Accuracy of preoperative MRI in predicting pathology stage in rectal cancers: node-for-node matched histopathology validation of MRI features

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.

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 ).

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.

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.

Controversy in the initial nodal staging of rectal cancer (MRI or PET/CT?)

OBJECTIVE

To compare the usefulness of MRI and PET/CT in nodal staging (N) of patients with locally advanced rectal cancer (LARC).

MATERIAL AND METHODS

Retrospective study of patients with LARC, who completed their initial staging with PET/CT, between January-20 and March-23. Regional nodes were assessed, and N was determined using both techniques according to TNM criteria. Concordance between MRI and PET/CT was analyzed. The accuracy of both techniques was calculated for those patients who underwent direct surgery. Non-regional pelvic lymph nodes were evaluated by both modalities.

RESULTS

Among the 73 patients, 48 were ultimately diagnosed with a locally advanced stage. Of these, 39 underwent neoadjuvant treatment (chemoradiotherapy) followed by surgery, and 9 direct surgery. In 25, the PET/CT extension study revealed distant disease, leading to systemic treatment. Weak concordance was observed between MRI and PET/CT in determining N (k=0.286; p<0.005). Out of 73 patients, 31(42%) exhibited concordance, and 42(58%) showed discordance. In 83% of the discordant cases, MRI overstaged compared to PET/CT, with 17 cases indicating nodal involvement (N+) by MRI and N0 by PET/CT. Diagnostic accuracy was 78% for both techniques. Sensitivity, specificity, positive predictive value, and negative predictive value were 80%, 75%, 80%, and 75% for MRI, and 60%, 100%, 100%, and 67%, for PET/CT. PET/CT identified pelvic metastatic adenopathies in 8 patients that were not visible/doubtful by MRI.

CONCLUSIONS

In the initial nodal staging of rectal cancer MRI overstages relative to PET/CT. Both modalities are complementary, PET/CT offers higher specificity and MRI higher sensitivity.

Should the Baseline MRI Staging Criteria Differentiate Between Mucinous and Classical Rectal Adenocarcinoma?

RATIONALE AND OBJECTIVES

To compare baseline MR imaging features for pre-treatment staging between rectal mucinous adenocarcinoma (RMAC) and rectal classical adenocarcinoma (RCAC), and to investigate whether the subtype of mucinous carcinoma influences MRI evaluation criteria and high-risk tumors identifying.

METHODS

A total of 306 patients who underwent surgical rectal cancer resection were retrospectively reviewed in the study. MR imaging parameters of the primary tumor and lymph nodes (LNs) were compared between two subtypes. Logistic regression and receiver operating characteristic analyses were performed to test significant associations between LN imaging parameters and malignant LN status in RMAC and RCAC, respectively.

RESULTS

The length of mucinous tumors was larger than RCAC tumors in pT3 and pT4 stage. For pN0 patients, the long and short diameters of the largest LN on MRI were more likely to be larger in RCAC than RMAC. For pN+ patients, the proportion of LNs exhibiting internal heterogeneity in RMAC was obviously greater than that in RCAC. The best cut-off value of the largest short diameter of malignant LNs was 6.05 mm for RMAC and 8.05 mm for RCAC. And the highest AUC for predicting LNs metastases based on the largest short diameter was 0.794 for RMAC using 6 mm size cut-off, and 0.667 for RCAC using 8 mm cut-off.

CONCLUSION

The imaging features that were associated with LN metastases were different between RMAC and RCAC, and different size criteria of LNs was suggested to distinguish high-risk tumors. Clinicians should stay vigilant of LN status and take histologic subtypes into consideration before assigning clinical strategies.

Sequential Lateral Lymphatic Metastasis Shows Similar Oncologic Outcomes to Upward Spread in Advanced Rectal Cancer After Preoperative Chemoradiotherapy

BACKGROUND

Whether lateral pelvic node metastasis should be considered as a regional or systemic disease is a long-standing debate. Although previous Japanese studies have considered it to be locoregional disease, Western countries consider it a systemic disease and do not perform lateral pelvic node dissection after preoperative chemoradiotherapy.

OBJECTIVE

To evaluate whether lateral pelvic node metastasis is a systemic or regional disease that is amenable to curative resection.

DESIGN

Retrospective analysis of a prospectively collected database.

SETTING

This study was conducted at a tertiary cancer center.

PATIENTS

There were 616 consecutive patients who underwent curative total mesorectal excision alone or with lateral pelvic node dissection after preoperative chemoradiotherapy for locally advanced rectal cancer between 2011 and 2019.

MAIN OUTCOME MEASURES

Three-year disease-free and overall survival.

RESULTS

A total of 360 patients underwent total mesorectal excision, and 160 patients underwent total mesorectal excision with lateral pelvic node dissection. There was no difference in the 3-year disease-free survival (DFS; p = 0.844) or overall survival rates ( p = 0.921) between the groups. Patients with lateral pelvic node metastasis showed DFS similar to those with perirectal lymph node metastasis in the total mesorectal excision group. In a subgroup analysis, patients with internal iliac pelvic node metastasis showed a disease-free survival comparable to those with perirectal node involvement, and patients with other lateral pelvic node metastasis showed a DFS similar to those with intermediate node involvement. In the lateral pelvic node dissection group, the lateral pelvic node metastatic rate was 32.5%. On multivariate analysis, fewer than 8 of the unilateral harvested lateral pelvic nodes and advanced ypT stage were significantly associated with poor disease-free survival.

LIMITATION

The retrospective design.

CONCLUSIONS

Lateral lymphatic metastasis showed oncologic outcomes similar to those of upward spread, especially perirectal lymph nodes metastasis. Large cohort studies with long-term follow-up are required to confirm these results. See Video Abstract .

LAS METSTASIS LINFTICAS SECUENCIALES LATERALES MUESTRAN RESULTADOS ONCOLGICOS SIMILARES EN LA PROPAGACIN ASCENDENTE DEL CNCER RECTAL AVANZADO DESPUS DE LA RADIOQUIMIOTERAPIA PREOPERATORIA

ANTECEDENTES:Es un debate muy antiguo si las metástasis en los ganglios pélvicos laterales deben considerarse una enfermedad regional o sistémica. Si bien estudios japoneses anteriores las consideran como una enfermedad locorregional, en los países de occidente se las considera como una enfermedad sistémica por la cual no se realiza disección de ganglios pélvicos laterales después de una radioquimioterapia preoperatoria.OBJETIVOS:Evaluar si la metástasis en los ganglios pélvicos laterales se consideran como enfermedad sistémica o enfermedad regional susceptible de resección curativa.DISEÑO:Análisis retrospectivo de una base de datos recopilada prospectivamente.AJUSTE:Este estudio se realizó en un centro oncológico terciario.PACIENTES:616 pacientes consecutivos se sometieron a excisión total del mesorrecto curativa sola o con disección de los ganglios pélvicos laterales después de radioquimioterapia preoperatoria en casos de cáncer de recto localmente avanzado entre 2011 y 2019.PRINCIPALES MEDIDAS DE RESULTADO:Sobrevida global y libre de enfermedad a 3 años.RESULTADOS:Un total de 360 pacientes se sometieron a excisión total del mesorrecto y 160 pacientes se sometieron a excisión total del mesorrecto con disección de ganglios pélvicos laterales.No hubo diferencias en la sobrevida libre de enfermedad a 3 años (p = 0,844) ni en las tasas de sobrevida general (p = 0,921) entre los grupos. Los pacientes con metástasis en los ganglios pélvicos laterales mostraron una sobrevida libre de enfermedad similar a aquellos con metástasis en los ganglios linfáticos perirrectales que se encontraban en el grupo de excisión total del mesorrecto.En el análisis de subgrupos, los pacientes con metástasis en los ganglios pélvicos ilíacos internos mostraron una sobrevida libre de enfermedad comparable a aquellos con afección de los ganglios perirrectales y los pacientes con otras metástasis en los ganglios pélvicos laterales mostraron una sobrevida libre de enfermedad similar a aquellos con afección de los ganglios intermedios.En el grupo de disección de los ganglios pélvicos laterales, la tasa de metástasis en dichos ganglios fué del 32,5%. En el análisis multivariado, < de 8 ganglios pélvicos laterales resecados unilateralmente y el estadio ypT avanzado se asociaron significativamente con una menor sobrevida libre de enfermedad.LIMITACIÓN:El diseño retrospectivo del estudio.CONCLUSIONES:Las metástasis linfáticas laterales mostraron resultados oncológicos similares a la diseminación ascendente, especialmente las metástasis en los ganglios linfáticos perirrectales. Se requieren grandes estudios de cohortes con seguimiento a largo plazo para confirmar estos resultados. (Traducción-Dr. Xavier Delgadillo ).

