A Nomogram of Combining IVIM-DWI and MRI Radiomics From the Primary Lesion of Rectal Adenocarcinoma to Assess Nonenlarged Lymph Node Metastasis Preoperatively

Preoperative prediction of lymph node metastasis in colorectal cancer using 18F-FDG PET/CT peritumoral radiomics analysis

BACKGROUND

Radiomics has been used in the diagnosis of tumor lymph node metastasis (LNM). However, to date, most studies have been based on intratumoral radiomics. Few studies have focused on the use of 18F-fluorodeoxyglucose positron emission computed tomography (18F-FDG PET/CT) peritumoral radiomics for the diagnosis of LNM in colorectal cancer (CRC).

PURPOSE

Determining the value of radiomics features extracted from 18F-FDG PET/CT images of the peritumoral region in predicting LNM in patients with CRC.

METHODS

The clinical data and preoperative 18F-FDG PET/CT images of 244 CRC patients were retrospectively analyzed. Intratumoral and peritumoral radiomics features were screened using the mutual information method, and least absolute shrinkage and selection operator regression. Based on the selected radiomics features, a radiomics score (Rad-score) was calculated, and independent risk factors obtained from univariate and multivariate logistic regression analyses were used to construct clinical and combined (Radiomics + Clinical) models. The performance of these models was evaluated using the DeLong test, while their clinical utility was assessed by decision curve analysis. Finally, a nomogram was constructed to visualize the predictive model.

RESULTS

The most optimal set of features retained by the feature filtering process were all peritumoral radiomic features. Carcinoembryonic antigen levels, PET/CT-reported lymph node status and Rad-score were found to be independent risk factors for LNM. All three LNM risk assessment models exhibited good predictive performance, with the combined model showing the best classification results, with areas under the curve of 0.85 and 0.76 in the training and validation groups, respectively. The DeLong test revealed that the performance of the combined model was superior to that of the clinical and radiomics models in both the training and validation groups, although this difference was only statistically significant in the training group. DCA indicated that the combined model displayed better clinical utility.

CONCLUSIONS

18F-FDG PET/CT peritumoral radiomics is uniquely suited to predict the presence of LNM in patients with CRC. In particular, the predictive efficacy of LNM for precision therapy and individualized patient management can be improved by using a combination of clinical risk factors.

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

BACKGROUND

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

AIM

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

METHODS

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

RESULTS

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

CONCLUSION

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

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

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

Intravoxel incoherent motion magnetic resonance imaging to assess early tumor response to radiation therapy: Review and future directions

Early prediction of radiation response by imaging is a dynamic field of research and it can be obtained using a variety of noninvasive magnetic resonance imaging methods. Recently, intravoxel incoherent motion (IVIM) has gained interest in cancer imaging. IVIM carries both diffusion and perfusion information, making it a promising tool to assess tumor response. Here, we briefly introduced the basics of IVIM, reviewed existing studies of IVIM in various type of tumors during radiotherapy in order to show whether IVIM is a useful technique for an early assessment of radiation response. 31/40 studies reported an increase of IVIM parameters during radiotherapy compared to baseline. In 27 studies, this increase was higher in patients with good response to radiotherapy. Future directions including implementation of IVIM on MR-Linac and its limitation are discussed. Obtaining new radiologic biomarkers of radiotherapy response could open the way for a more personalized, biology-guided radiation therapy.

Value of multiple models of diffusion-weighted imaging to predict hepatic lymph node metastases in colorectal liver metastases patients

BACKGROUND

About 10%-31% of colorectal liver metastases (CRLM) patients would concomitantly show hepatic lymph node metastases (LNM), which was considered as sign of poor biological behavior and a relative contraindication for liver resection. Up to now, there's still lack of reliable preoperative methods to assess the status of hepatic lymph nodes in patients with CRLM, except for pathology examination of lymph node after resection.

AIM

To compare the ability of mono-exponential, bi-exponential, and stretched-exponential diffusion-weighted imaging (DWI) models in distinguishing between benign and malignant hepatic lymph nodes in patients with CRLM who received neoadjuvant chemotherapy prior to surgery.

METHODS

In this retrospective study, 97 CRLM patients with pathologically confirmed hepatic lymph node status underwent magnetic resonance imaging, including DWI with ten b values before and after chemotherapy. Various parameters, such as the apparent diffusion coefficient from the mono-exponential model, and the true diffusion coefficient, the pseudo-diffusion coefficient, and the perfusion fraction derived from the intravoxel incoherent motion model, along with distributed diffusion coefficient (DDC) and α from the stretched-exponential model (SEM), were measured. The parameters before and after chemotherapy were compared between positive and negative hepatic lymph node groups. A nomogram was constructed to predict the hepatic lymph node status. The reliability and agreement of the measurements were assessed using the coefficient of variation and intraclass correlation coefficient.

RESULTS

Multivariate analysis revealed that the pre-treatment DDC value and the short diameter of the largest lymph node after treatment were independent predictors of metastatic hepatic lymph nodes. A nomogram combining these two factors demonstrated excellent performance in distinguishing between benign and malignant lymph nodes in CRLM patients, with an area under the curve of 0.873. Furthermore, parameters from SEM showed substantial repeatability.

CONCLUSION

The developed nomogram, incorporating the pre-treatment DDC and the short axis of the largest lymph node, can be used to predict the presence of hepatic LNM in CRLM patients undergoing chemotherapy before surgery. This nomogram was proven to be more valuable, exhibiting superior diagnostic performance compared to quantitative parameters derived from multiple b values of DWI. The nomogram can serve as a preoperative assessment tool for determining the status of hepatic lymph nodes and aiding in the decision-making process for surgical treatment in CRLM patients.

