Radiomics Approach Outperforms Diameter Criteria for Predicting Pathological Lateral Lymph Node Metastasis After Neoadjuvant (Chemo)Radiotherapy in Advanced Low Rectal Cancer

Diagnostic accuracy of lateral lymph node metastasis for locally advanced rectal cancer after neoadjuvant therapy: a systematic review and meta-analysis

BACKGROUND

The optimal criteria for lateral lymph node dissection (LLND) in rectal cancer following neoadjuvant therapy remain undefined. This systematic review and meta-analysis evaluated the diagnostic accuracy of lateral lymph node metastasis (LLNM) to refine criteria for selective LLND.

RESEARCH DESIGN AND METHODS

A systematic search of PubMed, Embase, and the Cochrane Library (10 August 2024) identified studies assessing magnetic resonance imaging (MRI)-based LLNM detection in patients with rectal cancer who underwent neoadjuvant therapy and radical surgery. Studies reporting MRI-based LLNM assessments with pathological confirmation were included. Non-English studies, reviews, case reports, and those lacking lymph node size data were excluded. The risk of bias was assessed using QUADAS-2. Pooled sensitivity, specificity, and diagnostic odds ratios were estimated using hierarchical summary receiver operating characteristic curve (HSROC) analysis.

RESULTS

Eleven studies met the inclusion criteria. All used MRI-based size assessments. The pooled sensitivity and specificity were 0.776 (95% CI: 0.639-0.872) and 0.694 (95% CI: 0.541-0.813), respectively, with an HSROC area under the curve (AUC) of 0.801.

CONCLUSIONS

MRI is the most widely used modality for diagnosing LLNM in rectal cancer patients who have undergone neoadjuvant therapy, with size criteria being the most commonly applied.

REGISTRATION

PROSPERO (CRD42024578499).

Computed Tomography Colonography: 2025 Update

Colorectal cancer (CRC) is the second leading cause of cancer-related deaths in the United States. Most cases arise from polyps, which can be detected and removed before becoming cancerous. Computed tomography colonography (CTC), also known as virtual colonoscopy, was first introduced in 1994 as a minimally invasive method for CRC screening and diagnosis. This 2025 update on CTC will focus on (1) techniques and dose reduction strategies, (2) image display methods, (3) reporting and classification systems, (4) tumor staging capabilities, (5) integration of advanced imaging techniques, and (6) cost-effectiveness and reimbursement.

Predicting lateral pelvic lymph node metastasis in rectal cancer patients using MRI radiomics: a multicenter retrospective study

MRI has relatively low sensitivity and specificity in detecting lymph node metastases. This study aimed to develop and validate an MRI radiomics-based model for predicting lateral pelvic lymph node (LPLN) metastasis in rectal cancer patients who underwent LPLN dissection, and to compare its performance with that of radiologists. This multicenter retrospective study included 336 rectal cancer patients (199 men; mean age, 58.9 years ± 11.1 [standard deviation]) who underwent LPLN dissection. Patients were divided into development (n = 190) and validation (n = 146) cohorts. Radiomics features were extracted from MR images, and the Least Absolute Shrinkage and Selection Operator regression was used to construct radiomics and clinical-radiomics models. Model performance was compared with radiologists using receiver operating characteristic (ROC) analysis. Malignant LPLN was diagnosed in 32.4% of the development cohort (65/190) and 32.9% of the validation cohort (48/146) (P = 0.798). Seven radiomics features and two clinical features were selected. The radiomics and clinical-radiomics models demonstrated area under the curves (AUCs) of 0.819 and 0.830 in the development cohort and 0.821 and 0.829 in the validation cohort, respectively. The optimal cut-off (- 0.47) yielded sensitivities of 72.3% and 45.8% and specificities of 82.4% and 87.8% in the development and validation cohorts, respectively. Decision curve analysis indicated no additional net benefit from the clinical-radiomics model compared to the radiomics-only model. Radiologists' AUCs were significantly lower than that of the radiomics model (0.842) and improved with radiomics probability scores (0.734 vs. 0.801; 0.668 vs. 0.791). The MRI-based radiomics model significantly improves the prediction of LPLN metastasis in rectal cancer, outperforming conventional criteria used by radiologists.Trial registration: Retrospectively registered.

Lateral Pelvic Recurrence in Rectal Cancer Is Not Local Recurrence but Lymphatic Metastasis

Objectives

Complete resection of advanced rectal cancer is challenging, with local recurrence rates ranging from 4% to 12%. Local recurrence is often categorized as central, anastomotic, or lateral, with lateral lymph node (LLN) metastasis being the primary driver of lateral recurrence. Although preoperative radiotherapy effectively manages nonlateral recurrences, it is less effective for lateral recurrences, and LLN dissection significantly reduces lateral recurrence rates. This study aimed to clarify the clinicopathological characteristics associated with lateral and nonlateral recurrences.

Methods

We retrospectively analyzed 232 patients (156 males and 76 females; median age, 64 years) who underwent preoperative radiotherapy followed by curative-intent surgery for clinical T3/4 rectal adenocarcinoma located below the peritoneal reflection between April 2010 and December 2017. In total, 40% of the patients underwent LLN dissection. Univariate and multivariate analyses of clinicopathological data were performed to identify the independent risk factors for lateral and nonlateral recurrences.

Results

Local recurrence occurred in 19 (8%) patients: 7 had lateral recurrence, 13 had nonlateral recurrence, and 1 had both. Multivariate analysis identified mesorectal lymph node metastasis as a significant risk factor for lateral recurrence, whereas positive circumferential resection margin was a significant risk factor for nonlateral recurrence.

Conclusions

The identification of different risk factors for lateral and nonlateral recurrence suggests that lateral recurrence is more strongly associated with lymphatic permeation than nonlateral recurrence. These findings highlight the importance of LLN dissection in minimizing the risk of lateral recurrence.

Factors associated with lymph node metastasis and survival in T2 colon cancer

PURPOSE

This study aimed to explore the clinical factors associated with lymph node metastasis (LNM) and survival in T2 colon cancer.

METHOD

Patients with T2 colon cancer and receiving radical surgery from 2017 to 2021 in our hospital were retrospectively enrolled. Patients were divided into two groups according to the LN status (LNM, non-LNM). The demographic, radiological, pathological, and survival data were collected and analyzed. Logistic regression was used to find the factors associated with LNM, and cox regression was adopted to identify factors contributing to poor survival. All the data analysis was performed by SPSS 22.0 and R.

RESULTS

A total of 150 patients were included in this study, among them thirty were with LNM (20%). The LNM group had significantly higher incidence of lymph-vascular invasion (LVI) and perineural invasion. Besides, positive LNs had more proportion of irregular margin (P < 0.001) and heterogeneity (P < 0.001) than the negative ones. The multivariate analysis indicated that LVI and heterogeneity of LN were independent risk factors of LNM in T2 colon cancer. The disease-free survival (DFS) was 80% and 93.3% in the LNM and non-LNM group (P = 0.02), respectively. Besides, the overall survival (OS) was 92.9% and 95% in the LNM and non-LNM group (P = 0.103), respectively. The results indicated that elevated CA199 value and LNM were independent risk factors contributing to poorer OS and DFS.

CONCLUSION

The current data indicated LVI and LN heterogeneity were independent risk factors of LNM in T2 colon cancer. More extended surgery should be considered when these factors were detected.

Features of Lateral Pelvic Lymph Nodes Associated With Pathological Involvement After Total Neoadjuvant Therapy in Patients Undergoing Lateral Pelvic Lymph Node Dissection

BACKGROUND

There is a lack of consensus regarding treating involved lateral pelvic lymph nodes in rectal cancer.

OBJECTIVE

This study aimed to evaluate the clinical and MRI-based factors associated with pathological lateral pelvic lymph node metastasis in patients undergoing total neoadjuvant therapy and lateral pelvic lymph node dissection.

DESIGN

This is a retrospective study.

SETTINGS

This study was conducted at a single comprehensive cancer center.

PATIENTS

A total of 107 patients with advanced low rectal cancer with pretreatment enlarged lateral pelvic lymph nodes (≥7 mm in long axis) undergoing total neoadjuvant therapy with long-course chemoradiotherapy, followed by total mesorectal excision and lateral pelvic lymph node dissection, were enrolled.