Performance reporting design in artificial intelligence studies using image-based TNM staging and prognostic parameters in rectal cancer: a systematic review

PURPOSE

The integration of artificial intelligence (AI) and magnetic resonance imaging in rectal cancer has the potential to enhance diagnostic accuracy by identifying subtle patterns and aiding tumor delineation and lymph node assessment. According to our systematic review focusing on convolutional neural networks, AI-driven tumor staging and the prediction of treatment response facilitate tailored treat-ment strategies for patients with rectal cancer.

METHODS

This paper summarizes the current landscape of AI in the imaging field of rectal cancer, emphasizing the performance reporting design based on the quality of the dataset, model performance, and external validation.

RESULTS

AI-driven tumor segmentation has demonstrated promising results using various convolutional neural network models. AI-based predictions of staging and treatment response have exhibited potential as auxiliary tools for personalized treatment strategies. Some studies have indicated superior performance than conventional models in predicting microsatellite instability and KRAS status, offer-ing noninvasive and cost-effective alternatives for identifying genetic mutations.

CONCLUSION

Image-based AI studies for rectal can-cer have shown acceptable diagnostic performance but face several challenges, including limited dataset sizes with standardized data, the need for multicenter studies, and the absence of oncologic relevance and external validation for clinical implantation. Overcoming these pitfalls and hurdles is essential for the feasible integration of AI models in clinical settings for rectal cancer, warranting further research.

MRI-predicted extramural vascular invasion and tumour deposit are main predictors of disease-free survival in patients undergoing surgical resection for rectal cancer

BACKGROUND

MRI is crucial in staging patients with rectal cancer and planning treatment. The aim was to analyse the prognostic role of MRI-predicted tumour deposits and/or extramural vascular invasion (mrTD/EMVI) in a cohort of patients with rectal cancer undergoing surgical resection, with selective neoadjuvant chemoradiotherapy (nCRT).

METHOD

Retrospective analysis of a single-centre cohort of consecutive patients with rectal cancer undergoing low anterior resection or abdominoperineal excision between 2008 and 2020. Unit policy was selective nCRT for MRI-predicted threatened or involved circumferential resection margin (mrCRM), or radiologically involved pelvic sidewall nodes. The primary outcome was disease-free survival. Secondary outcomes were rates of local recurrence, distant recurrence and overall survival.

RESULTS

A total of 314 patients were analysed. Median age was 65 years (female/male: 114/200). A total of 54/314 (17%) had nCRT and 35 patients (11%) underwent abdominoperineal excision. Median follow-up was 64 months. Overall, local recurrence was detected in 18/314 (5.7%) and distant recurrence in 45/314 (14.3%). In patients not receiving nCRT (n = 260), local recurrence was detected in 11/260 (4.2%) and distant recurrence in 35/260 (13.5%). Disease-free survival was 80.5% at 5 years. Specifically, disease-free survival was 89% in mrTD/EMVI-negative and mrCRM-negative, 67% in mrTD/EMVI-positive and mrCRM-negative, and 64% in the mrCRM-positive rectal cancer (log-rank, P < 0.001). On multivariable Cox-regression analysis mrTD/EMVI was the only MRI variable associated with disease-free survival (hazard ratio 2.95; P < 0.001).

CONCLUSION

mrTD/EMVI is a major prognostic indicator. Rectal cancer patients with mrCRM-negative and mrTD/EMVI-negative have excellent long-term outcomes with surgery alone. Patients with mrTD/EMVI-positive should be selectively stratified for neoadjuvant treatments in future clinical trials.

Development of a Diagnostic Artificial Intelligence Tool for Lateral Lymph Node Metastasis in Advanced Rectal Cancer

BACKGROUND

Metastatic lateral lymph node dissection can improve survival in patients with rectal adenocarcinoma, with or without chemoradiotherapy. However, the optimal imaging diagnostic criteria for lateral lymph node metastases remain undetermined.

OBJECTIVE

To develop a lateral lymph node metastasis diagnostic artificial intelligence tool using deep learning, for patients with rectal adenocarcinoma who underwent radical surgery and lateral lymph node dissection.

DESIGN

Retrospective study.

SETTINGS

Multicenter study.

PATIENTS

A total of 209 patients with rectal adenocarcinoma, who underwent radical surgery and lateral lymph node dissection at 15 participating hospitals, were enrolled in the study and allocated to training (n = 139), test (n = 17), or validation (n = 53) cohorts.

MAIN OUTCOME MEASURES

In the neoadjuvant treatment group, images taken before pretreatment were classified as baseline images and those taken after pretreatment as presurgery images. In the upfront surgery group, presurgery images were classified as both baseline and presurgery images. We constructed 2 types of artificial intelligence, using baseline and presurgery images, by inputting the patches from these images into ResNet-18, and we assessed their diagnostic accuracy.

RESULTS

Overall, 124 patients underwent surgery alone, 52 received neoadjuvant chemotherapy, and 33 received chemoradiotherapy. The number of resected lateral lymph nodes in the training, test, and validation cohorts was 2418, 279, and 850, respectively. The metastatic rates were 2.8%, 0.7%, and 3.7%, respectively. In the validation cohort, the precision-recall area under the curve was 0.870 and 0.963 for the baseline and presurgery images, respectively. Although both baseline and presurgery images provided good accuracy for diagnosing lateral lymph node metastases, the accuracy of presurgery images was better than that of baseline images.

LIMITATIONS

The number of cases is small.

CONCLUSIONS

An artificial intelligence tool is a promising tool for diagnosing lateral lymph node metastasis with high accuracy.

DESARROLLO DE UNA HERRAMIENTA DE INTELIGENCIA ARTIFICIAL PARA EL DIAGNSTICO DE METSTASIS EN GANGLIOS LINFTICOS LATERALES EN CNCER DE RECTO AVANZADO

