The role of MRI pelvimetry in predicting technical difficulty and outcomes of open and minimally invasive total mesorectal excision: a systematic review

Role of pelvimetry in predicting surgical outcomes and morbidity in rectal cancer surgery: A retrospective analysis

BACKGROUND

Rectal cancer has increased in incidence, and surgery remains the cornerstone of multimodal treatment. Pelvic anatomy, particularly a narrow pelvis, poses challenges in rectal cancer surgery, potentially affecting oncological outcomes and postoperative complications.

AIM

To investigate the relationship between radiologically assessed pelvic anatomy and surgical outcomes as well as the impact on local recurrence following rectal cancer surgery.

METHODS

We retrospectively analyzed 107 patients with rectal adenocarcinoma treated with elective rectal surgery between January 1, 2017, and September 1, 2022. Pelvimetric measurements were performed using computed tomography (CT)-based two-dimensional methods (n = 77) by assessing the pelvic inlet area in mm², and magnetic resonance imaging (MRI)-based three-dimensional techniques (n = 52) using the pelvic cavity index (PCI). Patient demographic, clinical, radiological, surgical, and pathological characteristics were collected and analyzed in relation to their pelvimetric data.

RESULTS

When patients were categorized based on CT measurements into narrow and normal/wide pelvis groups, a significant association was observed with male sex, and a lower BMI was more common in the narrow pelvis group (P = 0.002 for both). A significant association was found between a narrow pelvic structure, indicated by low PCI, and increased surgical morbidity (P = 0.049). Advanced age (P = 0.003) and male sex (P = 0.020) were significantly correlated with higher surgical morbidity. Logistic regression analysis identified four parameters that were significantly correlated with local recurrence: older age, early perioperative readmission, longer operation time, and a lower number of dissected lymph nodes (P < 0.05). However, there were no significant differences between the narrow and normal/wide pelvis groups in terms of the operation time, estimated blood loss, or overall local recurrence rate (P > 0.05).

CONCLUSION

MRI-based pelvimetry may be valuable in predicting surgical difficulty and morbidity in rectal cancer surgery, as indicated by the PCI. The observed correlation between low PCI and increased surgical morbidity suggests the potential importance of a preoperative MRI-based pelvimetric evaluation. In contrast, CT-based pelvimetry did not show significant differences in predicting surgical outcomes or cancer recurrence, indicating that the utility of pelvimetry alone may be limited in these respects.

Multi-Modal Machine Learning for Evaluating the Predictive Value of Pelvimetric Measurements (Pelvimetry) for Anastomotic Leakage After Restorative Low Anterior Resection

Background/Objectives: Anastomotic leakage (AL) remains a major complication after restorative rectal cancer surgery, with accurate preoperative risk stratification posing a significant challenge. Pelvic measurements derived from magnetic resonance imaging (MRI) have been proposed as potential predictors of AL, but their clinical utility remains uncertain. Methods: This retrospective, multicenter cohort study analyzed rectal cancer patients undergoing restorative surgery between 2013 and 2021. Pelvic dimensions were assessed using MRI-based pelvimetry. Univariate and multivariate regression analyses identified independent risk factors for AL. Subsequently, machine Learning (ML) models-logistic regression, random forest classifier, and XGBoost-were developed to predict AL using preoperative clinical data alone and in combination with pelvimetry. Model performance was evaluated using F1 scores, with the area under the receiver operating characteristic (ROC-AUC) and precision-recall curves (AUC-PR) as primary metrics. Results: Among 487 patients, the overall AL rate was 14%. Multivariate regression analysis identified distance to the anorectal junction, pelvic inlet width, and interspinous distance as independent risk factors for AL (p < 0.05). The logistic regression model incorporating pelvimetry achieved the highest predictive performance, with a mean ROC-AUC of 0.70 ± 0.09 and AUC-PR of 0.32 ± 0.10. Although predictive models that included pelvic measurements demonstrated higher ROC-AUCs compared to those without pelvimetry, the improvement was not statistically significant. Conclusions: Pelvic dimensions, specifically pelvic inlet and interspinous distance, were independently associated with an increased risk of AL. While ML models incorporating pelvimetry showed only moderate predictive performance, these measurements should be considered in developing clinical prediction tools for AL to enhance preoperative risk stratification.

Deep learning-based pelvimetry in pelvic MRI volumes for pre-operative difficulty assessment of total mesorectal excision

BACKGROUND

Specific pelvic bone dimensions have been identified as predictors of total mesorectal excision (TME) difficulty and outcomes. However, manual measurement of these dimensions (pelvimetry) is labor intensive and thus, anatomic criteria are not included in the pre-operative difficulty assessment. In this work, we propose an automated workflow for pelvimetry based on pre-operative magnetic resonance imaging (MRI) volumes.

METHODS

We implement a deep learning-based framework to measure the predictive pelvic dimensions automatically. A 3D U-Net takes a sagittal T2-weighted MRI volume as input and determines five anatomic landmark locations: promontorium, S3-vertebrae, coccyx, dorsal, and cranial part of the os pubis. The landmarks are used to quantify the lengths of the pelvic inlet, outlet, depth, and the angle of the sacrum. For the development of the network, we used MRI volumes from 1707 patients acquired in eight TME centers. The automated landmark localization and pelvic dimensions measurements are assessed by comparison with manual annotation.

