups: lymph node metastasis-positive and metastasis-negative groups. We collected clinical and laboratory data, three-dimensional endorectal ultrasound (3D-ERUS) findings, and parameters of the tumor for between-group comparisons. We constructed a clinical prediction model based on the ML algorithm, which demonstrated the best diagnostic performance. Finally, we analyzed the diagnostic results and processes of the ML model.

RESULTS

Between the two groups, there were significant differences in serum carcinoembryonic antigen (CEA) levels, tumor length, tumor breadth, circumferential extent of the tumor, resistance index (RI), and ultrasound T-stage (P < 0.05). The extreme gradient boosting (XGBoost) model had the best comprehensive diagnostic performance for predicting lymph node metastasis in patients with rectal cancer. Compared with experienced radiologists, the XGBoost model showed significantly higher diagnostic value in predicting lymph node metastasis; the area under curve (AUC) value of the receiver operating characteristic (ROC) curve of the XGBoost model and experienced radiologists was 0.82 and 0.60, respectively.

CONCLUSIONS

Preoperative predictive utility in lymph node metastasis was demonstrated by the XGBoost model based on the 3D-ERUS finding and related clinical information. This could be useful in guiding clinical decisions on the selection of different treatment strategies.

MRI Evaluation of Rectal Cancer Lymph Node Staging Using Apparent Diffusion Coefficient

Introduction Colorectal cancer is the third most diagnosed cancer globally. Lymph node metastases significantly affect prognosis, emphasizing the importance of early detection and management. Despite significant advances in conventional MRI's role in staging, improvements in advanced functional imaging such as diffusion-weighted imaging (DWI) in identifying lymph node metastases persist. Objectives The aim is to evaluate the effectiveness of apparent diffusion coefficient (ADC) MRI in evaluating lymph node staging in rectal cancer. Patients and methods In a prospective study, 89 patients with stage II-III rectal cancer were grouped into two treatments: pre-operative FOLFOX4 chemotherapy and standard pre-operative chemoradiotherapy. All underwent 1.5T MRI, with T2-weighted and DWI sequences. A radiologist defined regions of interest on the tumor, lymph nodes, and intact rectal wall to calculate ADC values. Results Rectal cancer ADC's receiver operating characteristic curve had an area under the curve (AUC) of 0.688 (P < 0.001), with optimal ADC cutoff at 0.99 x 10-3 mm2/s (sensitivity: 75%, specificity: 83%). For lymph nodes, AUC was 0.508 (P < 0.001), with a cutoff of 0.9 x 10-3 mm2/s (sensitivity: 78%, specificity: 67%). No correlation between tumor and lymph node ADC values was observed. In chemotherapy patients, "healthy" inguinal lymph nodes had higher ADC values than affected ones pre-treatment (P = 0.001), a disparity fading post-treatment (P = 0.313). For chemoradiotherapy patients, the ADC difference persisted pre and post-treatment (P = 0.001). Conclusion The study of ADC-MRI showed different ADC values between tumors and lymph nodes and highlighted ADC differences between treatment groups. Notably, no correlation was observed between tumor and lymph node ADC values. However, differences were apparent when comparing "healthy" inguinal nodes with lymph nodes affected by cancer.

Predictive value of MRI-detected tumor deposits in locally advanced rectal cancer

Background

Although tumor deposits (TDs) are not the same as lymph nodes, the prognosis of patients with TDs is similar or worse than that of patients with metastatic lymph nodes. TDs are mostly assessed by the histology of samples after surgery, thus, not helpful for preoperative treatment strategies. The primary objective of this study was to detect TDs by MRI and evaluate its predictive value.

Materials and methods

A total of 114 patients with rectal cancer were retrospectively analyzed. Clinicopathological and MRI data mainly including MRI- detected TDs (mTDs), tumor border configuration (TBC) on MRI, MRI-detected extramural vascular invasion (mEMVI), MRI-detected lymph node metastasis (mLN), MRI T stage, MRI N stage, the range of rectal wall involved by the tumor, peritoneal reflection invasion, tumor length, tumor location, cord sign at the tumor edge, nodular protrusion at the tumor edge, maximal extramural depth and pathology-proven lymph node involvement (pLN) were evaluated. The correlation of MRI factors with postoperative distant metastasis (PDM) and pLN were analyzed by univariate analysis and multivariate logistic regression analysis, and nomograms were established based on the latter. The diagnostic efficiency was evaluated by the receiver operating characteristic curve (ROC) and area under the curve (AUC).