ANTECEDENTES:Disección de nódulos linfáticos laterales metastásicos puede mejorar la supervivencia en pacientes con adenocarcinoma del recto, con o sin quimiorradioterapia. Sin embargo, aún no se han determinado los criterios óptimos de diagnóstico por imágenes de los nódulos linfáticos laterales metastásicos.OBJETIVO:Nuestro objetivo fue desarrollar una herramienta de inteligencia artificial para el diagnóstico de metástasis en nódulos linfáticos laterales mediante el aprendizaje profundo, para pacientes con adenocarcinoma del recto que se sometieron a cirugía radical y disección de nódulos linfáticos laterales.DISEÑO:Estudio retrospectivo.AJUSTES:Estudio multicéntrico.PACIENTES:Un total de 209 pacientes con adenocarcinoma del recto, que se sometieron a cirugía radical y disección de nódulos linfáticos laterales en 15 hospitales participantes, se inscribieron en el estudio y se asignaron a cohortes de entrenamiento (n = 139), prueba (n = 17) o validación (n = 53).PRINCIPALES MEDIDAS DE RESULTADO:En el grupo de tratamiento neoadyuvante, las imágenes tomadas antes del tratamiento se clasificaron como imágenes de referencia y las posteriores al tratamiento, como imágenes previas a la cirugía. En el grupo de cirugía inicial, las imágenes previas a la cirugía se clasificaron como imágenes de referencia y previas a la cirugía. Construimos dos tipos de inteligencia artificial, utilizando imágenes de referencia y previas a la cirugía, ingresando los parches de estas imágenes en ResNet-18. Evaluamos la precisión diagnóstica de los dos tipos de inteligencia artificial.RESULTADOS:En general, 124 pacientes se sometieron a cirugía solamente, 52 recibieron quimioterapia neoadyuvante y 33 recibieron quimiorradioterapia. El número de nódulos linfáticos laterales removidos en los cohortes de entrenamiento, prueba y validación fue de 2,418; 279 y 850, respectivamente. Las tasas metastásicas fueron 2.8%, 0.7%, y 3.7%, respectivamente. En el cohorte de validación, el área de recuperación de precisión bajo la curva fue de 0.870 y 0.963 para las imágenes de referencia y antes de la cirugía, respectivamente. Aunque tanto las imágenes previas a la cirugía como las iniciales proporcionaron una buena precisión para diagnosticar metástasis en los nódulos linfáticos laterales, la precisión de las imágenes previas a la cirugía fue mejor que la de las imágenes iniciales.LIMITACIONES:El número de casos es pequeño.CONCLUSIÓN:La inteligencia artificial es una herramienta prometedora para diagnosticar metástasis en los nódulos linfáticos laterales con alta precisión. (Traducción-Dr. Aurian Garcia Gonzalez ).

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.

Nonoperative Management of dMMR/MSI-H Colorectal Cancer following Neoadjuvant Immunotherapy: A Narrative Review

Immunotherapy with PD-1 blockade has achieved a great success in colorectal cancers (CRCs) with high microsatellite instability (MSI-H) and deficient mismatch repair (dMMR), and has become the first-line therapy in metastatic setting. Studies of neoadjuvant immunotherapy also report exciting results, showing high rates of clinical complete response (cCR) and pathological complete response. The high efficacy and long duration of response of immunotherapy has prompt attempts to adopt watch-and-wait strategy for patients achieving cCR following the treatment. Thankfully, the watch-and-wait approach has been proposed for nearly 20 years for patients undergoing chemoradiotherapy and has gained ground among patients as well as clinicians. In this narrative review, we combed through the available information on immunotherapy for CRC and on the watch-and-wait strategy in chemoradiotherapy, and looked forward to a future where neoadjuvant immunotherapy as a curative therapy would play a big part in the treatment of MSI-H/dMMR CRC.

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.

Magnifying chromoendoscopy is a reliable method in the selection of rectal neoplasms for local excision

PURPOSE

Adequate staging of early rectal neoplasms is essential for organ-preserving treatments, but magnetic resonance imaging (MRI) frequently overestimates the stage of those lesions. We aimed to compare the ability of magnifying chromoendoscopy and MRI to select patients with early rectal neoplasms for local excision.

METHODS

This retrospective study in a tertiary Western cancer center included consecutive patients evaluated by magnifying chromoendoscopy and MRI who underwent en bloc resection of nonpedunculated sessile polyps larger than 20 mm, laterally spreading tumors (LSTs) [Formula: see text] 20 mm, or depressed-type lesions of any size (Paris 0-IIc). Sensitivity, specificity, accuracy, and positive and negative predictive values of magnifying chromoendoscopy and MRI to determine which lesions were amenable to local excision (i.e., [Formula: see text] T1sm1) were calculated.

RESULTS

Specificity of magnifying chromoendoscopy was 97.3% (95% CI 92.2-99.4), and accuracy was 92.7% (95% CI 86.7-96.6) for predicting invasion deeper than T1sm1 (not amenable to local excision). MRI had lower specificity (60.5%, 95% CI 43.4-76.0) and lower accuracy (58.3%, 95% CI 43.2-72.4). Magnifying chromoendoscopy incorrectly predicted invasion depth in 10.7% of the cases in which the MRI was correct, while magnifying chromoendoscopy provided a correct diagnosis in 90% of the cases in which the MRI was incorrect (p = 0.001). Overstaging occurred in 33.3% of the cases in which magnifying chromoendoscopy was incorrect and 75% of the cases in which MRI was incorrect.

CONCLUSION

Magnifying chromoendoscopy is reliable for predicting invasion depth in early rectal neoplasms and selecting patients for local excision.

Effects of MRI radiomics combined with clinical data in evaluating lymph node metastasis in mrT1-3a staging rectal cancer

Objective

To investigate the value of a clinical-MRI radiomics model based on clinical characteristics and T2-weighted imaging (T2WI) for preoperatively evaluating lymph node (LN) metastasis in patients with MRI-predicted low tumor (T) staging rectal cancer (mrT1, mrT2, and mrT3a with extramural spread ≤ 5 mm).

Methods

This retrospective study enrolled 303 patients with low T-staging rectal cancer (training cohort, n = 213, testing cohort n = 90). A total of 960 radiomics features were extracted from T2WI. Minimum redundancy and maximum relevance (mRMR) and support vector machine were performed to select the best performed radiomics features for predicting LN metastasis. Multivariate logistic regression analysis was then used to construct the clinical and clinical-radiomics combined models. The model performance for predicting LN metastasis was assessed by receiver operator characteristic curve (ROC) and clinical utility implementing a nomogram and decision curve analysis (DCA). The predictive performance for LN metastasis was also compared between the combined model and human readers (2 seniors).

Results

Fourteen radiomics features and 2 clinical characteristics were selected for predicting LN metastasis. In the testing cohort, a higher positive predictive value of 75.9% for the combined model was achieved than those of the clinical model (44.8%) and two readers (reader 1: 54.9%, reader 2: 56.3%) in identifying LN metastasis. The interobserver agreement between 2 readers was moderate with a kappa value of 0.416. A clinical-radiomics nomogram and decision curve analysis demonstrated that the combined model was clinically useful.

Conclusion

T2WI-based radiomics combined with clinical data could improve the efficacy in noninvasively evaluating LN metastasis for the low T-staging rectal cancer and aid in tailoring treatment strategies.

Progress in the diagnosis of lymph node metastasis in rectal cancer: a review

Historically, the chief focus of lymph node metastasis research has been molecular and clinical studies of a few essential pathways and genes. Recent years have seen a rapid accumulation of massive omics and imaging data catalyzed by the rapid development of advanced technologies. This rapid increase in data has driven improvements in the accuracy of diagnosis of lymph node metastasis, and its analysis further demands new methods and the opportunity to provide novel insights for basic research. In fact, the combination of omics data, imaging data, clinical medicine, and diagnostic methods has led to notable advances in our basic understanding and transformation of lymph node metastases in rectal cancer. Higher levels of integration will require a concerted effort among data scientists and clinicians. Herein, we review the current state and future challenges to advance the diagnosis of lymph node metastases in rectal cancer.

Magnetic resonance imaging in preoperative assessment of the mesorectal nodal status of patients with rectal cancer - Can it be trusted?

PURPOSE

Magnetic resonance imaging (MRI) is a fundamental diagnostic modality for the evaluation of primary rectal cancer, but MRI assessment of nodal involvement remains a confounding factor.

METHOD

This prospective cohort study was conducted to investigate the accuracy of preoperative MRI in the assessment of nodal status by comparing histopathology reports to MRI findings on a node-by-node basis in 69 patients with rectal cancer.