RESULTS

A center-stratified fivefold cross-validation showed a mean landmark localization error of 5.6 mm. The inter-observer variation for manual annotation was 3.7 ± 8.4 mm. The automated dimension measurements had a Spearman correlation coefficient ranging between 0.7 and 0.87.

CONCLUSION

To our knowledge, this is the first study to automate pelvimetry in MRI volumes using deep learning. Our framework can measure the pelvic dimensions with high accuracy, enabling the extraction of metrics that facilitate a pre-operative difficulty assessment of the TME.

Simple pelvimetry predicts the pelvic manipulation time in robot-assisted low and ultra-low anterior resection for rectal cancer

PURPOSE

This study explored the difficulty factors in robot-assisted low and ultra-low anterior resection, focusing on simple measurements of the pelvic anatomy.

METHODS

This was a retrospective analysis of the clinical data of 61 patients who underwent robot-assisted low and ultra-low anterior resection for rectal cancer between October 2018 and April 2023. The relationship between the operative time in the pelvic phase and clinicopathological data, especially pelvic anatomical parameters measured on X-ray and computed tomography (CT), was evaluated. The operative time in the pelvic phase was defined as the time between mobilization from the sacral promontory and rectal resection.

RESULTS

Robot-assisted low and ultra-low anterior resections were performed in 32 and 29 patients, respectively. The median operative time in the pelvic phase was 126 (range, 31-332) min. A multiple linear regression analysis showed that a short distance from the anal verge to the lower edge of the cancer, a narrow area comprising the iliopectineal line, short anteroposterior and transverse pelvic diameters, and a small angle of the pelvic mesorectum were associated with a prolonged operative time in the pelvic phase.

CONCLUSION

Simple pelvic anatomical measurements using abdominal radiography and CT may predict the pelvic manipulation time in robot-assisted surgery for rectal cancer.

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSION

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

CLINICAL RELEVANCE STATEMENT

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

Advantages of robotic surgery for rectal cancer compared to laparoscopic surgery: pelvic anatomy and its impact on urinary dysfunction

The anatomical dimensions and the shape of the pelvis influence surgical difficulty for rectal cancer. Compared to conventional laparoscopic surgery, robot-assisted surgery is expected to improve surgical outcomes due to the multi-joint movement of its surgical instruments. The aim of this study was to investigate the impact of pelvic anatomical indicators on short-term outcomes of patients with rectal cancer. A retrospective analysis was conducted using data from 129 patients with rectal cancer who underwent conventional laparoscopic low anterior resection (L-LAR) or robot-assisted low anterior resection (R-LAR) with total mesorectal excision or tumor-specific mesorectal excision between January 2014 and December 2022. The transverse diameter of the lesser pelvis and the sacral promontory angle were used as indicators of pelvic anatomy. The sacral promontory angle was not associated with age and sex while the pelvic width was smaller in male than in female. The pelvic width did not affect postoperative complications in both L-LAR and R-LAR. In contrast, postoperative urinary dysfunction occurred more frequently in patients with a small sacral promontory angle (p = 0.005) in L-LAR although there was no impact on short-term outcomes in R-LAR. Multivariate analysis demonstrated that a small sacral promontory angle was an independent predictive factor for urinary dysfunction (p = 0.032). Sharp angulation of the sacral promontory was a risk factor for UD after L-LAR. Robot-assisted surgery could overcome anatomical difficulties and reduce the incidence of UD.

Using CT-Based Pelvimetry and Visceral Obesity Measurements to Predict Total Mesorectal Excision Quality for Patients Undergoing Rectal Cancer Surgery

BACKGROUND

A complete total mesorectal excision is the criterion standard in curative rectal cancer surgery. Ensuring quality is challenging in a narrow pelvis, and obesity amplifies technical difficulties. Pelvimetry is the measurement of pelvic dimensions, but its role in gauging preoperatively the difficulty of proctectomy is largely unexplored.

OBJECTIVE

To determine pelvic structural factors associated with incomplete total mesorectal excision after curative proctectomy and build a predictive model for total mesorectal excision quality.

DESIGN

Retrospective cohort study.

SETTING

A quaternary referral center database of patients diagnosed with rectal adenocarcinoma (2009-2017).

PATIENTS

Curative-intent proctectomy for rectal adenocarcinoma.

INTERVENTIONS

All radiological measurements were obtained from preoperative CT images using validated imaging processing software tools. Completeness of total mesorectal excision was obtained from histology reports.

MAIN OUTCOME MEASURES

Ability of radiological pelvimetry and obesity measurements to predict total mesorectal excision quality.

RESULTS

Of the 410 cases meeting inclusion criteria, 362 underwent a complete total mesorectal excision (88%). Multivariable regression identified a deeper sacral curve (per 100 mm 2 [OR: 1.14; 95% CI, 1.06-1.23; p < 0.001]) and a greater transverse distance of the pelvic outlet (per 10 mm [OR:1.41, 95% CI, 1.08-1.84; p = 0.012]) to be independently associated with incomplete total mesorectal excision. An increased area of the pelvic inlet (per 10 cm 2 [OR: 0.85; 95% CI, 0.75-0.97; p = 0.02) was associated with a higher rate of complete mesorectal excision. No difference in visceral obesity ratio and visceral obesity (ratio >0.4 vs <0.4) between BMI (<30 vs ≥30) and sex was identified. A model was built to predict mesorectal quality using the following variables: depth of sacral curve, area of pelvic inlet, and transverse distance of the pelvic outlet.