MAIN OUTCOME MEASURES

Pathological lateral pelvic lymph node metastasis and survival.

RESULTS

Among 107 patients, short-axis lateral node diameter at baseline was <7 mm in 48 patients and ≥7 mm in 59 patients. The ≥7 mm group showed significantly higher rates of pathological lateral pelvic lymph node metastasis (44.1% vs 2.1%; p < 0.0001). In this group, pathological lateral pelvic lymph node metastasis was independently associated with pretreatment malignant features and posttreatment short-axis diameter ≥4 mm. Five-year relapse-free survival was significantly lower in patients with posttreatment lateral node diameter ≥4 mm than those with <4 mm (71.1% vs 86.2%, p = 0.0364). Patients with pathological lateral pelvic lymph node metastasis had significantly lower overall survival, relapse-free survival, and local recurrence-free survival rates.

LIMITATIONS

Selection bias exists in a retrospective analysis.

CONCLUSIONS

Pathological lateral pelvic lymph node metastasis is rare in patients with pretreatment short-axis diameter <7 mm. In patients with pretreatment short-axis diameter ≥7 mm, pretreatment malignant features and posttreatment short-axis diameter are both associated with pathological lateral pelvic lymph node metastasis. These factors should be considered when deciding whether to proceed with lateral pelvic lymph node dissection after total neoadjuvant therapy. See Video Abstract.

CARACTERSTICAS DE LOS GANGLIOS LINFTICOS PLVICOS LATERALES ASOCIADOS CON AFECTACIN PATOLGICA DESPUS DE LA TERAPIA NEOADYUVANTE TOTAL EN PACIENTES SOMETIDOS A DISECCIN LATERAL DE GANGLIOS LINFTICOS PLVICOS

ANTECEDENTES:No existe consenso sobre el tratamiento de los ganglios linfáticos pélvicos laterales afectados en el cáncer rectal.OBJETIVO:Este estudio tuvo como objetivo evaluar los factores clínicos y basados en imágenes de resonancia magnética asociados con la metástasis patológica de los ganglios linfáticos pélvicos laterales en pacientes sometidos a terapia neoadyuvante total y disección lateral de ganglios linfáticos pélvicos.DISEO:Este es un estudio retrospectivo.EORNO CLINICO:Este estudio se llevó a cabo en un solo centro oncológico integral.PACIENTES:Se inscribieron 107 pacientes con cáncer rectal bajo avanzado con ganglios linfáticos pélvicos laterales agrandados antes del tratamiento (≥7 mm en el eje largo) sometidos a terapia neoadyuvante total con quimiorradioterapia de larga duración, seguida de escisión mesorrectal total y disección de ganglios linfáticos pélvicos laterales.PRINCIPALES MEDIDAS DE RESULTADOS:Metástasis patológica de ganglios linfáticos pélvicos laterales y supervivencia.RESULTADOS:Entre 107 pacientes, 48 tenían un diámetro ganglionar lateral en el eje corto <7 mm al inicio, mientras que 59 tenían ≥7 mm. El grupo de ≥7 mm mostró tasas significativamente más altas de metástasis patológica de los ganglios linfáticos pélvicos laterales (44,1% vs 2,1%; p < 0,0001). En este grupo, la metástasis patológica de los ganglios linfáticos pélvicos laterales se asoció de forma independiente con características malignas previas al tratamiento y un diámetro ganglionar lateral posterior al tratamiento ≥4 mm. La supervivencia sin recidiva a los cinco años fue significativamente menor en pacientes con un diámetro ganglionar lateral posterior al tratamiento ≥4 mm que en aquellos con un diámetro <4 mm (71,1% vs 86,2%, p = 0,0364). Los pacientes con la metástasis patológica de los ganglios linfáticos pélvicos laterales tuvieron tasas de supervivencia global, supervivencia sin recidiva y supervivencia sin recurrencia local significativamente más bajas.LIMITACIONES:Existe sesgo de selección en un análisis retrospectivo.CONCLUSIONES:La metástasis patológica de los ganglios linfáticos pélvicos laterales es poco frecuente en pacientes con un diámetro del eje corto previo al tratamiento <7 mm. En pacientes con un diámetro del eje corto previo al tratamiento ≥7 mm, las características malignas previas al tratamiento y el diámetro del eje corto posterior al tratamiento se asocian con metástasis patológica de los ganglios linfáticos pélvicos laterales. Estos factores deben tenerse en cuenta al decidir si se debe proceder a la disección de los ganglios linfáticos pélvicos laterales después de la terapia neoadyuvante total. (Traducción- Dr. Francisco M. Abarca-Rendon).

MRI-based Radiomics Model for Preoperative Prediction of Lateral Pelvic Lymph Node Metastasis in Locally Advanced Rectal Cancer

RATIONALE AND OBJECTIVES

To develop a magnetic resonance imaging (MRI)-based radiomics model for preoperative prediction of lateral pelvic lymph node (LPLN) metastasis (LPLNM) in patients with locally advanced rectal cancer MATERIALS AND METHODS: We retrospectively enrolled 263 patients with rectal cancer who underwent total mesorectal excision and LPLN dissection. Radiomics features from the primary lesion and LPLNs on baseline MRI images were utilized to construct a radiomics model, and their radiomics scores were combined to develop a radiomics scoring system. A clinical prediction model was developed using logistic regression. A hybrid predicting model was created through multivariable logistic regression analysis, integrating the radiomics score with significant clinical risk factors (baseline Carcinoembryonic Antigen (CEA), clinical circumferential resection margin status, and the short axis diameter of LPLN). This hybrid model was presented with a hybrid clinical-radiomics nomogram, and its calibration, discrimination, and clinical usefulness were assessed.

RESULTS

A total of 148 patients were included in the analysis and randomly divided into a training cohort (n = 104) and an independent internal testing cohort (n = 44). The hybrid clinical-radiomics model exhibited the highest discrimination, with an area under the receiver operating characteristic (AUC) of 0.843 [95% confidence interval (CI), 0.706-0.968] in the testing cohort compared to the clinical model [AUC (95% CI) = 0.772 (0.589-0.856)] and radiomics model [AUC (95% CI) = 0.731 (0.613-0.849)]. The hybrid prediction model also demonstrated good calibration, and decision curve analysis confirmed its clinical usefulness.

CONCLUSION

This study developed a hybrid MRI-based radiomics model that incorporates a combination of radiomics score and significant clinical risk factors. The proposed model holds promise for individualized preoperative prediction of LPLNM in patients with locally advanced rectal cancer.

DATA AVAILABILITY STATEMENT

The data presented in this study are available on request from the corresponding author.

Management of internal iliac and obturator lymph nodes in mid-low rectal cancer

In rectal cancer treatment, the diagnosis and management of lateral pelvic lymph nodes (LLN) are critical for preventing local recurrence. Over time, scholars have reached a consensus: when imaging suggests LLN metastasis, combining neoadjuvant chemoradiotherapy (nCRT) with selective LLN dissection (LLND) can mitigate the risk of recurrence. Selective LLND typically encompasses lymph nodes in the internal iliac and obturator regions. Recent studies emphasize distinctions between internal iliac and obturator lymph nodes regarding prognosis and treatment outcomes, prompting the need for differentiated diagnostic and treatment approaches.

Application of radiomics for preoperative prediction of lymph node metastasis in colorectal cancer: a systematic review and meta-analysis

BACKGROUND

Colorectal cancer (CRC) stands as the third most prevalent cancer globally, projecting 3.2 million new cases and 1.6 million deaths by 2040. Accurate lymph node metastasis (LNM) detection is critical for determining optimal surgical approaches, including preoperative neoadjuvant chemoradiotherapy and surgery, which significantly influence CRC prognosis. However, conventional imaging lacks adequate precision, prompting exploration into radiomics, which addresses this shortfall by converting medical images into reproducible, quantitative data.