RESULTS

Primary surgery was performed in 40 (58.0%) patients; 29 (42.0%) study patients underwent neoadjuvant chemoradiotherapy (CRT). Histopathological examination revealed T1 tumour in 8 (11.6%) patients, T2 tumour in 30 (43.5%), and T3 tumour in 25 (36.2%). In total, 897 lymph nodes (LNs) have been harvested (13.1 ± 5.4 LNs per specimen). There were 77 MRI-suspicious LNs, 21 (27.3%) of which were histologically proven malignant. The sensitivity of MRI for assessing nodal involvement was 51.2% and specificity 93.4%. Of the 28 patients with MRI-suspicious LNs the diagnosis was correct in 42.8%. The MRI accuracy was 33.3% in "primary surgery" subgroup (n = 18, malignant LNs found in 6 patients). Diagnosis of MRI-negative LNs was correct in 90.2% of study patients; malignant nodes were found in 9.8% of patients initially classified as cN0.

CONCLUSIONS

MRI prediction of nodal status in patients with rectal cancer has very low accuracy. Decisions regarding neoadjuvant CRT should not be based on MRI assessment of nodal status, but on the MRI evaluation of tumour depth invasion (T stage and relationship between the tumour and mesorectal fascia).

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.

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.

A prospective study of diagnostic accuracy of multidisciplinary team and radiology reporting of preoperative colorectal cancer local staging

INTRODUCTION

The aim of this study was to correlate and assess diagnostic accuracy of preoperative staging at multidisciplinary team meeting (MDT) against the original radiology reports and pathological staging in colorectal cancer patients.

METHODS

A prospective observational study was conducted at two institutions. Patients with histologically proven colorectal cancer and available preoperative imaging were included. Preoperative tumor and nodal staging (cT and cN) as determined by the MDT and the radiology report (computed tomography [CT] and/or magnetic resonance imaging [MRI]) were recorded. Kappa statistics were used to assess agreement between MDT and the radiology report for cN staging in colon cancer, cT and cN in rectal cancer, and tumor regression grade (TRG) in patients with rectal cancer who received neoadjuvant therapy. Pathological report after surgery served as the reference standard for local staging, and AUROC curves were constructed to compare diagnostic accuracy of the MDT and radiology report.

RESULTS

A total of 481 patients were included. Agreement between MDT and radiology report for cN stage was good in colon cancer (k = .756, Confidence Interval (CI) 95% .686-.826). Agreement for cT and cN and in rectal cancer was very good (kw = .825, CI 95% .758-.892) and good (kw = .792, CI 95% .709-.875), respectively. In the rectal cancer group that received neoadjuvant therapy, agreement on TRG was very good (kw = .919, CI 95% .846-.993). AUROC curves using pathological staging indicated no difference in diagnostic accuracy between MDT and radiology reports for either colon or rectal cancer.

CONCLUSION

Preoperative colorectal cancer local staging was consistent between specialist MDT review and original radiology reports, with no significant differences in diagnostic accuracy identified.

Development and validation of a high-resolution T2WI-based radiomic signature for the diagnosis of lymph node status within the mesorectum in rectal cancer

Purpose

The aim of this study was to explore the feasibility of a high-resolution T2-weighted imaging (HR-T2WI)-based radiomics prediction model for diagnosing metastatic lymph nodes (LNs) within the mesorectum in rectal cancer.

Method

A total of 604 LNs (306 metastatic and 298 non-metastatic) from 166 patients were obtained. All patients underwent HR-T2WI examination and total mesorectal excision (TME) surgery. Four kinds of segmentation methods were used to select region of interest (ROI), including method 1 along the border of LNs; method 2 along the expanded border of LNs with an additional 2–3 mm; method 3 covering the border of LNs only; and method 4, a circle region only within LNs. A total of 1,409 features were extracted for each method. Variance threshold method, Select K Best, and Lasso algorithm were used to reduce the dimension. All LNs were divided into training and test sets. Fivefold cross-validation was used to build the logistic model, which was evaluated by the receiver operating characteristic (ROC) with four indicators, including area under the curve (AUC), accuracy (ACC), sensitivity (SE), and specificity (SP). Three radiologists with different working experience in diagnosing rectal diseases assessed LN metastasis respectively. The diagnostic efficiencies with each of four segmentation methods and three radiologists were compared to each other.

Results

For the test set, the AUCs of four segmentation methods were 0.820, 0.799, 0.764, and 0.741; the ACCs were 0.725, 0.704, 0.709, and 0.670; the SEs were 0.756, 0.634, 0.700, and 0.589; and the SPs were 0.696, 0.772, 0.717, and 0.750, respectively. There was no statistically significant difference in AUC between the four methods (p > 0.05). Method 1 had the highest values of AUC, ACC, and SE. For three radiologists, the overall diagnostic efficiency was moderate. The corresponding AUCs were 0.604, 0.634, and 0.671; the ACCs were 0.601, 0.632, and 0.667; the SEs were 0.366, 0.552, and 0.392; and the SPs were 0.842, 0.715, and 0.950, respectively.

Conclusions

The proposed HR-T2WI-based radiomic signature exhibited a robust performance on predicting mesorectal LN status and could potentially be used for clinicians in order to determine the status of metastatic LNs in rectal cancer patients.

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.

A model based on endorectal ultrasonography predicts lateral lymph node metastasis in low and middle rectal cancer

PURPOSE

To investigate the risk factors for lymph node (LN) metastasis in low and middle rectal tumors, construct a predictive model and test the model's diagnostic efficacy.

METHODS

The clinical and pathological data of 172 patients with rectal cancer confirmed by surgery were retrospectively evaluated, among whom 61 patients were finally included in this study. Patients were divided into positive groups and negative groups based on LN metastasis, and risk factors that might affect LN metastasis were analyzed. Finally, a risk predictive model was constructed based on the weights of each risk factor.

RESULTS

Compared with pathology, the efficacy of diagnosing LN metastasis only according to conventional endorectal ultrasonography (ERUS) features of LN was not high, with sensitivity 67%, specificity 86%, positive predictive value 76%, negative predictive value 80%, and accuracy 79%. Univariate analysis showed that circumferential angle of the tumor, ultrasonic T- stage (UT stage), conventional ultrasound features diagnosis of LN metastasis, strain ratio (SR) of tumor were risk factors for LN metastasis, while vascular resistance index of rectal tumor was protective factor. Multivariate analysis showed that UT stage (OR = 7.188, p = 0.049), conventional ultrasound features diagnosis of LN metastasis (OR = 8.010, p = 0.025) and SR (OR = 5.022, p = 0.031) were independent risk factors for LN metastasis. These risk factors were included in logistic regression analysis and the model was established, Y = -7.3 + 1.9 X10 + 2.1 X11 + 1.6 X13 (Y = Logit[P], P: LN metastasis rate, X10: UT stage, X11: conventional ultrasound features diagnosis of LN metastasis, X13: SR). The receiver operating characteristic (ROC) curve was used to test the model's predictive efficacy, the area under the curve was 0.95, sensitivity: 95%, specificity: 87%. Hosmer-Lemeshow goodness of fit test showed X2 = 6.015, p = 0.65 (p > 0.05), indicating that the model had a high predictive value.

CONCLUSION

Evaluation of perirectal LN metastasis only based on conventional ERUS features of LN was not effective enough. UT stage of tumor, conventional ultrasound features diagnosis of LN metastasis and SR were independent risk factors for LN metastasis. The predictive model had good assessment efficacy and had certain clinical application value.

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.