Results

A total of 38 cases of pLN, 13 of PDM and 17 of pathology-proven TDs (pTDs) were found. Ten cases of PDM and 22 cases of pLN in 30 mTDs cases were also found. Chi-square test showed that mTDs, mLN, TBC, mEMVI, MRI T stage, nodular protrusion, cord sign, maximal extramural depth and peritoneal reflection invasion were correlated with PDM and pLN (P<0.05). mTDs and peritoneal reflection invasion were independent risk factors for PDM (odds ratio: 10.15 and 8.77, P<0.05), mTDs and mLN were independent risk factors for pLN (odds ratio: 5.50 and 5.91, P<0.05), and Hosmer-Lemeshow test showed that the results of two models were not statistically significant, suggesting that the fit was good. On this basis, two nomograms for predicting PDM and pLN were confirmed by Bootstrap self-sampling, and the C-indices of the two nomograms were 0.837 and 0.817, respectively. The calibration curves and ROC curves of the two nomograms showed that the correlation between the predicted and the actual incidence of PDM and pLN was good. The DeLong test showed that the predictive efficiency of the nomogram in predicting pLN was better than that of mLN (P=0.0129).

Conclusion

mTDs are a risk factor for PDM and lymph node metastasis. The two nomograms based on mTDs showed a good performance in predicting PDM and lymph node metastasis, possessing a certain clinical value.

Prediction of lymph node metastasis in stage T1-2 rectal cancers with MRI-based deep learning

OBJECTIVES

This study aimed to investigate whether a deep learning (DL) model based on preoperative MR images of primary tumors can predict lymph node metastasis (LNM) in patients with stage T1-2 rectal cancer.

METHODS

In this retrospective study, patients with stage T1-2 rectal cancer who underwent preoperative MRI between October 2013 and March 2021 were included and assigned to the training, validation, and test sets. Four two-dimensional and three-dimensional (3D) residual networks (ResNet18, ResNet50, ResNet101, and ResNet152) were trained and tested on T2-weighted images to identify patients with LNM. Three radiologists independently assessed LN status on MRI, and diagnostic outcomes were compared with the DL model. Predictive performance was assessed with AUC and compared using the Delong method.

RESULTS

In total, 611 patients were evaluated (444 training, 81 validation, and 86 test). The AUCs of the eight DL models ranged from 0.80 (95% confidence interval [CI]: 0.75, 0.85) to 0.89 (95% CI: 0.85, 0.92) in the training set and from 0.77 (95% CI: 0.62, 0.92) to 0.89 (95% CI: 0.76, 1.00) in the validation set. The ResNet101 model based on 3D network architecture achieved the best performance in predicting LNM in the test set, with an AUC of 0.79 (95% CI: 0.70, 0.89) that was significantly greater than that of the pooled readers (AUC, 0.54 [95% CI: 0.48, 0.60]; p < 0.001).

CONCLUSION

The DL model based on preoperative MR images of primary tumors outperformed radiologists in predicting LNM in patients with stage T1-2 rectal cancer.

KEY POINTS

• Deep learning (DL) models with different network frameworks showed different diagnostic performance for predicting lymph node metastasis (LNM) in patients with stage T1-2 rectal cancer. • The ResNet101 model based on 3D network architecture achieved the best performance in predicting LNM in the test set. • The DL model based on preoperative MR images outperformed radiologists in predicting LNM in patients with stage T1-2 rectal cancer.

Pathologic Implications of Magnetic Resonance Imaging-detected Extramural Venous Invasion of Rectal Cancer

BACKGROUND

Extramural venous invasion (EMVI) is a poor prognostic factor in rectal cancer. Recent advances in magnetic resonance imaging (MRI) allow for the detection of EMVI before surgery. This study aimed to analyze the correlations between MRI-detected EMVI (MR-EMVI) and pathologic parameters in patients with rectal cancer.