METHODS

Following PRISMA, Supplemental Digital Content 1 (http://links.lww.com/JS9/C77) and Supplemental Digital Content 2 (http://links.lww.com/JS9/C78), and AMSTAR-2 guidelines, Supplemental Digital Content 3 (http://links.lww.com/JS9/C79), we systematically searched PubMed, Web of Science, Embase, Cochrane Library, and Google Scholar databases until 11 January 2024, to evaluate radiomics models' diagnostic precision in predicting preoperative LNM in CRC patients. The quality and bias risk of the included studies were assessed using the Radiomics Quality Score (RQS) and the modified Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Subsequently, statistical analyses were conducted.

RESULTS

Thirty-six studies encompassing 8039 patients were included, with a significant concentration in 2022-2023 (20/36). Radiomics models predicting LNM demonstrated a pooled area under the curve (AUC) of 0.814 (95% CI: 0.78-0.85), featuring sensitivity and specificity of 0.77 (95% CI: 0.69, 0.84) and 0.73 (95% CI: 0.67, 0.78), respectively. Subgroup analyses revealed similar AUCs for CT and MRI-based models, and rectal cancer models outperformed colon and colorectal cancers. Additionally, studies utilizing cross-validation, 2D segmentation, internal validation, manual segmentation, prospective design, and single-center populations tended to have higher AUCs. However, these differences were not statistically significant. Radiologists collectively achieved a pooled AUC of 0.659 (95% CI: 0.627, 0.691), significantly differing from the performance of radiomics models (P<0.001).

CONCLUSION

Artificial intelligence-based radiomics shows promise in preoperative lymph node staging for CRC, exhibiting significant predictive performance. These findings support the integration of radiomics into clinical practice to enhance preoperative strategies in CRC management.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

Preoperative lateral lymph node features and impact on local recurrence after neoadjuvant chemoradiotherapy and total mesorectal excision for locally advanced rectal cancer: results from a multicentre international cohort study

AIM

Locally advanced rectal cancer (LARC) is commonly treated with neoadjuvant chemoradiotherapy (nCRT) and total mesorectal excision (TME) to reduce local recurrence (LR) and improve survival. However, LR, particularly associated with lateral lymph node (LLN) involvement, remains a concern. The aim of this study was to investigate preoperative factors associated with LLN involvement and their impact on LR rates in LARC patients undergoing nCRT and curative surgery.

METHOD

This multicentre retrospective study, including four academic high-volume institutions, involved 301 consecutive adult LARC patients treated with nCRT and curative surgery between January 2014 and December 2019 who did not undergo lateral lymph node dissection (LLND). Baseline and restaging pelvic MRIs were evaluated for suspicious LLNs based on institutional criteria. Patients were divided into two groups: cLLN+ (positive nodes) and cLLN- (no suspicious nodes). Primary outcome measures were LR and lateral local recurrence (LLR) rates at 3 years.

RESULTS

Among the cohort, 15.9% had suspicious LLNs on baseline MRI, and 9.3% had abnormal LLNs on restaging MRI. At 3 years, LR and LLR rates were 4.0% and 1.0%, respectively. Ten out of 12 (83.3%) patients with LR showed no suspicious LLNs at the baseline MRI. Abnormal LLNs on MRI were not independent risk factors for LR, distant recurrence or disease-free survival.

CONCLUSION

Abnormal LLNs on baseline and restaging MRI assessment did not impact LR and LLR rates in this cohort of patients with LARC submitted to nCRT and curative TME surgery.

A radiomics model based on T2WI and clinical indexes for prediction of lateral lymph node metastasis in rectal cancer

OBJECTIVE

The aim of this study was to explore the clinical value of a radiomics prediction model based on T2-weighted imaging (T2WI) and clinical indexes in predicting lateral lymph node (LLN) metastasis in rectal cancer patients.

METHODS

This was a retrospective analysis of 106 rectal cancer patients who had undergone LLN dissection. The clinical risk factors for LLN metastasis were selected by multivariable logistic regression analysis of the clinical indicators of the patients. The LLN radiomics features were extracted from the pelvic T2WI of the patients. The least absolute shrinkage and selection operator algorithm and backward stepwise regression method were adopted for feature selection. Three LLN metastasis prediction models were established through logistic regression analysis based on the clinical risk factors and radiomics features. Model performance was assessed in terms of discriminability and decision curve analysis in the training, verification and test sets.

RESULTS

The model based on the combined T2WI radiomics features and clinical risk factors demonstrated the highest accuracy, surpassing the models based solely on either T2WI radiomics features or clinical risk factors. Specifically, the model achieved an AUC value of 0.836 in the test set. Decision curve analysis revealed that this model had the greatest clinical utility for the vast majority of the threshold probability range from 0.4 to 1.0.

CONCLUSION

Combining T2WI radiomics features with clinical risk factors holds promise for the noninvasive assessment of the biological characteristics of the LLNs in rectal cancer, potentially aiding in therapeutic decision-making and optimizing patient outcomes.

Prediction of Tumor PD-L1 Expression in Resectable Non-Small Cell Lung Cancer by Machine Learning Models Based on Clinical and Radiological Features: Performance Comparison With Preoperative Biopsy

OBJECTIVE

We investigated if PD-L1 expression can be predicted by machine learning using clinical and imaging features.

METHODS

We included 117 patients with c-stage I/II non-small cell lung cancer who underwent radical resection. A total of 3951 radiomic features were extracted by defining the tumor (within tumor contour), rim (contour ±3 mm) and exterior (contour +10 mm) on preoperative contrast computed tomography. After feature selection by Boruta algorithm, prediction models of tumor PD-L1 expression (22C3: ≥1%, <1%) of resected specimens were constructed using Random Forest: radiomics, clinical, and combined models. Their performance was evaluated by 5-fold cross-validation, and AUCs were compared using Delong test. Next, study groups were categorized as patients without biopsy (training set), and those with biopsy (test set). Predictive ability of biopsy was compared to each prediction model.

RESULTS

Of 117 patients (66 ± 10 years old, 48% male), 33 (28.2%) had PD-L1≥1%. Mean AUC of PD-L1≥1% for the validation set in radiomics, clinical, and combined models were 0.80, 0.80, and 0.83 (P = .32 vs. clinical model), respectively. The diagnosis of malignancy was made in 22 of 38 (58%) patients with attempted biopsies, and PD-L1 was measurable in 19 of 38 (50%) patients. Diagnostic accuracies of PD-L1≥1% from 19 determinable biopsies and 38 all attempted biopsies were 0.68 and 0.34, respectively. These were out performed by machine learning: 0.71, 0.71, and 0.74 for radiomics, clinical, and combined models, respectively.

CONCLUSIONS

Our machine learning could be an adjunctive tool in estimating PD-L1 expression prior to neoadjuvant treatment, particularly when PD-L1 is indeterminable with biopsy.

Diagnostic value of a radiomics model based on CT and MRI for prediction of lateral lymph node metastasis of rectal cancer

This study aimed to develop a radiomics model for predicting lateral lymph node (LLN) metastasis in rectal cancer patients using MR-T2WI and CT images, and assess its clinical value. This prospective study included rectal cancer patients with complete MR-T2WI and portal enhanced CT images who underwent LLN dissection at Tianjin Union Medical Center between June 2017 and November 2022. Primary lesions and LLN were segmented using 3D slicer. Radiomics features were extracted from the region of interest using pyradiomics in Python. Least absolute shrinkage and selection operator algorithm and backward stepwise regression were employed for feature selection. Three LLN metastasis radiomics prediction models were established via multivariable logistic regression analysis. The performance of the model was evaluated using receiver operating characteristic curve analysis, and the area under the curve (AUC), sensitivity, specificity were calculated for the training, validation, and test sets. A nomogram was constructed for visualization, and decision curve analysis (DCA) was performed to evaluate clinical value. We included 94 eligible patients in the analysis. For each patient, we extracted a total of 1344 radiomics features. The CT combined with MR-T2WI model had the highest AUC for all sets compared to CT and MR-T2WI models. AUC values for the CT combined with MR-T2WI model in the training, validation, and test sets were 0.957, 0.901, and 0.936, respectively. DCA revealed high prediction value for the combined MR-T2WI and CT model. A radiomics model based on CT and MR-T2WI data effectively predicted LLN metastasis in rectal cancer patients preoperatively.