Validation of In Vivo Nodal Assessment of Solid Malignancies with USPIO-Enhanced MRI: A Workflow Protocol

Background: In various cancer types, the first step towards extended metastatic disease is the presence of lymph node metastases. Imaging methods with sufficient diagnostic accuracy are required to personalize treatment. Lymph node metastases can be detected with ultrasmall superparamagnetic iron oxide (USPIO)-enhanced magnetic resonance imaging (MRI), but this method needs validation. Here, a workflow is presented, which is designed to compare MRI-visible lymph nodes on a node-to-node basis with histopathology. Methods: In patients with prostate, rectal, periampullary, esophageal, and head-and-neck cancer, in vivo USPIO-enhanced MRI was performed to detect lymph nodes suspicious of harboring metastases. After lymphadenectomy, but before histopathological assessment, a 7 Tesla preclinical ex vivo MRI of the surgical specimen was performed, and in vivo MR images were radiologically matched to ex vivo MR images. Lymph nodes were annotated on the ex vivo MRI for an MR-guided pathological examination of the specimens. Results: Matching lymph nodes of ex vivo MRI to pathology was feasible in all cancer types. The annotated ex vivo MR images enabled a comparison between USPIO-enhanced in vivo MRI and histopathology, which allowed for analyses on a nodal, or at least on a nodal station, basis. Conclusions: A workflow was developed to validate in vivo USPIO-enhanced MRI with histopathology. Guiding the pathologist towards lymph nodes in the resection specimens during histopathological work-up allowed for the analysis at a nodal basis, or at least nodal station basis, of in vivo suspicious lymph nodes with corresponding histopathology, providing direct information for validation of in vivo USPIO-enhanced, MRI-detected lymph nodes.

The importance of MRI for rectal cancer evaluation

Magnetic resonance imaging (MRI) has gained increasing importance in the management of rectal cancer over the last two decades. The role of MRI in patients with rectal cancer has expanded beyond the tumor-node-metastasis (TNM) system in both staging and restaging scenarios and has contributed to identifying "high" and "low" risk features that can be used to tailor and personalize patient treatment; for instance, selecting the patients for neoadjuvant chemoradiation (NCRT) before the total mesorectal excision (TME) surgery based on risk of recurrence. Among those features, the status of the circumferential resection margin (CRM), extramural vascular invasion (EMVI), and tumor deposits (TD) have stood out. Moreover, MRI also has played a role in surgical planning, especially when the tumor is located in the low rectum, when the relationship between tumor and the anal canal is important to choose the best surgical approach, and in cases of locally advanced or recurrent tumors invading adjacent pelvic organs that may require more complex surgeries such as pelvic exenteration. As approaches using organ preservation emerge, including transanal local excision and "watch-and-wait", MRI may help in the patient selection for those treatments, follow up, and detection of tumor regrowth. Additionally, potential MRI-based prognostic and predictive biomarkers, such as quantitative and semi-quantitative metrics derived from functional sequences like diffusion-weighted imaging (DWI) and dynamic contrast-enhanced (DCE), and radiomics, are under investigation. This review provides an overview of the current role of MRI in rectal cancer in staging and restaging and highlights the main areas under investigation and future perspectives.

A novel classification method of lymph node metastasis in colorectal cancer

Colorectal cancer lymph node metastasis, which is highly associated with the patient's cancer recurrence and survival rate, has been the focus of many therapeutic strategies that are highly associated with the patient's cancer recurrence and survival rate. The popular methods for classification of lymph node metastasis by neural networks, however, show limitations as the available low-level features are inadequate for classification, and the radiologists are unable to quickly review the images. Identifying lymph node metastasis in colorectal cancer is a key factor in the treatment of patients with colorectal cancer. In the present work, an automatic classification method based on deep transfer learning was proposed. Specifically, the method resolved the problem of repetition of low-level features and combined these features with high-level features into a new feature map for classification; and a merged layer which merges all transmitted features from previous layers into a map of the first full connection layer. With a dataset collected from Harbin Medical University Cancer Hospital, the experiment involved a sample of 3,364 patients. Among these samples, 1,646 were positive, and 1,718 were negative. The experiment results showed the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were 0.8732, 0.8746, 0.8746 and 0.8728, respectively, and the accuracy and AUC were 0.8358 and 0.8569, respectively. These demonstrated that our method significantly outperformed the previous classification methods for colorectal cancer lymph node metastasis without increasing the depth and width of the model.

Multidisciplinary management of early rectal cancer - The role of surgical local excision in current and future clinical practice

The implementation of bowel cancer screening programs has led to a rise in the incidence of early rectal cancer. The combination of increased incidence and the growing interest in organ-sparing treatment options has led to an amplified importance of local excision techniques in treatment strategies for early rectal cancer. In addition, developments in new technologies of single-port surgery have popularized surgical techniques. Although local treatment of early rectal cancer seems promising, a multidisciplinary approach is necessary and awareness of the oncological robustness is warranted to enable shared decision-making. This review illustrates the position of surgical local excision in the treatment of early rectal cancer and reflects on its role in current and future clinical practice.

Diagnostic Efficiency of Diffusion Sequences and a Clinical Nomogram for Detecting Lymph Node Metastases from Rectal Cancer

RATIONALE AND OBJECTIVES

First, to evaluate and compare three different diffusion sequences (i.e., standard DWI, IVIM, and DKI) for nodal staging. Second, to combine the DWI, and anatomic information to assess metastatic lymph node (LN).

MATERIALS AND METHODS

We retrospectively identified 136 patients of rectal adenocarcinoma who met the inclusion criteria. Three diffusion sequences (i.e., standard DWI, IVIM, and DKI) were performed, and quantitative parameters were evaluated. Univariate and multivariate analyses were used to assess the associations between the anatomic and DWI information and LN pathology. Multivariate logistic regression was used to identify independent risk factors. A nomogram model was established, and the model performance was evaluated by the concordance index (c-index) and calibration curve.

RESULTS

There was a statistical difference in variables (LN long diameter, LN short diameter, LN boundary, LN signal, peri-LN signal intensity, ADC_-1000, ADC_-1400, ADC_-2000, K_app and D) between metastatic and non-metastatic LN for training and validation cohorts (p < 0.05). The ADC value derived from b = 1000 mm/s (ADC_-1000) showed the relative higher AUC (AUC = 0.780) than the ADC value derived from b = 1400 mm/s (ADC_-1400) (AUC = 0.703). The predictive accuracy of the nomogram measured by the c-index was 0.854 and 0.812 in the training and validation cohort, respectively.

CONCLUSION

The IVIM and DKI model's diagnostic efficiency was not significantly improved compared to conventional DWI. The diagnostic accuracy of metastatic LN can be enhanced using the nomogram model, leading to a rational therapeutic choice.

Value of multiple models of diffusion-weighted imaging for improving the nodal staging of preoperatively node-negative rectal cancer

OBJECTIVE

To investigate the parameters of multiple diffusion-weighted imaging (DWI) models for improving nodal staging of preoperatively node-negative rectal cancer.

MATERIALS AND METHODS

A total of 74 rectal cancer patients without suspected metastatic lymph nodes on conventional MRI who underwent direct surgical resection between November 2018 and January 2020 were enrolled in this prospective study. DWI parameters of mono-exponential model (ADC), intravoxel incoherent motion (D, D* and f), stretched exponential model (DDC and α), and diffusion kurtosis imaging (MD and MK) within the whole tumor were measured to predict the nodal staging in rectal cancer patients.

RESULTS

The D*, DDC, and MK values were significantly different in patients with pN0 and pN1-2 (all P < 0.001). The D*, DDC, and MK showed good diagnostic performance with the area under the receiver operating characteristic (AUC) of 0.788, 0.827 and 0.799. Multivariate analysis indicated D* (odds ratio, OR = 1.163, P = 0.003) and DDC (OR = 0.007, P = 0.019) as significant predictors of nodal staging. The combination of DDC and D* demonstrated superior diagnostic performance with the AUC, sensitivity, specificity and accuracy of 0.872, 0.800, 0.932 and 0.878, respectively.

CONCLUSION

Multiple functional DWI parameters were potential to identify the rectal cancer patients with micro-nodal involvement for accurate treatment.