LIMITATIONS

Retrospective analysis is not controlled for the choice of surgical approach.

CONCLUSIONS

Pelvimetry predicts total mesorectal excision quality in rectal cancer surgery and can alert surgeons preoperatively to cases of unusual difficulty. This predictive model may contribute to treatment strategy and aid in the comparison of outcomes between traditional and novel techniques of total mesorectal excision. See Video Abstract .

USO DE MEDICIONES DE PELVIMETRA Y OBESIDAD VISCERAL BASADAS EN TC PARA PREDECIR LA CALIDAD DE TME EN PACIENTES SOMETIDOS A CIRUGA DE CNCER DE RECTO

ANTECEDENTES:Una escisión mesorrectal total y completa es el estándar de oro en la cirugía curativa del cáncer de recto. Garantizar la calidad es un desafío en una pelvis estrecha y la obesidad amplifica las dificultades técnicas. La pelvimetría es la medición de las dimensiones pélvicas, pero su papel para medir la dificultad preoperatoria de la proctectomía está en gran medida inexplorado.OBJETIVO:Determinar los factores estructurales pélvicos asociados con la escisión mesorrectal total incompleta después de una proctectomía curativa y construir un modelo predictivo para la calidad de la escisión mesorrectal total.DISEÑO:Estudio de cohorte retrospectivo.ÁMBITO:Base de datos de un centro de referencia cuaternario de pacientes diagnosticados con adenocarcinoma de recto (2009-2017).PACIENTES:Proctectomía con intención curativa para adenocarcinoma de recto.INTERVENCIONES:Todas las mediciones radiológicas se obtuvieron a partir de imágenes de TC preoperatorias utilizando herramientas de software de procesamiento de imágenes validadas. La integridad de la escisión mesorrectal total se obtuvo a partir de informes histológicos.PRINCIPALES MEDIDAS DE VALORACIÓN:Capacidad de la pelvimetría radiológica y las mediciones de obesidad para predecir la calidad total de la escisión mesorrectal.RESULTADOS:De los 410 casos que cumplieron los criterios de inclusión, 362 tuvieron una escisión mesorrectal total completa (88%). Una regresión multivariable identificó una curva sacra más profunda (por 100 mm2); OR:1,14,[IC95%:1,06-1,23,p<0,001], y mayor distancia transversal de salida pélvica (por 10mm); OR:1,41, [IC 95%:1,08-1,84,p=0,012] como asociación independiente con escisión mesorrectal total incompleta. Un área aumentada de entrada pélvica (por 10 cm2); OR:0,85, [IC95%:0,75-0,97,p=0,02] se asoció con una mayor tasa de escisión mesorrectal completa. No se identificaron diferencias en la proporción de obesidad visceral y la obesidad visceral (proporción>0,4 vs.<0,4) entre el índice de masa corporal (<30 vs.>=30) o el sexo. Se construyó un modelo para predecir la calidad mesorrectal utilizando variables: profundidad de la curva sacra, área de la entrada pélvica y distancia transversal de la salida pélvica.LIMITACIONES:Análisis retrospectivo no controlado por la elección del abordaje quirúrgico.CONCLUSIONES:La pelvimetría predice la calidad de la escisión mesorrectal total en la cirugía del cáncer de recto y puede alertar a los cirujanos preoperatoriamente sobre casos de dificultad inusual. Este modelo predictivo puede contribuir a la estrategia de tratamiento y ayudar en la comparación de resultados entre técnicas tradicionales y novedosas de escisión mesorrectal total. (Traducción- Dr. Ingrid Melo).

Construction of a nomogram model to predict technical difficulty in performing laparoscopic sphincter-preserving radical resection for rectal cancer

BACKGROUND

Colorectal surgeons are well aware that performing surgery for rectal cancer becomes more challenging in obese patients with narrow and deep pelvic cavities. Therefore, it is essential for colorectal surgeons to have a comprehensive understanding of pelvic structure prior to surgery and anticipate potential surgical difficulties.

AIM

To evaluate predictive parameters for technical challenges encountered during laparoscopic radical sphincter-preserving surgery for rectal cancer.

METHODS

We retrospectively gathered data from 162 consecutive patients who underwent laparoscopic radical sphincter-preserving surgery for rectal cancer. Three-dimensional reconstruction of pelvic bone and soft tissue parameters was conducted using computed tomography (CT) scans. Operative difficulty was categorized as either high or low, and multivariate logistic regression analysis was employed to identify predictors of operative difficulty, ultimately creating a nomogram.

RESULTS

Out of 162 patients, 21 (13.0%) were classified in the high surgical difficulty group, while 141 (87.0%) were in the low surgical difficulty group. Multivariate logistic regression analysis showed that the surgical approach using laparoscopic intersphincteric dissection, intraoperative preventive ostomy, and the sacrococcygeal distance were independent risk factors for highly difficult laparoscopic radical sphincter-sparing surgery for rectal cancer (P < 0.05). Conversely, the anterior-posterior diameter of pelvic inlet/sacrococcygeal distance was identified as a protective factor (P < 0.05). A nomogram was subsequently constructed, demonstrating good predictive accuracy (C-index = 0.834).