A nomogram for preoperative differentiation of tumor deposits from lymph node metastasis in rectal cancer: A retrospective study

The objective is to develop and validate a combined model for noninvasive preoperative differentiating tumor deposits (TDs) from lymph node metastasis (LNM) in patients with rectal cancer (RC). A total of 204 patients were enrolled and randomly divided into 2 sets (training and validation set) at a ratio of 8:2. Radiomics features of tumor and peritumor fat were extracted by using Pyradiomics software from the axial T2-weighted imaging of MRI. Rad-score based on extracted Radiomics features were calculated by combination of feature selection and the machine learning method. Factors (Rad-score, laboratory test factor, clinical factor, traditional characters of tumor on MRI) with statistical significance were integrated to build a combined model. The combined model was visualized by a nomogram, and its distinguish ability, diagnostic accuracy, and clinical utility were evaluated by the receiver operating characteristic curve (ROC) analysis, calibration curve, and clinical decision curve, respectively. Carbohydrate antigen (CA) 19-9, MRI reported node stage (MRI-N stage), tumor volume (cm3), and Rad-score were all included in the combined model (odds ratio = 3.881 for Rad-score, 2.859 for CA19-9, 0.411 for MRI-N stage, and 1.055 for tumor volume). The distinguish ability of the combined model in the training and validation cohorts was area under the summary receiver operating characteristic curve (AUC) = 0.863, 95% confidence interval (CI): 0.8-0.911 and 0.815, 95% CI: 0.663-0.919, respectively. And the combined model outperformed the clinical model in both training and validation cohorts (AUC = 0.863 vs 0.749, 0.815 vs 0.627, P = .0022, .0302), outperformed the Rad-score model only in training cohorts (AUC = 0.863 vs 0.819, P = .0283). The combined model had highest net benefit and showed good diagnostic accuracy. The combined model incorporating Rad-score and clinical factors could provide a preoperative differentiation of TD from LNM and guide clinicians in making individualized treatment strategy for patients with RC.

A Deep Learning Framework with Explainability for the Prediction of Lateral Locoregional Recurrences in Rectal Cancer Patients with Suspicious Lateral Lymph Nodes

Malignant lateral lymph nodes (LLNs) in low, locally advanced rectal cancer can cause (ipsi-lateral) local recurrences ((L)LR). Accurate identification is, therefore, essential. This study explored LLN features to create an artificial intelligence prediction model, estimating the risk of (L)LR. This retrospective multicentre cohort study examined 196 patients diagnosed with rectal cancer between 2008 and 2020 from three tertiary centres in the Netherlands. Primary and restaging T2W magnetic resonance imaging and clinical features were used. Visible LLNs were segmented and used for a multi-channel convolutional neural network. A deep learning model was developed and trained for the prediction of (L)LR according to malignant LLNs. Combined imaging and clinical features resulted in AUCs of 0.78 and 0.80 for LR and LLR, respectively. The sensitivity and specificity were 85.7% and 67.6%, respectively. Class activation map explainability methods were applied and consistently identified the same high-risk regions with structural similarity indices ranging from 0.772-0.930. This model resulted in good predictive value for (L)LR rates and can form the basis of future auto-segmentation programs to assist in the identification of high-risk patients and the development of risk stratification models.

Risk factors for lateral pelvic lymph node metastasis in patients with lower rectal cancer: a systematic review and meta-analysis

Background and objective

Lateral pelvic lymph node (LPLN) metastasis is one of the prominent reasons for local recurrence (LR) in patients with rectal cancer (RC). The evaluation criteria of lateral lymph node dissection (LLND) for patients in eastern (mainly in Japan) and western countries have been controversial. The aim of this study was to analyse the risk factors for LPLN metastasis in order to guide surgical methods.

Methods

We searched relevant databases (Embase (Ovid), Medline (Ovid), PubMed, Cochrane Library, and Web of Science) for articles published between 1 January 2000 and 05 October 2022 to evaluate the risk factors for LPLN metastasis in patients with RC in this meta-analysis.

Results

A total of 24 articles with 5843 patients were included in this study. The overall results showed that female sex, age <60 years, pretherapeutic CEA level >5 ng/ml, clinical T4 stage (cT4), clinical M1 stage (cM1), distance of the tumour from the anal verge (AV) <50 mm, tumour centre located below the peritoneal reflection (Rb), short axis (SA) of LPLN ≥8 mm before nCRT, short axis (SA) of LPLN ≥5 mm after nCRT, border irregularity of LPLN, tumour size ≥50 mm, pathological T3-4 stage (pT3-4), pathological N2 stage (pN2), mesorectal lymph node metastasis (MLNM), lymphatic invasion (LI), venous invasion (VI), CRM (+) and poor differentiation were significant risk factors for LPLN metastasis (P <0.05).

Conclusion

This study summarized almost all potential risk factors of LPLN metastasis and expected to provide effective treatment strategies for patients with LRC. According to the risk factors of lateral lymph node metastasis, we can adopt different comprehensive treatment strategies. High-risk patients can perform lateral lymph node dissection to effectively reduce local recurrence; In low-risk patients, we can avoid overtreatment, reduce complications and trauma caused by lateral lymph node dissection, and maximize patient survival and quality of life.

Artificial intelligence in colorectal surgery: an AI-powered systematic review

Artificial intelligence (AI) has the potential to revolutionize surgery in the coming years. Still, it is essential to clarify what the meaningful current applications are and what can be reasonably expected. This AI-powered review assessed the role of AI in colorectal surgery. A Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-compliant systematic search of PubMed, Embase, Scopus, Cochrane Library databases, and gray literature was conducted on all available articles on AI in colorectal surgery (from January 1 1997 to March 1 2021), aiming to define the perioperative applications of AI. Potentially eligible studies were identified using novel software powered by natural language processing (NLP) and machine learning (ML) technologies dedicated to systematic reviews. Out of 1238 articles identified, 115 were included in the final analysis. Available articles addressed the role of AI in several areas of interest. In the preoperative phase, AI can be used to define tailored treatment algorithms, support clinical decision-making, assess the risk of complications, and predict surgical outcomes and survival. Intraoperatively, AI-enhanced surgery and integration of AI in robotic platforms have been suggested. After surgery, AI can be implemented in the Enhanced Recovery after Surgery (ERAS) pathway. Additional areas of applications included the assessment of patient-reported outcomes, automated pathology assessment, and research. Available data on these aspects are limited, and AI in colorectal surgery is still in its infancy. However, the rapid evolution of technologies makes it likely that it will increasingly be incorporated into everyday practice.

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.

Phantom-based radiomics feature test-retest stability analysis on photon-counting detector CT

OBJECTIVES

Radiomics image data analysis offers promising approaches in research but has not been implemented in clinical practice yet, partly due to the instability of many parameters. The aim of this study is to evaluate the stability of radiomics analysis on phantom scans with photon-counting detector CT (PCCT).

METHODS

Photon-counting CT scans of organic phantoms consisting of 4 apples, kiwis, limes, and onions each were performed at 10 mAs, 50 mAs, and 100 mAs with 120-kV tube current. The phantoms were segmented semi-automatically and original radiomics parameters were extracted. This was followed by statistical analysis including concordance correlation coefficients (CCC), intraclass correlation coefficients (ICC), as well as random forest (RF) analysis, and cluster analysis to determine the stable and important parameters.

RESULTS

Seventy-three of the 104 (70%) extracted features showed excellent stability with a CCC value > 0.9 when compared in a test and retest analysis, and 68 features (65.4%) were stable compared to the original in a rescan after repositioning. Between the test scans with different mAs values, 78 (75%) features were rated with excellent stability. Eight radiomics features were identified that had an ICC value greater than 0.75 in at least 3 of 4 groups when comparing the different phantoms in a phantom group. In addition, the RF analysis identified many features that are important for distinguishing the phantom groups.

CONCLUSION

Radiomics analysis using PCCT data provides high feature stability on organic phantoms, which may facilitate the implementation of radiomics analysis likewise in clinical routine.