Histogram models based on intravoxel incoherent motion diffusion-weighted imaging to predict nodal staging of rectal cancer

PURPOSE

To develop a model based on histogram parameters derived from intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) for predicting the nodal staging of rectal cancer (RC).

MATERIAL AND METHODS

A total of 95 RC patients who underwent direct surgical resection were enrolled in this prospective study. The nodal staging on conventional magnetic resonance imaging (MRI) was evaluated according to the short axis diameter and morphological characteristics. Histogram parameters were extracted from apparent diffusion coefficient (ADC), true diffusion coefficient (D), pseudo-diffusion coefficient (D*), and perfusion fraction (f) maps. Multivariate binary logistic regression analysis was conducted to establish models for predicting nodal staging among all patients and those underestimated on conventional MRI.

RESULTS

The combined model based on multiple maps demonstrated superior diagnostic performance to single map models, with an area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy of 0.959, 94.3%, 88.3%, and 90.5%, respectively. The AUC of the combined model was significantly higher than that of the conventional nodal staging (P < 0.001). Additionally, 85.0% of the underestimated patients had suspicious lymph nodes with 5-8 mm short-axis diameter. The histogram model for these subgroups of patients showed good diagnostic efficacy with an AUC, sensitivity, specificity, and accuracy of 0.890, 100%, 75%, and 80.5%.

CONCLUSION

The histogram model based on IVIM-DWI could improve the diagnostic performance of nodal staging of RC. In addition, histogram parameters of IVIM-DWI may help to reduce the uncertainty of nodal staging in underestimated patients on conventional MRI.

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

Background

Magnetic resonance imaging (MRI)-based lymph node staging remains a significant challenge in the treatment of rectal cancer. Pretreatment evaluation of lymph node metastasis guides the formulation of treatment plans. This systematic review aimed to evaluate the diagnostic performance of MRI in lymph node staging using various morphological criteria.

Methods

A systematic search of the EMBASE, Medline, and Cochrane databases was performed. Original articles published between 2000 and January 2021 that used MRI for lymph node staging in rectal cancer were eligible. The included studies were assessed using the QUADAS-2 tool. A bivariate random-effects model was used to conduct a meta-analysis of diagnostic test accuracy.

Results

Thirty-seven studies were eligible for this meta-analysis. The pooled sensitivity, specificity, and diagnostic odds ratio of preoperative MRI for the lymph node stage were 0.73 (95% confidence interval [CI], 0.68–0.77), 0.74 (95% CI, 0.68–0.80), and 7.85 (95% CI, 5.78–10.66), respectively. Criteria for positive mesorectal lymph node metastasis included (A) a short-axis diameter of 5 mm, (B) morphological standard, including an irregular border and mixed-signal intensity within the lymph node, (C) a short-axis diameter of 5 mm with the morphological standard, (D) a short-axis diameter of 8 mm with the morphological standard, and (E) a short-axis diameter of 10 mm with the morphological standard. The pooled sensitivity/specificity for these criteria were 75%/64%, 81%/67%, 74%/79%, 72%/66%, and 62%/91%, respectively. There was no significant difference among the criteria in sensitivity/specificity. The area under the receiver operating characteristic (ROC) curve values of the fitted summary ROC indicated a diagnostic accuracy rate of 0.75–0.81.

Conclusion

MRI scans have minimal accuracy as a reference index for pretreatment staging of various lymph node staging criteria in rectal cancer. Multiple types of evidence should be used in clinical decision-making.

Deep transfer learning based on magnetic resonance imaging can improve the diagnosis of lymph node metastasis in patients with rectal cancer

Background

Lymph node (LN) metastasis is the main prognostic factor for local recurrence and overall survival of patients with rectal cancer. The accurate evaluation of LN status in rectal cancer patients is associated with improved treatment and prognosis. This study aimed to apply deep transfer learning to classify LN status in patients with rectal cancer to improve N staging accuracy.

Methods

The study included 129 patients with 325 rectal cancer screenshots of LN T2-weighted (T2W) images from April 2018 to March 2019. Deep learning was applied through a pre-trained model, Inception-v3, for recognition and detection of LN status. The results were compared to manual identification by experienced radiologists. Two radiologists reviewed images and independently identified their status using various criteria with or without short axial (SA) diameter measurements. The accuracy, positive predictive value (PPV), negative predictive value (NPV), sensitivity, specificity, and the area under the receiver operating characteristic (ROC) curve (AUC) were calculated.

Results

When the same radiologist performed the analysis, the AUC was not significantly different in the presence or absence of LN diameter measurements (P>0.05). In the deep transfer learning method, the PPV, NPV, sensitivity, and specificity were 95.2%, 95.3%, 95.3%, and 95.2%, respectively, and the AUC and accuracy were 0.994 and 95.7%, respectively. These results were all higher than that achieved with manual diagnosis by the radiologists.

Conclusions

The internal details of LNs should be used as the main criteria for positive diagnosis when using MRI. Deep transfer learning can improve the MRI diagnosis of positive LN metastasis in patients with rectal cancer.

Current status of surgical treatment of rectal cancer in China

With the changing lifestyle and the acceleration of aging in the Chinese population, the incidence and mortality of colorectal cancer (CRC) have risen in the last decades. On the contrary, the incidence and mortality of CRC have continued to decline in the USA since the 1980s, which is mainly attributed to early screening and standardized diagnosis and treatment. Rectal cancer accounts for the largest proportion of CRC in China, and its treatment regimens are complex. At present, surgical treatment is still the most important treatment for rectal cancer. Since the first Chinese guideline for diagnosis and treatment of CRC was issued in 2010, the fourth version has been revised in 2020. These guidelines have greatly promoted the standardization and internationalization of CRC diagnosis and treatment in China. And with the development of comprehensive treatment methods such as neoadjuvant chemoradiotherapy, targeted therapy, and immunotherapy, the post-operative quality of life and prognosis of patients with rectal cancer have improved. We believe that the inflection point of the rising incidence and mortality of rectal cancer will appear in the near future in China. This article reviewed the current status and research progress on surgical therapy of rectal cancer in China.

Prediction of lymph node metastasis in rectal cancer: comparison between shear-wave elastography based ultrasomics and MRI

PURPOSE

We aimed to explore the diagnostic efficiency of shear-wave elastography (SWE) ultrasomics in the preoperative prediction of lymph node (LN) metastasis in rectal cancer.

METHODS

This study included 87 patients with pathologically confirmed rectal cancer, with data gathered from August 2017 to August 2018. A total of 1044 ultrasomics features of rectal tumor were collected with AK software from the SWE examinations. The least absolute shrinkage and selection operator (LASSO) regression model was used for feature selection and building a SWE ultrasomics signature. The diagnostic performance was evaluated with an area under the receiver operating characteristic curve (AUC) analysis. Then, the diagnostic performance of the SWE ultrasomics signature was compared with magnetic resonance imaging (MRI).

RESULTS

Of the 87 patients, 40 (46.0%) had LN metastasis. Thirteen ultrasomics features of rectal tumor were selected as the most significant features. The SWE ultrasomics signature correlated with LN metastasis (p < 0.001). Patients with LN metastasis had higher signature than patients without LN metastasis. In terms of diagnostic performance, SWE ultrasomics signature was significantly superior to MRI (AUC, 0.883 vs. 0.760, p = 0.034). The diagnostic accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of SWE ultrasomics signature were 82.8%, 87.5%, 78.8%, 77.8%, and 88.1%, respectively, while those of MRI were 75.9%, 77.5%, 74.5%, 72.1%, and 79.6%, respectively.

CONCLUSION

SWE ultrasomics is a more accurate predictive method for identifying LN metastasis preoperatively than MRI. Thus, SWE ultrasomics might be used to better guide preoperative individual therapies for patients with rectal cancer.