CONCLUSION

The surgical approach, intraoperative preventive ostomy, the sacrococcygeal distance, and the anterior-posterior diameter of pelvic inlet/sacrococcygeal distance could help to predict the difficulty of laparoscopic radical sphincter-preserving surgery.

Sex Disparities in Rectal Cancer Surgery: An In-Depth Analysis of Surgical Approaches and Outcomes

Anatomical and physiological differences exist between sex, leading to variations in how diseases, such as rectal cancer, are prevalence and treatment outcomes of diseases including rectal cancer. In particular, in the case of rectal cancer, anatomical differences may be associated with surgical challenges, and these factors are believed to be important contributors to potential disparities in postoperative recovery, associated complications, and oncological outcomes between male and female patients. However, there is still ongoing debate regarding this matter. Significantly, the male pelvic anatomy is distinguished by its narrower dimensions, which can present surgical challenges and impede visual access during operative procedures, rendering it more complex than surgical interventions in the female pelvis. As a result, this anatomical difference leads to a greater occurrence of postoperative complications, such as anastomotic leakage. Moreover, the pelvis houses nerves that are vital for urinary and genital functions, underscoring the need to assess the potential risks of sexual and urinary dysfunction in rectal cancer surgery. These postoperative complications can significantly impact the quality of life; therefore, it is imperative to perform surgery with an understanding of the structural differences between sexes. Therefore, to address the limitations imposed by anatomical structures, new approaches such as robotic surgery, trans-anal total mesorectal excision, and intraoperative neuromonitoring are being introduced. Furthermore, it is essential to conduct research into fundamental mechanisms that may give rise to differences in surgical outcomes and oncological results between sexes. By comprehending the disparities between males and females, we can advance toward personalized treatments. Consequently, this review outlines variations in surgical approaches, complications, and treatments for rectal cancer in male and female patients.

Interpretable machine learning model to predict surgical difficulty in laparoscopic resection for rectal cancer

Background

Laparoscopic total mesorectal excision (LaTME) is standard surgical methods for rectal cancer, and LaTME operation is a challenging procedure. This study is intended to use machine learning to develop and validate prediction models for surgical difficulty of LaTME in patients with rectal cancer and compare these models' performance.

Methods

We retrospectively collected the preoperative clinical and MRI pelvimetry parameter of rectal cancer patients who underwent laparoscopic total mesorectal resection from 2017 to 2022. The difficulty of LaTME was defined according to the scoring criteria reported by Escal. Patients were randomly divided into training group (80%) and test group (20%). We selected independent influencing features using the least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression method. Adopt synthetic minority oversampling technique (SMOTE) to alleviate the class imbalance problem. Six machine learning model were developed: light gradient boosting machine (LGBM); categorical boosting (CatBoost); extreme gradient boost (XGBoost), logistic regression (LR); random forests (RF); multilayer perceptron (MLP). The area under receiver operating characteristic curve (AUROC), accuracy, sensitivity, specificity and F1 score were used to evaluate the performance of the model. The Shapley Additive Explanations (SHAP) analysis provided interpretation for the best machine learning model. Further decision curve analysis (DCA) was used to evaluate the clinical manifestations of the model.

Results

A total of 626 patients were included. LASSO regression analysis shows that tumor height, prognostic nutrition index (PNI), pelvic inlet, pelvic outlet, sacrococcygeal distance, mesorectal fat area and angle 5 (the angle between the apex of the sacral angle and the lower edge of the pubic bone) are the predictor variables of the machine learning model. In addition, the correlation heatmap shows that there is no significant correlation between these seven variables. When predicting the difficulty of LaTME surgery, the XGBoost model performed best among the six machine learning models (AUROC=0.855). Based on the decision curve analysis (DCA) results, the XGBoost model is also superior, and feature importance analysis shows that tumor height is the most important variable among the seven factors.

Conclusions

This study developed an XGBoost model to predict the difficulty of LaTME surgery. This model can help clinicians quickly and accurately predict the difficulty of surgery and adopt individualized surgical methods.

Accuracy and Reliability of Pelvimetry Measures Obtained by Manual or Automatic Labeling of Three-Dimensional Pelvic Models

(1) Background: The morphology of the pelvic cavity is important for decision-making in obstetrics. This study aimed to estimate the accuracy and reliability of pelvimetry measures obtained when radiologists manually label anatomical landmarks on three-dimensional (3D) pelvic models. A second objective was to design an automatic labeling method. (2) Methods: Three operators segmented 10 computed tomography scans each. Three radiologists then labeled 12 anatomical landmarks on the pelvic models, which allowed for the calculation of 15 pelvimetry measures. Additionally, an automatic labeling method was developed based on a reference pelvic model, including reference anatomical landmarks, matching the individual pelvic models. (3) Results: Heterogeneity among landmarks in radiologists' labeling accuracy was observed, with some landmarks being rarely mislabeled by more than 4 mm and others being frequently mislabeled by 10 mm or more. The propagation to the pelvimetry measures was limited; only one out of the 15 measures reported a median error above 5 mm or 5°, and all measures showed moderate to excellent inter-radiologist reliability. The automatic method outperformed manual labeling. (4) Conclusions: This study confirmed the suitability of pelvimetry measures based on manual labeling of 3D pelvic models. Automatic labeling offers promising perspectives to decrease the demand on radiologists, standardize the labeling, and describe the pelvic cavity in more detail.