MATERIALS AND METHODS

This study retrospectively analyzed 721 patients who underwent radical resection for locally advanced rectal cancer between 2018 and 2019 at the Asan Medical center. All patients underwent an MRI before surgery. The lesions of patients who received neoadjuvant chemoradiation therapy (CRT) were evaluated by MRI before and after the neoadjuvant CRT.

RESULTS

Of the 721 patients, 118 (16.4%) showed a positive MR-EMVI, which significantly correlated with advanced pathologic T-category and N-category, extranodal extension, poor differentiation, lymphatic invasion, venous invasion, and perineural invasion. In addition, MR-EMVI was an independent factor for predicting the pathologic nodal status (OR 3.476, 95% CI, 2.186-5.527, P < .001). Patients with a positive MR-EMVI had a sensitivity of 28.0% and specificity of 91.9% for predicting regional lymph node metastasis, whereas the MR-N category had a sensitivity of 88.7% and specificity of 30.6%. Patients whose MR-EMVI changed from positive to negative after neoadjuvant CRT had no significant differences in pathologic parameters except for lymphatic invasion with patients who were negative before and after neoadjuvant CRT.

CONCLUSION

MR-EMVI correlated with aggressive pathologic features, which indicated a poor prognosis. MR-EMVI may be a complementary imaging biomarker for predicting nodal status and evaluating tumor response to neoadjuvant CRT.

Endoscopic ultrasound in patients with resectable perihilar cholangiocarcinoma: impact on clinical decision-making

Background and study aims  Accurate assessment of the lymph node (LN) status is crucial in resectable perihilar cholangiocarcinoma (pCCA) to prevent major surgery in patients with extraregional metastatic LNs (MLNs). This study investigates the added value of preoperative endoscopic ultrasound (EUS) with or without tissue acquisition (TA) for the detection of MLNs in patients with resectable pCCA. Patients and methods  In this retrospective, multicenter cohort study, patients with potentially resectable pCCA who underwent EUS preoperatively between 2010-2020, were included. The clinical impact of EUS-TA was defined as the percentage of patients who did not undergo surgical resection due to MLNs found with EUS-TA. Findings of cross-sectional imaging were compared with EUS-TA findings and surgery. Results  EUS was performed on 141 patients, of whom 107 (76 %) had suspicious LNs on cross-sectional imaging. Surgical exploration was prevented in 20 patients (14 %) because EUS-TA detected MLNs, of which 17 (85 %) were extraregional. Finally, 74 patients (52 %) underwent surgical exploration followed by complete resection in 40 (28 %). MLNs were identified at definitive pathology in 24 (33 %) patients, of which 9 (38 %) were extraregional and 15 (63 %) regional. Conclusions  EUS-TA may be of value in patients with potentially resectable pCCA based on preoperative cross-sectional imaging, regardless of lymphadenopathy at cross-sectional imaging. A prospective study in which a comprehensive EUS investigation with LN assessment and EUS-TA of LNs is performed routinely should confirm this promise.

Preoperative MR radiomics based on high-resolution T2-weighted images and amide proton transfer-weighted imaging for predicting lymph node metastasis in rectal adenocarcinoma

OBJECTIVES

Lymph node (LN) metastasis is an important prognostic factor in rectal cancer (RC). However, accurate identification of LN metastasis can be challenged for radiologists. The aim of our study was to assess the utility of MRI radiomics based on T2-weighted images (T2WI) and amide proton transfer-weighted (APTw) images for predicting LN metastasis in RC preoperatively.

METHODS

A total of 125 patients with pathologically confirmed rectal adenocarcinoma (RA) from January 2019 to June 2021 who underwent preoperative MR were enrolled in this retrospective study. Radiomics features were extracted from high-resolution T2WI and APTw images of primary tumor. The most relevant radiomics and clinical features were selected using correlation and multivariate logistic analysis. Radiomics models were built using five machine learning algorithms including support vector machine (SVM), logical regression (LR), k- nearest neighbor (KNN), naive bayes (NB), and random forest (RF). The best algorithm was selected for further establish the clinical- radiomics model. The receiver operating characteristic curve (ROC) analysis was used to assess the performance of radiomics and clinical-radiomics model for predicting LN metastasis.