An MRI-based multi-objective radiomics model predicts lymph node status in patients with rectal cancer

PURPOSE

To apply a multi-objective radiomics model based on pre-operative magnetic resonance imaging (MRI) for improving diagnostic accuracy of LN metastasis in rectal cancer patients.

METHODS

This study consisted of 91 patients diagnosed with rectal cancer from April 2018 to March 2019. All patients underwent rectal MRI before surgery without any other treatment. Clinical data, subjective radiologist assessments, and radiomic features of LNs were obtained. A total of 1409 radiomic features were extracted from T2WI LN images. Multi-objective optimization with the iterative multi-objective immune algorithm (IMIA) was used to select radiomic features to build prediction models. Predictive performances of radiomic, radiologist, and combined radiomic and radiologist models were assessed for accuracy by receiver operating characteristics (ROC) curves.

RESULTS

For the radiologist analysis, heterogeneity was the only significant independent predictor of LN status. The sensitivity, specificity, and accuracy of the subjective radiologist analysis were 72.09%, 73.81%, and 78.12%, respectively. The sensitivity, specificity, and accuracy of the solitary radiomic model consisting of 10 features were 89.81%, 82.57%, and 87.77%, respectively. The sensitivity, specificity, and accuracy of the combined model, which consisted of 12 radiomic and radiologist features, were 92.23%, 84.69%, and 89.88%, respectively. The combined model had the best prediction performance with an AUC of 0.94.

CONCLUSIONS

The multi-objective radiomics model based on T2WI images was very useful in predicting pre-operative LN status in rectal cancer patients.

Assessment of T and N staging with MRI3T in lower and middle rectal cancer and impact on clinical strategy

BACKGROUND

To determine the diagnostic accuracy of preoperative T/N stage using MRI in lower and middle rectal cancer patients and the impacts on clinical decision-making.

PATIENTS AND METHODS

There were 354 patients recruited from May 2017 to February 2019. MRI was performed within 2 weeks before surgery. Histopathologic results were evaluated for the postoperative T/N stage and MRI diagnostic accuracy was assessed based on the postoperative histopathologic results. Accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and Kappa values were used to evaluate MRI diagnostic accuracy and analysis consistency compared with postoperative histopathologic staging.

RESULTS

Overall MRI diagnostic accuracy was 78.2% and 56.8% for T1-4 and N0-2 staging. The Kappa values were 0.625 and 0.323 for T1-4 and N0-2 staging, respectively. After combination, MRI diagnostic accuracy was 85% and 69.5% for T and N staging. The Kappa values were 0.693 and 0.4 for T and N staging. The diagnostic accuracy of MRI for treatment decision-making was 79.1%.

CONCLUSION

MRI enables a highly accurate preoperative assessment of T stage but only a fairly accurate preoperative assessment of the N stage for rectal cancer with surgery. The diagnostic accuracy of MRI for treatment decision-making is promising.

Histogram Analysis of Diffusion-Weighted Magnetic Resonance Imaging as a Biomarker to Predict Lymph Node Metastasis in T3 Stage Rectal Carcinoma

Purpose

This study investigated the predictive value of apparent diffusion coefficient (ADC) histogram parameters of the primary tumor for regional lymph node metastasis (LNM) in pathological T3 stage rectal cancer.

Patients and Methods

We retrospectively studied 175 patients with T3 stage rectal cancer who underwent preoperative MRI, including diffusion-weighted imaging, between January 2015 and October 2017. Based on pathological analysis of surgical specimens, 113 patients were classified into the LN− group and 62 in the LN+ group. We analyzed clinical data, radiological characteristics and histogram parameters derived from ADC maps. Then, receiver operating characteristic curve (ROC) analyses were generated to determine the best diagnostic performance.

Results

The mean (p=0.002, cutoff=1.08×10^–3 s/mm²), coefficient of variation (CV) (p=0.040, cutoff=0.249) of the ADC map, carbohydrate antigen 199, and N stage with magnetic resonance (mrN stage) were independent factors for LNM. Combining these factors yielded the best diagnostic performance, with the area under the ROC curve of 0.838, 72.9% sensitivity, 79.1% specificity, 65.2% positive predictive value, and 84.5% negative predictive value.

Conclusion

With the mean >1.08×10^–3 s/mm² and CV <0.249, the ADC improved the diagnostic performance of LNM in T3 stage rectal cancer, which could assist surgeons with neoadjuvant chemoradiotherapy.

Endorectal Ultrasound and Magnetic Resonance Imaging for Rectal Cancer Staging: A Modern Multimodality Approach

Preoperative staging represents a crucial point for the management, type of surgery, and candidacy for neoadjuvant therapy in patient with rectal cancer. The most recent clinical guidelines in oncology recommend an accurate preoperative evaluation in order to address early and advanced tumors to different therapeutic options. In particular, potential pitfalls may occur in the assessment of T3 tumors, which represents the most common stage at diagnosis. The depth of tumor invasion is known to be an important prognostic factor in rectal carcinoma; as a consequence, the T3 imaging classification has a substantial importance for treatment strategy and patient survival. However, the differentiation between tumor invasion of perirectal fat and mesorectal desmoplastic reactions remains a main goal for radiologists. Magnetic resonance imaging (MRI) is actually considered as the best imaging modality for rectal cancer staging. Although the endorectal ultrasound (ERUS) is the preferred staging method for early tumors, it could also be useful in identifying perirectal fat invasion. Moreover, the addiction of diffusion weighted imaging (DWI) improves the diagnostic performance of MRI in rectal cancer staging by adding functional information about rectal tumor and adjacent mesorectal tissues. This study investigated the diagnostic performance of conventional MRI alone, in combination with the DWI technique and ERUS in order to assess the best diagnostic imaging combination for rectal cancer staging.

Accuracy of pelvic magnetic resonance imaging in local staging for rectal cancer: a single local health district, real world experience

BACKGROUND

Neoadjuvant chemoradiotherapy plays a key role in reducing local recurrence rates for locally advanced rectal cancer. Pelvic magnetic resonance imaging (pMRI) is the gold standard for local clinical staging which allows clinicians to decide the treatment patients receive. A more advanced tumour or the presence of high-risk features on pMRI mean that neoadjuvant therapy will be offered to these patients. Understanding the accuracy of pMRI in local staging for rectal cancer is therefore crucial.

METHODS

A retrospective cohort analysis of the accuracy of pMRI staging in a subgroup of patients who had primary rectal cancer surgery without neoadjuvant therapy was performed. Specificity and sensitivity for T-staging, N-staging and presence of high-risk features (threatened circumferential resection margin and extramural venous invasion) were calculated. Patients who had previous pelvic surgery, previous pelvic radiotherapy and previous surgery for continence were excluded.

RESULTS

A total of 114 patients were included in the analysis. MRI accurately predicts T-stage in 56.6% and N-stage in 55.8%. Prediction of extramural disease was accurate in 51%. A negative circumferential resection margin was accurately predicted in 98.6% of patients. Overall adherence to reporting proforma was 15.8%.

CONCLUSION

Overall, this study provided valuable information about the clinical staging of patients with rectal cancer who are at an early stage within a large regional catchment area in Australia with pMRI. These results allow us to assess the accuracy of our local staging with ramifications to the clinical decisions being made in the context of the more recent trials which questioned the need for neoadjuvant chemo-radiotherapy in all node positive patients.

Prediction Model Combining Clinical and MR Data for Diagnosis of Lymph Node Metastasis in Patients With Rectal Cancer

BACKGROUND

Determining the status of lymph node (LN) metastasis in rectal cancer patients preoperatively is crucial for the treatment option. However, the diagnostic accuracy of current imaging methods is low.

PURPOSE

To develop and test a model for predicting metastatic LNs of rectal cancer patients based on clinical data and MR images to improve the diagnosis of metastatic LNs.