KEY POINTS

• Radiomics analysis using photon-counting computed tomography provides high feature stability. • Photon-counting computed tomography may pave the way for implementation of radiomics analysis in clinical routine.

Radiomics and Radiogenomics in Pelvic Oncology: Current Applications and Future Directions

Radiomics refers to the conversion of medical imaging into high-throughput, quantifiable data in order to analyse disease patterns, guide prognosis and aid decision making. Radiogenomics is an extension of radiomics that combines conventional radiomics techniques with molecular analysis in the form of genomic and transcriptomic data, serving as an alternative to costly, labour-intensive genetic testing. Data on radiomics and radiogenomics in the field of pelvic oncology remain novel concepts in the literature. We aim to perform an up-to-date analysis of current applications of radiomics and radiogenomics in the field of pelvic oncology, particularly focusing on the prediction of survival, recurrence and treatment response. Several studies have applied these concepts to colorectal, urological, gynaecological and sarcomatous diseases, with individual efficacy yet poor reproducibility. This article highlights the current applications of radiomics and radiogenomics in pelvic oncology, as well as the current limitations and future directions. Despite a rapid increase in publications investigating the use of radiomics and radiogenomics in pelvic oncology, the current evidence is limited by poor reproducibility and small datasets. In the era of personalised medicine, this novel field of research has significant potential, particularly for predicting prognosis and guiding therapeutic decisions. Future research may provide fundamental data on how we treat this cohort of patients, with the aim of reducing the exposure of high-risk patients to highly morbid procedures.

MRI radiomics signature to predict lymph node metastasis after neoadjuvant chemoradiation therapy in locally advanced rectal cancer

PURPOSE

To investigative the performance of MRI-radiomics analysis derived from T2WI and apparent diffusion coefficients (ADC) images before and after neoadjuvant chemoradiation therapy (nCRT) separately or simultaneously for predicting post-nCRT lymph node status in patients with locally advanced rectal cancer (LARC). MATERIALS AND METHODS: Eighty-three patients (training cohort, n = 57; validation cohort, n = 26) with LARC between June 2017 and December 2022 were retrospectively enrolled. All the radiomics features were extracted from volume of interest on T2WI and ADC images from baseline and post-nCRT MRI. Delta-radiomics features were defined as the difference between radiomics features before and after nCRT. Seven clinical-radiomics models were constructed by combining the most predictive radiomics signatures and clinical parameters selected from support vector machine. Receiver operating characteristic curve (ROC) was used to evaluate the performance of models. The optimum model-based LNM was applied to assess 5-years disease-free survival (DFS) using Kaplan-Meier analysis. The end point was clinical or radiological locoregional recurrence or distant metastasis during postoperative follow-up.

RESULTS

Clinical-deltaADC radiomics combined model presented good performance for predicting post-CRT LNM in the training (AUC = 0.895,95%CI:0.838-0.953) and validation cohort (AUC = 0.900,95%CI:0.771-1.000). Clinical-deltaADC radiomics-postT2WI radiomics combined model also showed good performances (AUC = 0.913,95%CI:0.838-0.953) in the training and (AUC = 0.912,95%CI:0.771-1.000) validation cohort. As for subgroup analysis, clinical-deltaADC radiomics combined model showed good performance predicting LNM in ypT0-T2 (AUC = 0.827;95%CI:0.649-1.000) and ypT3-T4 stage (AUC = 0.934;95%CI:0.864-1.000). In ypT0-T2 stage, clinical-deltaADC radiomics combined model-based LNM could assess 5-years DFS (P = 0.030).

CONCLUSION

Clinical-deltaADC radiomics combined model could predict post-nCRT LNM, and this combined model-based LNM was associated with 5-years DFS in ypT0-T2 stage.

Repeatability of radiomics studies in colorectal cancer: a systematic review

BACKGROUND

Recently, radiomics has been widely used in colorectal cancer, but many variable factors affect the repeatability of radiomics research. This review aims to analyze the repeatability of radiomics studies in colorectal cancer and to evaluate the current status of radiomics in the field of colorectal cancer.

METHODS

The included studies in this review by searching from the PubMed and Embase databases. Then each study in our review was evaluated using the Radiomics Quality Score (RQS). We analyzed the factors that may affect the repeatability in the radiomics workflow and discussed the repeatability of the included studies.

RESULTS

A total of 188 studies was included in this review, of which only two (2/188, 1.06%) studies controlled the influence of individual factors. In addition, the median score of RQS was 11 (out of 36), range-1 to 27.

CONCLUSIONS

The RQS score was moderately low, and most studies did not consider the repeatability of radiomics features, especially in terms of Intra-individual, scanners, and scanning parameters. To improve the generalization of the radiomics model, it is necessary to further control the variable factors of repeatability.

Establishing and validating predictive nomograms for lateral pelvic lymph node metastasis in patients with rectal cancer based on radiologic factors and clinicopathologic characteristics

INTRODUCTION

It is critical to accurately predict the occurrence of lateral pelvic lymph node (LPN) metastasis. Currently, verified predictive tools are unavailable. This study aims to establish nomograms for predicting LPN metastasis in patients with rectal cancer who received or did not receive neoadjuvant chemoradiotherapy (nCRT).

MATERIALS AND METHODS

We carried out a retrospective study of patients with rectal cancer and clinical LPN metastasis who underwent total mesorectal excision (TME) and LPN dissection (LPND) from January 2012 to December 2019 at 3 institutions. We collected and evaluated their clinicopathologic and radiologic features, and constructed nomograms based on the multivariable logistic regression models.

RESULTS

A total of 472 eligible patients were enrolled into the non-nCRT cohort (n = 312) and the nCRT cohort (n = 160). We established nomograms using variables from the multivariable logistic regression models in both cohorts. In the non-nCRT cohort, the variables included LPN short diameter, cT stage, cN stage, histologic grade, and malignant features, and the C-index was 0.930 in the training cohort and 0.913 in the validation cohort. In the nCRT cohort, the variables included post-nCRT LPN short diameter, ycT stage, ycN stage, histologic grade, and post-nCRT malignant features, and the C-index was 0.836 in the training dataset and 0.827 in the validation dataset. The nomograms in both cohorts were moderately calibrated and well-validated.

CONCLUSIONS

We established nomograms for patients with rectal cancer that accurately predict LPN metastasis. The performance of the nomograms in both cohorts was high and well-validated.

Application of Radiomics in Predicting Treatment Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer: Strategies and Challenges

Neoadjuvant chemoradiotherapy (nCRT) followed by total mesorectal excision is the standard treatment for locally advanced rectal cancer (LARC). A noninvasive preoperative prediction method should greatly assist in the evaluation of response to nCRT and for the development of a personalized strategy for patients with LARC. Assessment of nCRT relies on imaging and radiomics can extract valuable quantitative data from medical images. In this review, we examined the status of radiomic application for assessing response to nCRT in patients with LARC and indicated a potential direction for future research.

Evaluation of radiomics feature stability in abdominal monoenergetic photon counting CT reconstructions

Feature stability and standardization remain challenges that impede the clinical implementation of radiomics. This study investigates the potential of spectral reconstructions from photon-counting computed tomography (PCCT) regarding organ-specific radiomics feature stability. Abdominal portal-venous phase PCCT scans of 10 patients in virtual monoenergetic (VM) (keV 40-120 in steps of 10), polyenergetic, virtual non-contrast (VNC), and iodine maps were acquired. Two 2D and 3D segmentations measuring 1 and 2 cm in diameter of the liver, lung, spleen, psoas muscle, subcutaneous fat, and air were obtained for spectral reconstructions. Radiomics features were extracted with pyradiomics. The calculation of feature-specific intraclass correlation coefficients (ICC) was performed by comparing all segmentation approaches and organs. Feature-wise and organ-wise correlations were evaluated. Segmentation-resegmentation stability was evaluated by concordance correlation coefficient (CCC). Compared to non-VM, VM-reconstruction features tended to be more stable. For VM reconstructions, 3D 2 cm segmentation showed the highest average ICC with 0.63. Based on a criterion of ≥ 3 stable organs and an ICC of ≥ 0.75, 12-mainly non-first-order features-are shown to be stable between the VM reconstructions. In a segmentation-resegmentation analysis in 3D 2 cm, three features were identified as stable based on a CCC of > 0.6 in ≥ 3 organs in ≥ 6 VM reconstructions. Certain radiomics features vary between monoenergetic reconstructions and depend on the ROI size. Feature stability was also shown to differ between different organs. Yet, glcm_JointEntropy, gldm_GrayLevelNonUniformity, and firstorder_Entropy could be identified as features that could be interpreted as energy-independent and segmentation-resegmentation stable in this PCCT collective. PCCT may support radiomics feature standardization and comparability between sites.