Tumour, narrow pelvis and surgery specific factors for total mesorectal excision quality and morbidity following rectal cancer resection

BACKGROUND

Pelvic anatomy is critical in challenging rectal resections. This study investigated how pelvic anatomy relates to total mesorectal excision (TME) quality, anastomotic leakage rate, and long-term oncological outcomes.

METHODS

Patients undergoing elective rectal cancer resection from 2008 to 2017 in an Austrian institution were retrospectively reviewed regardless of the surgical approach. CT scans were analysed for pelvic measurements and volumes. The primary outcomes of interest were the correlation between pelvic dimensions and the TME quality and anastomotic leakage. Subanalysis was done by surgical approach (open, laparoscopic, transanal TME). Secondary outcomes were overall and disease-free survivals.

RESULTS

Among 154 eligible patients, 112 were included. The angle between pubic symphysis and promontory significantly correlated with worse TME grades (TME grade 1: mean(s.d.) 102.7(5.7)°; TME grade 2: 92.0(4.4)°; TME grade 3: 91.4(3.6)°; P < 0.001). A significantly lower distance between tumour and circumferential resection margin (CRM) was observed in grade 3 resections, whereas no difference appeared in grade 1 and grade 2 resection (TME grade 1: mean(s.d.) 11.92(9.4) mm; TME grade 2: 10.8(8.1) mm; TME grade 3: 3.1(4.1) mm; P = 0.003). The anastomotic leakage rate was significantly higher in case of a lower CRM (patients with anastomotic leakage: mean(s.d.) 6.8(5.8) mm versus others: 12.6(9.8) mm, P = 0.027), but not associated with pelvimetry measurements. The transanal TME (TaTME) subgroup displayed a wider angle between the pubic symphysis and promontory, younger age and improved TME quality compared to others (respectively, mean TME grades in TaTME versus open versus laparoscopic: 1.0 ± 0.0, 1.5 ± 0.7 and 1.3 ± 0.5, P = 0.013). Finally, oncological survival was not impacted by pelvic measurements or worse TME quality.

CONCLUSION

The angle between the pubic symphysis and promontory and the distance between tumour and CRM were associated with worse TME grades. The anastomotic leakage was associated with a lower CRM but not with pelvimetric measures.

Development and validation of a nomogram for predicting operating time in laparoscopic anterior resection of rectal cancer

Aims

The goal of this study is to create and verify a nomogram estimate operating time in rectal cancer (RC) patients based on clinicopathological factors and MRI/CT measurements before surgery.

Materials and Methods

The nomogram was developed in a cohort of patients who underwent laparoscopic anterior resection (L-AR) for RC. The clinicopathological and pelvis parameters were collected. Risk factors for a long operating time were determined by univariate and multivariate logistic regression analyses, and a nomogram was established with independent risk factors. The performance of the nomogram was evaluated. An independent cohort of consecutive patients served as the validation dataset.

Results

The development group recruited 159 RC patients, while 54 patients were enrolled in the validation group. Independent risk factors identified in multivariate analysis were a distance from the anal verge <5 cm (P = 0.024), the transverse diameter of the pelvic inlet (P < 0.001), mesorectal fat area (P = 0.017), and visceral fat area (P < 0.001). Then, a nomogram was built based on these four independent risk factors. The C-indexes of the nomogram in the development and validation group were 0.886 and 0.855, respectively. And values of AUC were the same with C-indexes in both groups. Besides, the calibration plots showed satisfactory consistency between actual observation and nomogram-predicted probabilities of long operating time.

Conclusions

A nomogram for predicting the risk of long operating duration in L-AR of RC was developed. And the nomogram displayed a good prediction effect and can be utilized as a tool for evaluating operating time preoperatively.

Establishment of Surgical Difficulty Grading System and Application of MRI-Based Artificial Intelligence to Stratify Difficulty in Laparoscopic Rectal Surgery

(1) Background: The difficulty of pelvic operation is greatly affected by anatomical constraints. Defining this difficulty and assessing it based on conventional methods has some limitations. Artificial intelligence (AI) has enabled rapid advances in surgery, but its role in assessing the difficulty of laparoscopic rectal surgery is unclear. This study aimed to establish a difficulty grading system to assess the difficulty of laparoscopic rectal surgery, as well as utilize this system to evaluate the reliability of pelvis-induced difficulties described by MRI-based AI. (2) Methods: Patients who underwent laparoscopic rectal surgery from March 2019 to October 2022 were included, and were divided into a non-difficult group and difficult group. This study was divided into two stages. In the first stage, a difficulty grading system was developed and proposed to assess the surgical difficulty caused by the pelvis. In the second stage, AI was used to build a model, and the ability of the model to stratify the difficulty of surgery was evaluated at this stage, based on the results of the first stage; (3) Results: Among the 108 enrolled patients, 53 patients (49.1%) were in the difficult group. Compared to the non-difficult group, there were longer operation times, more blood loss, higher rates of anastomotic leaks, and poorer specimen quality in the difficult group. In the second stage, after training and testing, the average accuracy of the four-fold cross validation models on the test set was 0.830, and the accuracy of the merged AI model was 0.800, the precision was 0.786, the specificity was 0.750, the recall was 0.846, the F1-score was 0.815, the area under the receiver operating curve was 0.78 and the average precision was 0.69; (4) Conclusions: This study successfully proposed a feasible grading system for surgery difficulty and developed a predictive model with reasonable accuracy using AI, which can assist surgeons in determining surgical difficulty and in choosing the optimal surgical approach for rectal cancer patients with a structurally difficult pelvis.