RESULTS

The LR classifier had the best prediction performance, with AUCs of 0.983 (95% CI 0.957-1.000), 0.864 (95% CI 0.729-0.972), 0.851 (95% CI 0.713-0.940) on the training set, validation, and test sets, respectively. In terms of prediction, the clinical-radiomics combined model outperformed the radiomics model. The AUCs of the clinical-radiomics combined model in the validation and test sets were 0.900 (95% CI 0.785-0.986), and 0.929 (95% CI 0.721-0.943), respectively.

CONCLUSION

The radiomics model based on high-resolution T2WI and APTw images can predict LN metastasis accurately in patients with RA.

Detection of magnetomotive ultrasound signals from human tissue

Rectal cancer is a common cancer, with presently a 5-year survival of 67 %. Treatment is based on tumor stage, but current staging methods, such as magnetic resonance imaging (MRI) or ultrasound, are limited in the ability to correctly stage the disease. Magnetomotive ultrasound is a developing modality that has a potential to improve rectal cancer staging. Magnetic nanoparticles are set in motion by an external magnetic field, and the resulting motion signature is detected by ultrasound. Here, we report on magnetomotive images of magnetic nanoparticles in human tissue, using a prototype system where a rotating permanent magnet provides the varying magnetic field, and an ultrasound transducer array encircling the magnet, detects the induced motion. Prior to surgery, a patient with a low rectal tumor was injected at three sites close to the tumor with magnetic nanoparticles. Postsurgical magnetomotive ultrasound scanning revealed the three injection sites, with no obvious artefactual signals. A phantom study showed detection of nanoparticles beyond 40 mm, where 30 mm is the expected maximum distance to mesorectal lymph nodes. Magnetomotive ultrasound image of iron oxide nanoparticles in human tissue. Prior to surgery a patient was injected with nanoparticles, and the excised tissue specimen was imaged with a prototype magnetomotive ultrasound system. The three colored areas overlaid on the standard B-mode greyscale image, correspond to the three injection sites.

Role of PET/CT in the follow-up of postoperative and/or post-therapy cancer rectum: comparison with pelvic MRI

Background

In locally advanced rectal cancer, many imaging modalities are used, for example 18F-2-fluoro-2-deoxy-d-glucose (18F-FDG) positron emission tomography-computed tomography (PET-CT) and MRI. The aim of our study is to compare the diagnostic accuracy of 18 F-FDG-PET/CT & pelvic MRI; as well as to investigate the possible added value of using combined pelvic MRI and PET-CT for assessment of tumor response.

Results

Regarding the presence of local tumor, both PET CT and MRI showed perfect agreement with 97.1% overall accuracy, while in N category, PET CT showed higher specificity but lower sensitivity than MRI. MRI was superior to PET/CT in detecting extension to nearby organs; owing to the more anatomical details of MRI regarding the involvement of mesorectal fascia and EMVI. Almost total agreement of both MRI and PET/CT was noticed in evaluating post-therapy and postoperative complications.

Conclusion

For locally advanced rectal cancer (pT3–4 N0 M0 or any T N1 M0), a multimodality strategy has been shown to be the best option to evaluate local disease process, using the diagnostic criteria that were based on morphology, as well as glucose uptake, instead of the SUV alone for reassessment of post-therapy or postoperative changes.

Effect of lateral lymph nodes without malignant characteristics on the prognosis of patients with rectal cancer

Background: Lateral lymph node (LLN) metastasis is a poor prognostic factor for rectal cancer patients. However, the effect of LLNs without malignant characteristics on the prognosis of rectal cancer patients has been uncertain. Methods: Consecutive patients who underwent laparoscopic-assisted low anterior resection were included. Patients with MRI-detected LLNs, but without malignant characteristics, were compared with patients with no MRI-detected LLNs. Results: The local recurrence rate was higher in the LLN-present group than in the LLN-absent group (9.8% vs 2.5%; p = 0.056). The overall survival of patients with no MRI-detected LLNs was significantly better than that of patients with MRI-detected LLNs (p = 0.021). Conclusion: The presence of LLNs, even without malignant features, may lead to worse local control and overall survival.

Establishment and validation of nomograms for predicting mesorectal lymph node staging and restaging

BACKGROUND

Preoperative determination of lymph node (LN) status is crucial in treatment planning for rectal cancer. This study prospectively evaluated the risk factors for lymph node metastasis (LNM) at staging and restaging based on a node-by-node pairing between MRI imaging findings and histopathology and constructed nomograms to evaluate its diagnostic value.