STUDY TYPE

Retrospective.

SUBJECTS

In all, 341 patients with histologically confirmed rectal cancer were divided into one training set (120 cases) and three validation sets (69, 103, 49 cases).

FIELD STRENGTH/SEQUENCE

3.0T, axial and sagittal T₂ -weighted turbo spin echo and diffusion-weighted imaging (b = 0 s/mm² , 800 s/mm² ) ASSESSMENT: In the training dataset, univariate logistic regression was used to identify the clinical factors (age, gender, and tumor markers) and MR data that correlated with LN metastasis. Then we developed a prediction model with these factors by multiple logistic regression analysis. The accuracy of the model was verified using three validation sets and compared with the traditional MRI method.

STATISTICAL TESTS

Univariate and multivariate logistic regression. The area under the curve (AUC) value was used to quantify the diagnostic accuracy of the model.

RESULTS

Eight factors (CEA, CA199, ADCmean, mriT stage, mriN stage, CRM, EMVI, and differentiation degree) were significantly associated with LN metastasis in rectal cancer patients (P<0.1). In the training set (120) and the three validation sets (69, 103, 49), the AUC values of the model were much higher than the diagnosis by MR alone (training set, 0.902 vs. 0.580; first validation set, 0.789 vs. 0.743; second validation set, 0.774 vs. 0.573; third validation set, 0.761 vs. 0.524).

DATA CONCLUSION

For the diagnosis of metastatic LNs in rectal cancer patients, our proposed logistic regression model, combining clinical and MR data, demonstrated higher diagnostic efficiency than MRI alone.

LEVEL OF EVIDENCE

4 TECHNICAL EFFICACY STAGE: 2.

Watch and wait approach in rectal cancer: Current controversies and future directions

According to the main international clinical guidelines, the recommended treatment for locally-advanced rectal cancer is neoadjuvant chemoradiotherapy followed by surgery. However, doubts have been raised about the appropriate definition of clinical complete response (cCR) after neoadjuvant therapy and the role of surgery in patients who achieve a cCR. Surgical resection is associated with significant morbidity and decreased quality of life (QoL), which is especially relevant given the favourable prognosis in this patient subset. Accordingly, there has been a growing interest in alternative approaches with less morbidity, including the organ-preserving watch and wait strategy, in which surgery is omitted in patients who have achieved a cCR. These patients are managed with a specific follow-up protocol to ensure adequate cancer control, including the early identification of recurrent disease. However, there are several open questions about this strategy, including patient selection, the clinical and radiological criteria to accurately determine cCR, the duration of neoadjuvant treatment, the role of dose intensification (chemotherapy and/or radiotherapy), optimal follow-up protocols, and the future perspectives of this approach. In the present review, we summarize the available evidence on the watch and wait strategy in this clinical scenario, including ongoing clinical trials, QoL in these patients, and the controversies surrounding this treatment approach.

Value of 3Tesla MRI in the preoperative staging of mid-low rectal cancer and its impact on clinical strategies

Background

To determine the diagnostic accuracy of preoperative T/N stage with magnetic resonance imaging (MRI) in lower and middle rectal cancer patients and the impacts on clinical decision‐making.

Patients and methods

A total of 211 patients were recruited from October 2015 to March 2017 in this retrospective study. High‐resolution MRI was performed within 2 weeks before surgery. Histopathologic results were evaluated for the postoperative T/N stage and the diagnostic accuracy of MRI was assessed according to the postoperative histopathologic results. The accuracy, sensitivity, specificity, positive predictive value and negative predictive value were evaluated for T/N staging and κ values were used to evaluate MRI consistent analysis compared with postoperative histopathologic staging.

Results

The overall MRI diagnostic accuracy was 79.62% for T1‐4 staging and 54.50% for N0‐2 staging. The κ values were 0.619 and 0.255 for T1‐4 and N0‐2 staging, respectively. The diagnostic accuracy of MRI for treatment decision‐making was 80.57%.

Conclusion

MRI allows a highly accurate preoperative assessment of T stage but only a fairly accurate preoperative assessment of N stage for rectal cancer. The diagnostic accuracy of MRI for treatment decision‐making is promising, but additional studies are needed to validate these findings in a larger sample size from multiple centers.

Value of intravoxel incoherent motion magnetic resonance imaging for differentiating metastatic from nonmetastatic mesorectal lymph nodes with different short-axis diameters in rectal cancer

Background

Conventional magnetic resonance imaging (MRI) does not accurately evaluate lymph node (LN) status, which is essential for the treatment and prognosis assessment in patients with rectal cancer.

Objective

The aim of this study is to evaluate the diagnostic value of intravoxel incoherent motion (IVIM) MRI in differentiating metastatic and nonmetastatic mesorectal LNs with different short-axis diameters in rectal cancer patients.

Materials and Methods

Forty patients (154 LNs) were divided into three groups based on short-axis diameter: 3 mm ≤ × ≤5 mm, 5 mm < × ≤7 mm, and × >7 mm. MRI characteristics and IVIM parameters were compared between the metastatic and nonmetastatic LNs to determine the diagnostic value for discriminating them.

Results

In the 3 mm ≤ × ≤ 5 mm group, mean D values were significantly lower in metastatic than in the nonmetastatic LNs (P < 0.001). In the 5 mm < × ≤7 mm group, mean f values were significantly lower in metastatic than nonmetastatic LNs (P < 0.05). In the × >7 mm group, only the short-axis diameter of metastatic LNs was significantly greater than that of nonmetastatic LNs (P < 0.05). The area under the curve, sensitivity, specificity, and cutoff values were used for differentiating the metastatic from the nonmetastatic LNs.

Conclusion

IVIM parameters can differentiate metastatic from nonmetastatic LNs with smaller short-axis diameters (× ≤7 mm) in rectal cancer, and the short-axis diameter is a significant factor in identifying metastatic and nonmetastatic LNs in larger short-axis diameter groups (× >7 mm).

Risk factor analysis for inaccurate pre-operative MRI staging in rectal cancer

BACKGROUND

Various tumor characteristics might lead to inaccurate local MRI-defined stage of rectal cancer and the purpose of this study was to explore the clinicopathological factors that impact on the precision pre-treatment MRI-defined stage of rectal cancer.

METHODS

A retrospectively analysis was conducted in non-metastatic rectal cancer patients who received radical tumor resection without neoadjuvant treatment during 2007-2015 in the Sixth Affiliated Hospital of Sun Yat-sen University. Clinical T stage and N stage defined by pelvic enhanced MRI and pathological stage were compared and patients were subdivided into accurate-staging, over-staging and under-staging subgroups. Logistic regressions were used to explore risk factors for over-staging or under-staging.

RESULTS

Five hundred fifty-one cases of patients were collected. Among them, 109 cases (19.4%) of patients were over-T-staged and 50 cases (8.9%) were under-T-staged, while 78 cases (13.9%) were over-N-staged and 75 cases (13.3%) were under-N-staged. Logistic regression suggested that pre-operative bowel obstruction was risk factor for over-T-staging (OR = 3.120, 95%CI: 1.662-5.857, P < 0.001) as well as over-N-staging (OR = 3.494, 95%CI: 1.797-6.794, P < 0.001), while mucinous adenocarcinoma was a risk factor for under-N-staging (OR = 4.049, 95%CI: 1.876-8.772, P < 0.001). Patients with larger tumor size were at lower risk for over-T-staging (OR = 0.837, 95%CI: 0.717-0.976, P = 0.024) and higher risk for over-N-staging (OR = 1.434, 95%CI: 1.223-1.680, P < 0.001).

CONCLUSION

Bowel obstruction, mucinous adenocarcinoma and tumor size might have impact on the pre-operative MRI T staging or N staging of rectal cancer. Our results reminded clinicians to assess clinical stage individually in such rectal cancer patients.