Advances in pelvic imaging parameters predicting surgical difficulty in rectal cancer

Due to the fixed bony structure of the pelvis, the pelvic operation space is limited, complicating the surgical operation of rectal cancer, especially middle and low rectal cancer. The closer the tumor is to the anal verge, the smaller the operative field and operating space, the longer the operative time, and the greater the incidence of intraoperative side injuries and postoperative complications. To date, there is still no clear definition of a difficult pelvis that affects the surgical operation of rectal cancer. Few related research reports exist in the literature, and views on this aspect are not the same between countries. Therefore, it is particularly important to predict the difficulty of rectal cancer surgery in a certain way before surgery and to select the surgical method most suitable for each case during the treatment of rectal cancer.

Three-dimensional visualization of the total mesorectal excision plane for dissection in rectal cancer surgery and its ability to predict surgical difficulty

Total mesorectal excision (TME) for rectal cancer is often technically challenging. We aimed to develop a method for three-dimensional (3D) visualization of the TME dissection plane and to evaluate its ability to predict surgical difficulty. Sixty-six patients with lower rectal cancer who underwent robot-assisted surgery were retrospectively analyzed. A 3D TME dissection plane image for each case was reconstructed using Ziostation2. Subsequently, a novel index that reflects accessibility to the deep pelvis during TME, namely, the TME difficulty index, was defined and measured. Representative bony pelvimetry parameters and clinicopathological factors were also analyzed. The operative time for TME was used as an indicator of surgical difficulty. Univariate regression analysis revealed that sex, body mass index, mesorectal fat area, and TME difficulty index were associated with the operative time for TME, whereas bony pelvimetry parameters were not. Multivariate regression analysis found that TME difficulty index (β = - 0.398, P = 0.0025) and mesorectal fat area (β = 0.223, P = 0.045) had significant predictability for the operative time for TME. Compared with conventional bony pelvimetry parameters, the TME difficulty index and mesorectal fat area might be more useful in predicting the difficulty of rectal cancer surgery.

Magnetic resonance imaging-based deep learning model to predict multiple firings in double-stapled colorectal anastomosis

BACKGROUND

Multiple linear stapler firings during double stapling technique (DST) after laparoscopic low anterior resection (LAR) are associated with an increased risk of anastomotic leakage (AL). However, it is difficult to predict preoperatively the need for multiple linear stapler cartridges during DST anastomosis.

AIM

To develop a deep learning model to predict multiple firings during DST anastomosis based on pelvic magnetic resonance imaging (MRI).

METHODS

We collected 9476 MR images from 328 mid-low rectal cancer patients undergoing LAR with DST anastomosis, which were randomly divided into a training set (n = 260) and testing set (n = 68). Binary logistic regression was adopted to create a clinical model using six factors. The sequence of fast spin-echo T2-weighted MRI of the entire pelvis was segmented and analyzed. Pure-image and clinical-image integrated deep learning models were constructed using the mask region-based convolutional neural network segmentation tool and three-dimensional convolutional networks. Sensitivity, specificity, accuracy, positive predictive value (PPV), and area under the receiver operating characteristic curve (AUC) was calculated for each model.

RESULTS

The prevalence of ≥ 3 linear stapler cartridges was 17.7% (58/328). The prevalence of AL was statistically significantly higher in patients with ≥ 3 cartridges compared to those with ≤ 2 cartridges (25.0% vs 11.8%, P = 0.018). Preoperative carcinoembryonic antigen level > 5 ng/mL (OR = 2.11, 95%CI 1.08-4.12, P = 0.028) and tumor size ≥ 5 cm (OR = 3.57, 95%CI 1.61-7.89, P = 0.002) were recognized as independent risk factors for use of ≥ 3 linear stapler cartridges. Diagnostic performance was better with the integrated model (accuracy = 94.1%, PPV = 87.5%, and AUC = 0.88) compared with the clinical model (accuracy = 86.7%, PPV = 38.9%, and AUC = 0.72) and the image model (accuracy = 91.2%, PPV = 83.3%, and AUC = 0.81).

CONCLUSION

MRI-based deep learning model can predict the use of ≥ 3 linear stapler cartridges during DST anastomosis in laparoscopic LAR surgery. This model might help determine the best anastomosis strategy by avoiding DST when there is a high probability of the need for ≥ 3 linear stapler cartridges.

BMI and pelvimetry help to predict the duration of laparoscopic resection for low and middle rectal cancer

BACKGROUND

In rectal cancer surgery, recent studies have found associations between clinical factors, especially pelvic parameters, and surgical difficulty; however, their findings are inconsistent because the studies use different criteria. This study aimed to evaluate common clinical factors that influence the operative time for the laparoscopic anterior resection of low and middle rectal cancer.

METHODS

Patients who underwent laparoscopic radical resection of low and middle rectal cancer from January 2018 to December 2020 were retrospectively analyzed and classified according to the operative time. Preoperative clinical and magnetic resonance imaging (MRI)-related parameters were collected. Logistic regression analysis was used to identify factors for predicting the operative time.