Magnetic Resonance Imaging Directed Surgical Decision Making for Lateral Pelvic Lymph Node Dissection in Rectal Cancer After Total Neoadjuvant Therapy (TNT)

OBJECTIVE

Lateral pelvic lymph node (LPLN) metastases are an important cause of preventable local failure in rectal cancer. The aim of this study was to evaluate clinical and oncological outcomes following magnetic resonance imaging (MRI)-directed surgical selection for lateral pelvic lymph node dissection (LPLND) after total neoadjuvant therapy (TNT).

METHODS

A retrospective consecutive cohort analysis was performed of rectal cancer patients with enlarged LPLN on pretreatment MRI. Patients were categorized as LPLND or non-LPLND. The main outcomes were lateral local recurrence rate, perioperative and oncological outcomes and factors associated with decision making for LPLND.

RESULTS

A total of 158 patients with enlarged pretreatment LPLN and treated with TNT were identified. Median follow-up was 20 months (interquartile range 10-32). After multidisciplinary review, 88 patients (56.0%) underwent LPLND. Mean age was 53 (SD±12) years, and 54 (34.2%) were female. Total operative time (509 vs 429 minutes; P =0.003) was greater in the LPLND group, but median blood loss ( P =0.70) or rates of major morbidity (19.3% vs 17.0%) did not differ. LPLNs were pathologically positive in 34.1%. The 3-year lateral local recurrence rates (3.4% vs 4.6%; P =0.85) did not differ between groups. Patients with LPLNs demonstrating pretreatment heterogeneity and irregular margin (odds ratio, 3.82; 95% confidence interval: 1.65-8.82) or with short-axis ≥5 mm post-TNT (odds ratio 2.69; 95% confidence interval: 1.19-6.08) were more likely to undergo LPLND.

CONCLUSIONS

For rectal cancer patients with evidence of LPLN metastasis, the appropriate selection of patients for LPLND can be facilitated by a multidisciplinary MRI-directed approach with no significant difference in perioperative or oncologic outcomes.

Ultrasound-based radiomics XGBoost model to assess the risk of central cervical lymph node metastasis in patients with papillary thyroid carcinoma: Individual application of SHAP

Objectives

A radiomics-based explainable eXtreme Gradient Boosting (XGBoost) model was developed to predict central cervical lymph node metastasis (CCLNM) in patients with papillary thyroid carcinoma (PTC), including positive and negative effects.

Methods

A total of 587 PTC patients admitted at Binzhou Medical University Hospital from 2017 to 2021 were analyzed retrospectively. The patients were randomized into the training and test cohorts with an 8:2 ratio. Radiomics features were extracted from ultrasound images of the primary PTC lesions. The minimum redundancy maximum relevance algorithm and the least absolute shrinkage and selection operator regression were used to select CCLNM positively-related features and radiomics scores were constructed. Clinical features, ultrasound features, and radiomics score were screened out by the Boruta algorithm, and the XGBoost model was constructed from these characteristics. SHapley Additive exPlanations (SHAP) was used for individualized and visualized interpretation. SHAP addressed the cognitive opacity of machine learning models.

Results

Eleven radiomics features were used to calculate the radiomics score. Five critical elements were used to build the XGBoost model: capsular invasion, radiomics score, diameter, age, and calcification. The area under the curve was 91.53% and 90.88% in the training and test cohorts, respectively. SHAP plots showed the influence of each parameter on the XGBoost model, including positive (i.e., capsular invasion, radiomics score, diameter, and calcification) and negative (i.e., age) impacts. The XGBoost model outperformed the radiologist, increasing the AUC by 44%.

Conclusions

The radiomics-based XGBoost model predicted CCLNM in PTC patients. Visual interpretation using SHAP made the model an effective tool for preoperative guidance of clinical procedures, including positive and negative impacts.

Development and External Validation of a Preoperative Nomogram for Predicting Lateral Pelvic Lymph Node Metastasis in Patients With Advanced Lower Rectal Cancer

Background

The preoperative prediction of lateral pelvic lymph node (LPLN) metastasis is crucial in determining further treatment strategies for advanced lower rectal cancer patients. In this study, we established a nomogram model to preoperatively predict LPLN metastasis and then externally validated the accuracy of this model.

Methods

A total of 287 rectal cancer patients who underwent LPLN dissection were included in this study. Among them, 200 patients from the Peking University First Hospital were included in the development set, and 87 patients from the First Affiliated Hospital of Xi’an Jiaotong University were included in the independent external validation set. Multivariate logistic regression analysis was used to develop the nomogram. The performance of the nomogram was assessed based on its calibration, discrimination, and clinical utility.

Results

Five factors (differentiation grade, extramural vascular invasion, distance of the tumor from the anal verge, perirectal lymph node status, and largest short-axis diameter of LPLN) were identified and included in the nomogram. The nomogram developed based on the analysis showed robust discrimination with an area under the receiver operating characteristic curve (AUC) of 0.878 (95% CI, 0.824–0.932). The validation set showed good discrimination with an AUC of 0.863 (95% CI, 0.779–0.948). Decision curve analysis showed that the nomogram was clinically useful.

Conclusions

The present study proposed a clinical-imaging nomogram with a combination of clinicopathological risk factors and imaging features. After external verification, the predictive power of the nomogram model was satisfactory, and it is expected to be a convenient, visual, and personalized clinical tool for assessing the risk of LPLN metastasis in advanced lower rectal cancer patients.

Establishment and validation of a nomogram for predicting potential lateral pelvic lymph node metastasis in low rectal cancer

BACKGROUND

Identifying lateral pelvic lymph node (LPN) metastasis in low rectal cancer is crucial before treatment. Several risk factors and prediction models for LPN metastasis have been reported. However, there is no useful tool to accurately predict LPN metastasis. Therefore, we aimed to construct a nomogram for predicting LPN metastasis in rectal cancer.

METHODS

We analyzed the risk factors for potential LPN metastasis by logistic regression analysis in 705 patients who underwent primary resection of low rectal cancer. We included patients at 49 institutes of the Japan Society of Laparoscopic Colorectal Surgery between June 2010 and February 2012. Clinicopathological factors and magnetic resonance imaging findings were evaluated. The nomogram performance was assessed using the c-index and calibration plots, and the nomogram was validated using an external cohort.

RESULTS

In the univariable logistic regression analysis, age, sex, carcinoembryonic antigen, tumor location, clinical T stage, tumor size, circumferential resection margin (CRM), extramural vascular invasion (EMVI), and the short and long axes of LPN and perirectal lymph node (PRLN) were nominated as risk factors for potential LPN metastasis. We identified a combination of the short axis of LPN, tumor location, EMVI, and short axis of PRLN as optimal for predicting potential LPN metastasis and developed a nomogram using these factors. This model had a c-index of 0.74 and was moderately calibrated and well-validated.

CONCLUSIONS

This is the first study to construct a well-validated nomogram for predicting potential LPN metastasis in rectal cancer, and its performance was high.

Machine Learning Algorithms for Predicting Surgical Outcomes after Colorectal Surgery: A Systematic Review

BACKGROUND

Machine learning (ML) has been introduced in various fields of healthcare. In colorectal surgery, the role of ML has yet to be reported. In this systematic review, an overview of machine learning models predicting surgical outcomes after colorectal surgery is provided.

METHODS

Databases PubMed, EMBASE, Cochrane, and Web of Science were searched for studies using machine learning models for patients undergoing colorectal surgery. To be eligible for inclusion, studies needed to apply machine learning models for patients undergoing colorectal surgery. Absence of machine learning or colorectal surgery or studies reporting on reviews, children, study abstracts were excluded. The Probast risk of bias tool was used to evaluate the methodological quality of machine learning models.