METHODS

From July 2021 to July 2022, patients with histopathologically verified rectal cancer who underwent MRI before surgery were prospectively enrolled. Histological examination of each LN status in the surgical specimens and anatomical matching with preoperative imaging. Taking histopathological results as the gold standard, federating clinical features from patients and LN imaging features on MRI-T2WI. Risk factors for LN metastasis were identified by multivariate logistic regression analysis and used to create a nomogram. The performance of the nomograms was assessed with calibration plots and bootstrapped-concordance index and validated using validation cohorts.

RESULTS

A total of 500 target LNs in 120 patients were successfully matched with node-by-node comparisons. A total of 353 LNs did not receive neoadjuvant therapy and 147 LNs received neoadjuvant chemoradiotherapy (neoCRT). Characterization of LNs not receiving neoadjuvant therapy and multivariate regression showed that the short diameter, preoperative CEA level, mrT-stage, border contour, and signal intensity were associated with a high risk of LN metastasis (P < 0.05). The nomogram predicted that the area under the curve was 0.855 (95% CI, 0.794-0.916) and 0.854 (95% CI, 0.727-0.980) in the training and validation cohorts, respectively. In the neoadjuvant therapy group, short diameter, ymrT-stage, internal signal, and MRI-EMVI were associated with LN positivity (P < 0.05), and the area under the curves using the nomogram was 0.912 (95% CI, 0.856-0.968) and 0.915 (95% CI, 0.817-1.000) in two cohorts. The calibration curves demonstrate good agreement between the predicted and actual probabilities for both the training and validation cohorts.

CONCLUSION

Our nomograms combined with preoperative clinical and imaging biomarkers have the potential to improve the prediction of nodal involvement, which can be used as an essential reference for preoperative N staging and restaging of rectal cancer.

Technique to match mesorectal lymph nodes imaging findings to histopathology: node-by-node comparison

BACKGROUND

Lymph node status is critical for staging rectal cancer and determining neoadjuvant therapy regimens. Establishing a matching between imaging and histopathological lymph nodes is fundamental for predicting lymph node status. This study reports a technique to achieve node-by-node pairing of mesorectal lymph nodes between imaging findings and histopathology.

METHODS

Fifty-two patients with histopathologically verified rectal cancer underwent magnetic resonance imaging before surgery. The status of each lymph node in the surgical specimens was analyzed histopathologically and matched with preoperative imaging after the operation.

RESULTS

A total of 346 mesorectal lymph nodes were located on imaging evaluation, of which 313 were confirmed histopathologically, and 33 were unmatched. The total success rate of the technique was 90.5%. Node-by-node analysis revealed 280 benign and 33 malignant nodal structures.

CONCLUSION

The technique to match mesorectal lymph node imaging findings to histopathology was feasible and effective. It simplified the technical method and had a reasonable success matching rate, which could provide a standardized approach for obtaining a prospective correlation between imaging and histological findings, supporting all subsequent related studies at the level of mesorectal lymph nodes.

Lymph Nodes Evaluation in Rectal Cancer: Where Do We Stand and Future Perspective

The assessment of nodal involvement in patients with rectal cancer (RC) is fundamental in disease management. Magnetic Resonance Imaging (MRI) is routinely used for local and nodal staging of RC by using morphological criteria. The actual dimensional and morphological criteria for nodal assessment present several limitations in terms of sensitivity and specificity. For these reasons, several different techniques, such as Diffusion Weighted Imaging (DWI), Intravoxel Incoherent Motion (IVIM), Diffusion Kurtosis Imaging (DKI), and Dynamic Contrast Enhancement (DCE) in MRI have been introduced but still not fully validated. Positron Emission Tomography (PET)/CT plays a pivotal role in the assessment of LNs; more recently PET/MRI has been introduced. The advantages and limitations of these imaging modalities will be provided in this narrative review. The second part of the review includes experimental techniques, such as iron-oxide particles (SPIO), and dual-energy CT (DECT). Radiomics analysis is an active field of research, and the evidence about LNs in RC will be discussed. The review also discusses the different recommendations between the European and North American guidelines for the evaluation of LNs in RC, from anatomical considerations to structured reporting.