RESULTS

In total, 214 patients with a mean age of 60.3 ± 8.9 years were divided into two groups: the long operative time group (n = 105) and the short operative time group (n = 109). Univariate analysis revealed that the male sex, a higher body mass index (BMI, ≥ 24.0 kg/m²), preoperative treatment, a smaller pelvic inlet (< 11.0 cm), a deeper pelvic depth (≥ 10.7 cm) and a shorter intertuberous distance (< 10.1 cm) were significantly correlated with a longer operative time (P < 0.05). However, only BMI (OR 1.893, 95% CI 1.064-3.367, P = 0.030) and pelvic inlet (OR 0.439, 95% CI 0.240-0.804, P = 0.008) were independent predictors of operative time. Moreover, the rate of anastomotic leakage was higher in the long operative time group (P < 0.05).

CONCLUSION

Laparoscopic rectal resection is expected to take longer to perform in patients with a higher BMI or smaller pelvic inlet.

An MRI-based pelvimetry nomogram for predicting surgical difficulty of transabdominal resection in patients with middle and low rectal cancer

Objective

The current work aimed to develop a nomogram comprised of MRI-based pelvimetry and clinical factors for predicting the difficulty of rectal surgery for middle and low rectal cancer (RC).

Methods

Consecutive mid to low RC cases who underwent transabdominal resection between June 2020 and August 2021 were retrospectively enrolled. Univariable and multivariable logistic regression analyses were carried out for identifying factors (clinical factors and MRI-based pelvimetry parameters) independently associated with the difficulty level of rectal surgery. A nomogram model was established with the selected parameters for predicting the probability of high surgical difficulty. The predictive ability of the nomogram model was assessed by the receiver operating characteristic (ROC) curve and decision curve analysis (DCA).

Results

A total of 122 cases were included. BMI (OR = 1.269, p = 0.006), pelvic inlet (OR = 1.057, p = 0.024) and intertuberous distance (OR = 0.938, p = 0.001) independently predicted surgical difficulty level in multivariate logistic regression analysis. The nomogram model combining these predictors had an area under the ROC curve (AUC) of 0.801 (95% CI: 0.719–0.868) for the prediction of a high level of surgical difficulty. The DCA suggested that using the nomogram to predict surgical difficulty provided a clinical benefit.

Conclusions

The nomogram model is feasible for predicting the difficulty level of rectal surgery, utilizing MRI-based pelvimetry parameters and clinical factors in mid to low RC cases.

Evaluation of Nursing Effects of Pelvic Floor Muscle Rehabilitation Exercise on Gastrointestinal Tract Rectal Cancer Patients Receiving Anus-preserving Operation by Intelligent Algorithm-based Magnetic Resonance Imaging

Based on magnetic resonance imaging (MRI) technology under artificial intelligence algorithm, the postoperative nursing effects of pelvic floor muscle rehabilitation exercise on gastrointestinal tract rectal cancer (RC) patients were investigated. A total of 88 patients receiving RC anus-preserving surgery in hospital were selected. The included patients were divided randomly into the experimental group (44 cases) and the control group (44 cases). Patients in the control group engaged in Kegel motion, while patients in the experimental group underwent self-designed comprehensive pelvic floor training. Anorectum function rating scale and quality of life questionnaire for colorectal cancer (EORTC QLQ-CR29) were utilized to compare and analyze anus functions and living quality of patients in the two groups. Besides, all patients in two groups received MRI examinations, and images were processed by a convolutional neural network (CNN) algorithm. The results showed that in MRI images, there were significant signal differences between lesion tissues and normal tissues. After being processed by an artificial intelligence algorithm, the definition of MRI images was remarkably enhanced with clearer lesion edges. The quality of images was also significantly improved. Besides, the comparison of anus functions of patients in two groups showed that the differences demonstrated statistical meaning after the intervention (P < 0.05). In conclusion, artificial intelligence algorithm-based MRI and comprehensive pelvic floor muscle exercise showed significant application prospects and values in the recovery of patients' intestinal functions after RC anus-preserving surgery.

Can MRI pelvimetry predict the technical difficulty of laparoscopic rectal cancer surgery?

PURPOSE

Selection of an open or minimally invasive approach to total mesorectal excision (TME) is generally based on surgeon preference and an intuitive assessment of patient characteristics but there consensus on criteria to predict surgical difficulty. Pelvimetry has been used to predict the difficult surgical pelvis, typically using only bony landmarks. This study aimed to assess the relationship between pelvic soft tissue measurements on preoperative MRI and surgical difficulty.

METHODS

Preoperative MRIs for patients undergoing laparoscopic rectal resection in the Australasian Laparoscopic Cancer of the Rectum Trial (ALaCaRT) were retrospectively reviewed by two blinded surgeons and pelvimetric variables measured. Pelvimetric variables were analyzed for predictors of successful resection of the rectal cancer, defined by clear circumferential and distal resection margins and completeness of TME.

RESULTS

There was no association between successful surgery and any measurement of distance, area, or ratio. However, the was a strong association between the primary outcome and the estimated total pelvic volume on adjusted logistic regression analysis (OR = 0.99, P = 0.01). For each cubic centimeter increase in the pelvic volume, there was a 1% decrease in the odds of successful laparoscopic rectal cancer surgery. Intuitive prediction of unsuccessful surgery was correct in 43% of cases, and correlation between surgeons was poor (ICC = 0.18).