RESULTS

A total of 1821 studies were analysed, resulting in the inclusion of 31 articles. A vast proportion of ML algorithms have been used to predict the course of disease and response to neoadjuvant chemoradiotherapy. Radiomics have been applied most frequently, along with predictive accuracies up to 91%. However, most studies included a retrospective study design without external validation or calibration.

CONCLUSIONS

Machine learning models have shown promising potential in predicting surgical outcomes after colorectal surgery. However, large-scale data is warranted to bridge the gap between calibration and external validation. Clinical implementation is needed to demonstrate the contribution of ML within daily practice.

The Effectiveness of Machine Learning in Predicting Lateral Lymph Node Metastasis From Lower Rectal Cancer: A Single Center Development and Validation Study

AIM

Accurate preoperative diagnosis of lateral lymph node metastasis (LLNM) from lower rectal cancer is important to identify patients who require lateral lymph node dissection (LLND). We aimed to create an effective prediction model for LLNM using machine learning by combining preoperative information.

METHODS

We retrospectively examined patients who underwent primary rectal cancer surgery with unilateral or bilateral LLND between April 2010 and March 2020 at a single institution. Using the machine learning software "Prediction One" (Sony Network Communications), we developed a prediction model in the training cohort that included 267 consecutive patients (500 sides) from April 2010. Clinicopathological data obtained from the preoperative examinations were used as the learning items. In the validation cohort that included subsequent patients until March 2020, we compared the discriminating powers of the prediction model and the conventional method using the short-axis diameter of the largest lateral lymph node, as detected on magnetic resonance imaging.

RESULTS

The area under the receiver operating characteristic curve (AUC) of the prediction model was 0.903 in the validation cohort comprising 56 patients (107 sides). This indicated significantly higher predictive power than that of the conventional method (AUC = 0.754; P = .022). Using the cutoff values defined in the training cohort, the accuracy, sensitivity, and specificity of the prediction model were 80.4%, 90.0%, and 79.4%, respectively. The model was able to correctly predict four of five sides comprising LLNM with the short-axis diameters ≤4 mm.

CONCLUSION

Machine learning contributed to the creation of an effective prediction model for LLNM.

Effects of Preoperative Radiotherapy on Long-Term Bowel Function in Patients With Rectal Cancer Treated With Anterior Resection: A Systematic Review and Meta-analysis

Background: Anterior resection is a common surgical approach used in rectal cancer surgery; however, this procedure is known to cause bowel injury and dysfunction. Neoadjuvant therapy is widely used in patients with locally advanced rectal cancer. In this study, we determined the effect of preoperative radiotherapy on long-term bowel function in patients who underwent anterior resection for treatment of rectal cancer. Methods: We performed a comprehensive literature search of the PubMed, Embase, Web of Science, and the Cochrane Library databases. A random-effects model was used in the meta-analysis by the Review Manager software, version 5.3. Results: This systematic review and meta-analysis included 12 studies, which used low anterior resection syndrome score with a total of 2349 patients. Based on them, we concluded that low anterior resection syndrome was significantly more common in the preoperative radiotherapy group (odds ratio 3.59, 95% confidence interval 2.68-4.81, P < .00001) and that major low anterior resection syndrome also occurred significantly more frequently in the preoperative radiotherapy group (odds ratio 3.28, 95% confidence interval 2.05-5.26, P < .00001). Subgroup analyses of long-course radiation, total mesorectal excision, and non-metastatic tumors were performed, and the results met the conclusions of the primary outcomes. Conclusions: Preoperative radiotherapy negatively affects long-term bowel function in patients who undergo anterior resection for rectal cancer.

Lateral lymph node dissection in rectal cancer: State of the art review

Half of the local regional recurrences from rectal cancer are nowadays located in the lateral compartments, most likely due to lateral lymph node (LLN) metastases. There is evidence that a lateral lymph node dissection (LLND) can lower the lateral local recurrence rate. An LLND without neoadjuvant (chemo)radiotherapy in patients with or without suspected LLN metastases has been the standard of care in the East, while Western surgeons believed LLN metastases to be cured by neoadjuvant treatment and total mesorectal excision (TME) only. An LLND in patients without enlarged LLNs might result in overtreatment with low rates of pathological LLNs, but in patients with enlarged LLNs who are treated with (C)RT and TME only, the risk of a lateral local recurrence significantly increases to 20%. Certain Eastern and Western centers are increasingly performing a selective LLND after neoadjuvant treatment in the presence of suspicious LLNs due to new scientific insights, but (inter)national consensus on the indication and surgical approach of LLND is lacking. An LLND is an anatomically challenging procedure with intraoperative risks such as bleeding and postoperative morbidity. It is therefore essential to carefully select the patients who will benefit from this procedure and where possible to perform the LLND in a minimally invasive manner to limit these risks. This review gives an overview of the current evidence of the assessment of LLNs, the indications for LLND, the surgical technique, pitfalls in performing this procedure and the future studies are discussed, aiming to contribute to more (inter)national consensus.

Artificial intelligence for pre-operative lymph node staging in colorectal cancer: a systematic review and meta-analysis

Background

Artificial intelligence (AI) is increasingly being used in medical imaging analysis. We aimed to evaluate the diagnostic accuracy of AI models used for detection of lymph node metastasis on pre-operative staging imaging for colorectal cancer.

Methods

A systematic review was conducted according to PRISMA guidelines using a literature search of PubMed (MEDLINE), EMBASE, IEEE Xplore and the Cochrane Library for studies published from January 2010 to October 2020. Studies reporting on the accuracy of radiomics models and/or deep learning for the detection of lymph node metastasis in colorectal cancer by CT/MRI were included. Conference abstracts and studies reporting accuracy of image segmentation rather than nodal classification were excluded. The quality of the studies was assessed using a modified questionnaire of the QUADAS-2 criteria. Characteristics and diagnostic measures from each study were extracted. Pooling of area under the receiver operating characteristic curve (AUROC) was calculated in a meta-analysis.

Results

Seventeen eligible studies were identified for inclusion in the systematic review, of which 12 used radiomics models and five used deep learning models. High risk of bias was found in two studies and there was significant heterogeneity among radiomics papers (73.0%). In rectal cancer, there was a per-patient AUROC of 0.808 (0.739–0.876) and 0.917 (0.882–0.952) for radiomics and deep learning models, respectively. Both models performed better than the radiologists who had an AUROC of 0.688 (0.603 to 0.772). Similarly in colorectal cancer, radiomics models with a per-patient AUROC of 0.727 (0.633–0.821) outperformed the radiologist who had an AUROC of 0.676 (0.627–0.725).

Conclusion

AI models have the potential to predict lymph node metastasis more accurately in rectal and colorectal cancer, however, radiomics studies are heterogeneous and deep learning studies are scarce.

Trial registration

PROSPERO CRD42020218004.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08773-w.

Dose-based radiomic analysis (dosiomics) for intensity-modulated radiotherapy in patients with prostate cancer: Correlation between planned dose distribution and biochemical failure

PURPOSE

Although radiotherapy is one of the most significant modalities for localized prostate cancer, the prognostic factors for biochemical recurrence (BCR) regarding the treatment plan are unclear. We aimed to develop a novel dosiomics-based prediction model for BCR in patients with prostate cancer and clarify the correlations between the dosimetric factors and BCR.

METHODS AND MATERIALS

This study included 489 patients with localized prostate cancer (BCR: 96, No-BCR: 393) who received intensity-modulated radiation therapy. A total of 2,475 dosiomic features were extracted from the dose distributions on the prostate, clinical target volume (CTV), and planning target volume. A prediction model for BCR was trained on a training cohort of 342 patients. The performance of this model was validated using the concordance index (C-index) in a validation cohort of 147 patients. Another model was constructed using clinical variables, dosimetric parameters, and radiomic features for comparisons. Kaplan-Meier curves with log-rank analysis were used to assess the univariate discrimination based on the predictive dosiomic features.