CONCLUSIONS

A surgeon's intuitive assessment of the difficult pelvis, based on visible MRI assessment, is not a reliable predictor of successful laparoscopic surgery. Further assessment of pelvic volume may provide an objective method of defining the difficult surgical pelvis.

Concepts and prospects of minimally invasive colorectal cancer surgery

It is important that clinical radiologists understand and appreciate the minimally invasive surgery (MIS) options available to surgeons. Operative technologies are constantly evolving, and accurate, informed interpretation of clinical imaging is essential for optimum surgical management. Concurrent advancements in both MIS and radiological staging have certainly improved treatment decisions and outcomes. This article outlines the history, current concepts, evolving techniques, and future prospects of MIS as it pertains to colorectal cancer surgery.

Robotic transanal total mesorectal excision: Is the future now?

Total mesorectal excision (TME) is the standard surgical treatment for the curative radical resection of rectal cancers. Minimally invasive TME has been gaining ground favored by the continuous technological advancements. New procedures, such as transanal TME (TaTME), have been introduced to overcome some technical limitations, especially in low rectal tumors, obese patients, and/or narrow pelvis. The earliest TaTME reports showed promising results when compared with the conventional laparoscopic TME. However, recent publications raised concerns regarding the high rates of anastomotic leaks or local recurrences observed in national series. Robotic TaTME (R-TaTME) has been proposed as a novel technique incorporating the potential benefits of a perineal dissection together with precise control of the distal margins, and also offers all those advantages provided by the robotic technology in terms of improved precision and dexterity. Encouraging short-term results have been reported for R-TaTME, but further studies are needed to assess the real role of the new technique in the long-term oncological or functional outcomes. The present review aims to provide a general overview of R-TaTME by analyzing the body of the available literature, with a special focus on the potential benefits, harms, and future perspectives for this novel approach.

Pelvimetric and Nutritional Factors Predicting Surgical Difficulty in Laparoscopic Resection for Rectal Cancer Following Preoperative Chemoradiotherapy

AIM

Laparoscopic total mesorectal excision (LaTME) following preoperative chemoradiotherapy (PCRT) in locally advanced rectal cancer (LARC) is technically demanding. The present study is intended to evaluate predictive factors of surgical difficulty of LaTME following PCRT by using pelvimetric and nutritional factors.

METHOD

Consecutive LARC patients receiving LaTME after PCRT were included. Surgical difficulty was classified based upon intraoperative (operation time, blood loss, and conversion) and postoperative outcomes (postoperative hospital stay and morbidities). Pelvimetry was performed using preoperative T2-weighted MRI. Nutritional factors such as albumin-to-globulin ratio (AGR) and prognostic nutritional index (PNI) were calculated. Multivariable logistic analysis was used to identify predictors of high surgical difficulty. A predictive nomogram was developed and validated internally.

RESULTS

Among 294 patients included, 36 (12.4%) patients were graded as high surgical difficulty. Logistic regression analysis demonstrated that previous abdominal surgery (OR = 6.080, P = 0.001), tumor diameter (OR = 1.732, P = 0.003), intersphincteric resection (vs. low anterior resection, OR = 13.241, P < 0.001), interspinous distance (OR = 0.505, P = 0.009), and preoperative AGR (OR = 0.041, P = 0.024) were independently predictive of high surgical difficulty of LaTME after PCRT. Then, a predictive nomogram was built (C-index = 0.867).

CONCLUSION

Besides previous abdominal surgery, type of surgery (intersphincteric resection), tumor diameter, and interspinous distance, we found that preoperative AGR could be useful for the prediction of surgical difficulty of LaTME after PCRT. A predictive nomogram for surgical difficulty may aid in planning an appropriate approach for rectal cancer surgery after PCRT.

3D pelvimetry and biometric measurements: a surgical perspective for colorectal resections

PURPOSE

Male sex, high BMI, narrow pelvis, and bulky mesorectum were acknowledged as clinical variables correlated with a difficult pelvic dissection in colorectal surgery. This paper aimed at comparing pelvic biometric measurements in female and male patients and at providing a perspective on how pelvimetry segmentation may help in visualizing mesorectal distribution.

METHODS

A 3D software was used for segmentation of DICOM data of consecutive patients aged 60 years, who underwent elective abdominal CT scan. The following measurements were estimated: pelvic inlet, outlet, and depth; pubic tubercle height; distances from the promontory to the coccyx and to S3/S4; distance from S3/S4 to coccyx's tip; ischial spines distance; pelvic tilt; offset angle; pelvic inlet angle; angle between the inlet/sacral promontory/coccyx; angle between the promontory/coccyx/pelvic outlet; S3 angle; and pelvic inlet to pelvic depth ratio. The measurements were compared in males and females using statistical analyses.

RESULTS

Two-hundred patients (M/F 1:1) were analyzed. Out of 21 pelvimetry measurements, 19 of them documented a significant mean difference between groups. Specifically, female patients had a significantly wider pelvic inlet and outlet but a shorter pelvic depth, and promontory/sacral/coccyx distances, resulting in an augmented inlet/depth ratio when comparing with males (p < 0.0001). The sole exceptions were the straight conjugate (p = 0.06) and S3 angle (p = 0.17). 3D segmentation provided a perspective of the mesorectum distribution according to the pelvic shape.

CONCLUSION

Significant differences in the structure of pelvis exist in males and females. Surgeons must be aware of the pelvic shape when approaching the rectum.