RESULTS

The dosiomic feature derived from the CTV was significantly associated with BCR (hazard ratio: 0.73; 95% confidence interval [CI]: 0.57-0.93; P = .01). Although the dosiomics model outperformed the dosimetric and radiomics models, it did not outperform the clinical model. The performance significantly improved by combining the clinical variables and dosiomic features (C-index: 0.67; 95% CI: 0.65-0.68; P < .0001). The predictive dosiomic features were used to distinguish high-risk and low-risk patients (P < .05).

CONCLUSIONS

The dosiomic feature extracted from the CTV was significantly correlated with BCR in patients with prostate cancer, and the dosiomics model outperformed the model with conventional dose indices. Hence, new metrics for evaluating the quality of a treatment plan are warranted. Moreover, further research should be conducted to determine whether dosiomics can be incorporated in a clinical workflow or clinical trial.

Oncologic impact of lateral lymph node metastasis at the distal lateral compartment in locally advanced low rectal cancer after neoadjuvant (chemo)radiotherapy

INTRODUCTION

The frequency and oncologic outcomes of lateral lymph node (LLN) metastasis at the most distal lateral compartment (DLC) among clinical stage II-III low rectal cancer patients treated with neoadjuvant (chemo)radiotherapy (nCRT) are poorly understood. The aim was to investigate the oncologic impact of LLN metastasis in the DLC versus the proximal lateral compartment (PLC).

MATERIALS AND METHODS

Consecutive patients with low rectal cancer treated with nCRT followed by total mesorectal excision and selective LLN dissection including the DLC were analyzed retrospectively. DLC was defined as the area distal to the infra-piriformis foramen on axial MRI images. Size and location of LLN metastasis on MRI, and survival were retrospectively assessed.

RESULTS

Of the 718 patients, 72 (10.0%) had pathological LLN metastasis. Thirty-two (44.4%) had metastasis in the DLC (DLC group), while 40 (55.6%) had metastasis in the PLC without metastasis in the DLC (PLC group). The proportion of ypN2 category tended to be lower in the DLC group (15.6% vs 35.0%, P = 0.105). The median number of metastatic LLN was similar (1 vs. 1, P = 0.691). The median short-axis size of metastatic LLN was smaller in the DLC group than in the PLC group on pre-treatment (P < 0.001) and re-staging (P = 0.004) MRI. By multivariable analysis, LLN metastasis in the DLC was predictive of better disease-free survival (HR, 0.412; 95% CI, 0.159-0.958, P = 0.039).

CONCLUSION

LLN metastasis in the DLC is frequent and has favorable oncologic outcomes after surgical dissection with nCRT.

Advances in radiological staging of colorectal cancer

The role of imaging in clinically staging colorectal cancer has grown substantially in the 21^st century with more widespread availability of multi-row detector computed tomography (CT), high-resolution magnetic resonance imaging (MRI) with diffusion weighted imaging (DWI), and integrated positron-emission tomography (PET)/CT. In contrast to staging many other cancers, increasing colorectal cancer stage does not highly correlate with survival. As has been the case previously, clinical practice incorporates advances in staging and it is used to guide therapy before adoption into international staging guidelines. Emerging imaging techniques show promise to become part of future staging standards.

Prediction of recurrence after surgery in colorectal cancer patients using radiomics from diagnostic contrast-enhanced computed tomography: a two-center study

OBJECTIVES

To assess the value of contrast-enhanced (CE) diagnostic CT scans characterized through radiomics as predictors of recurrence for patients with stage II and III colorectal cancer in a two-center context.

MATERIALS AND METHODS

This study included 193 patients diagnosed with stage II and III colorectal adenocarcinoma from 1 July 2008 to 15 March 2017 in two different French University Hospitals. To compensate for the variability in two-center data, a statistical harmonization method Bootstrapped ComBat (B-ComBat) was used. Models predicting disease-free survival (DFS) were built using 3 different machine learning (ML): (1) multivariate regression (MR) with 10-fold cross-validation after feature selection based on least absolute shrinkage and selection operator (LASSO), (2) random forest (RF), and (3) support vector machine (SVM), both with embedded feature selection.

RESULTS

The performance for both balanced and 95% sensitivity models was systematically higher after our proposed B-ComBat harmonization compared to the use of the original untransformed data. The most clinically relevant performance was achieved by the multivariate regression model combining a clinical variable (postoperative chemotherapy) with two radiomics shape descriptors (compactness and least axis length) with a BAcc of 0.78 and an MCC of 0.6 associated with a required sensitivity of 95%. The resulting stratification in terms of DFS was significant (p = 0.00021), especially compared to the use of unharmonized original data (p = 0.17).

CONCLUSIONS

Radiomics models derived from contrast-enhanced CT could be trained and validated in a two-center cohort with a good predictive performance of recurrence in stage II et III colorectal cancer patients.

KEY POINTS

• Adjuvant therapy decision in colorectal cancer can be a challenge in medical oncology. • Radiomics models, derived from diagnostic CT, trained and validated in a two-center cohort, could predict recurrence in stage II and III colorectal cancer patients. • Identifying patients with a low risk of recurrence, these models could facilitate treatment optimization and avoid unnecessary treatment.

Radiogenomics in Colorectal Cancer

Simple Summary

Colorectal carcinoma is characterized by intratumoral heterogeneity that can be assessed by radiogenomics. Radiomics, high-throughput quantitative data extracted from medical imaging, combined with molecular analysis, through genomic and transcriptomic data, is expected to lead to significant advances in personalized medicine. However, a radiogenomics approach in colorectal cancer is still in its early stages and many problems remain to be solved. Here we review the progress and challenges in this field at its current stage, as well as future developments.

Abstract

The steady improvement of high-throughput technologies greatly facilitates the implementation of personalized precision medicine. Characterization of tumor heterogeneity through image-derived features—radiomics and genetic profile modifications—genomics, is a rapidly evolving field known as radiogenomics. Various radiogenomics studies have been dedicated to colorectal cancer so far, highlighting the potential of these approaches to enhance clinical decision-making. In this review, a general outline of colorectal radiogenomics literature is provided, discussing the current limitations and suggested further developments.

Simultaneous Integrated Boost Intensity-Modulated Radiation Therapy Can Benefit the Locally Advanced Rectal Cancer Patients With Clinically Positive Lateral Pelvic Lymph Node

Background and Purpose

The optimal treatment modality for clinically positive lateral pelvic lymph node (LPLN) from locally advanced rectal cancer (LARC) is unknown. Thus, we aimed to analyze the optimal radiotherapy dose for clinically positive LPLN from LARC.

Materials and Methods

We retrospectively evaluated distal LARC (i.e., within 8 cm from the anal verge) patients with clinically positive LPLN (i.e., ≥7 mm in the short axis). They were divided into two groups based on whether or not they received simultaneous integrated boost intensity-modulated radiation therapy (SIB-IMRT)–based chemoradiotherapy. The total radiotherapy dose on LPLN were 56-60Gy for SIB-IMRT group and 41.8Gy for non-SIB-IMRT group. The clinical parameters and regrowth rate of LPLN were then compared between the two groups.

Results

A total of 151 patients were evaluated, and 83 and 68 patients were classified to the SIB-IMRT and non-SIB-IMRT group, respectively. The median follow-up period was 22.6 months, and the 2-year LPLN regrowth rate was significantly different between the SIB-IMRT group and the non-SIB-IMRT group (0% vs 10.8%, P=0.024). Further, SIB-IMRT yielded a significantly lower 2-year LPLN regrowth rate in patients whose LPLN measured ≥8 mm in the short axis (0% vs. 15.9%, P=0.019) or ≥10 mm in the long axis (0% vs. 17.6%, P=0.024) compared to patients who were in non-SIB-IMRT group. Meanwhile, there was no significant difference in grade II radiation-related toxicity (30.1% vs. 39.1%, P=0.217) and surgical complications (21.8% vs. 12.2%, P=0.198) between the two groups.

Conclusion

SIB-IMRT–based neoadjuvant chemoradiotherapy is beneficial for eliminating clinically positive LPLN from LARC without increasing the incidence of radiotherapy-related toxicity and surgical complications, and patients with larger LPLN may gain benefit from this technique.