Preoperative detection of lymphovascular invasion in rectal cancer using intravoxel incoherent motion imaging based on radiomics

BACKGROUND

Lymphovascular invasion (LVI) status plays an important role in treatment decision-making in rectal cancer (RC). Intravoxel incoherent motion (IVIM) imaging has been shown to detect LVI; however, making better use of IVIM data remains an important issue that needs to be discussed.

PURPOSE

We proposed to explore the best way to use IVIM quantitative parameters and images to construct radiomics models for the noninvasive detection of LVI in RC.

METHODS

A total of 83 patients (LVI negative (LVI-): LVI positive (LVI+) = 51:32) with postoperative pathology-confirmed LVI status in RC were divided into a training group (n = 58) and a validation group (n = 25). Images were acquired from a 3.0 Tesla machine, including oblique axial T2 weighted imaging (T2WI) and IVIM with 11 b values. The ADC, D, D* and f values were measured on IVIM maps. The ROIs of tumors were delineated on T2WI, DWI, ADCmap , and Dmap images, and three mapping methods were used: ROIs_mapping from DWI, ROIs_mapping from ADCmap , and ROIs_mapping from Dmap . Three-dimensional radiomics features were extracted from the delineated ROIs. Multivariate logistic regression was used for radiomics feature selection. Radiomics models based on different mapping methods were developed. Receiver operating characteristic (ROC) curves, calibration, and decision curve analyses (DCA) were used to evaluate the performance of the models.

RESULTS

Model B, which was constructed with radiomics features from ADCmap , Dmap and fmap by "ROIs_mapping from DWI" and T2WI (AUC 0.894), performed better than other models based on single sequence (AUC 0.600-0.806) and even better than Model A, which was based on "ROIs_mapping from ADC" and T2WI (AUC 0.838). Furthermore, an integrated model was constructed with Model B and the IVIM parameter (f value) with an AUC of 0.920 (95% CI: 0.820-1.000), which was higher than that of Model B, in the validation group.

CONCLUSIONS

The integrated model incorporating the radiomics features and IVIM parameters accurately detected LVI of RC. The "ROIs_mapping from DWI" method provided the best results.

Evaluating Mediastinal Lymph Node Metastasis of Non-Small Cell Lung Cancer Using Mono-exponential, Bi-exponential, and Stretched-exponential Models of Diffusion-weighted Imaging

PURPOSE

To explore and compare the diagnostic values of mono-exponential, bi-exponential, and stretched-exponential diffusion-weighted imaging (DWI) parameters of primary lesions and lymph nodes (LNs) to predict mediastinal LN metastasis in patients with non-small cell lung cancer.

PATIENTS AND METHODS

Sixty-one patients with non-small cell lung cancer underwent preoperative magnetic resonance imaging, including multiple b-value DWI. The DWI parameters, including apparent diffusion coefficient (ADC) from a mono-exponential model, true diffusion (D) coefficient, pseudo-diffusion (D*) coefficient, and perfusion fraction (f) from a bi-exponential model, distributed diffusion coefficient (DDC) and intravoxel diffusion heterogeneity index (α) from a stretched-exponential model of primary tumors and LNs and the size characteristics of LNs, were measured and compared. Multivariate logistic regression analysis was used to establish models for predicting mediastinal LN metastasis. Receiver operating characteristic analysis was applied to evaluate diagnostic performances.

RESULTS

The DWI parameters of primary tumors showed no statistical significance between LN metastasis-positive and LN metastasis-negative groups. Nonmetastatic LNs had significantly higher ADC, D, DDC, and α values compared with metastatic LNs (all P < 0.05). The short-dimension, long-dimension, and short-long dimension ratio of metastatic LNs was significantly larger than those of nonmetastatic ones (all P < 0.05). The D value showed the best diagnostic performance among all DWI-derived single parameters, and the short dimension of LNs performed the same among all the size variables. Furthermore, the combination of DWI parameters (ADC and D) and the short dimension of LNs can significantly improve diagnostic efficiency.

CONCLUSIONS

The ADC, D, DDC, and α from the mono-exponential, bi-exponential, and stretched-exponential models were demonstrated efficient in differentiating benign from metastatic LNs, and the combination of ADC, D, and short dimension of LNs may have a better diagnostic performance than DWI or size-derived parameters either in combination or individually.

DCE-MRI-based radiomics in predicting angiopoietin-2 expression in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is the sixth most common cancer, and the third leading cause of cancer death worldwide. Studies have shown that increased angiopoietin-2 (Ang-2) expression relative to Ang-1 expression in tumors is associated with a poor prognosis.The purpose of this study was to investigate the efficacy of predicting Ang-2 expression in HCC by preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI)-based radiomics.

METHODS

The data of 52 patients with HCC who underwent surgical resection in our hospital were retrospectively analyzed. Ang-2 expression in HCC was analyzed by immunohistochemistry. All patients underwent preoperative upper abdominal DCE-MRI and intravoxel incoherent motion diffusion-weighted imaging scans. Radiomics features were extracted from the early and late arterial and portal phases of axial DCE-MRI. Univariate analysis and least absolute shrinkage and selection operator (LASSO) was performed to select the optimal radiomics features for analysis. A logistic regression analysis was performed to establish a DCE-MRI radiomics model, clinic-radiologic (CR) model and combined model integrating the radiomics score with CR factors. The stability of each model was verified by 10-fold cross-validation. Receiver operating characteristic (ROC) curve analysis, calibration curve analysis and decision curve analysis (DCA) were employed to evaluate these models.

RESULTS

Among the 52 HCC patients, high Ang-2 expression was found in 30, and low Ang-2 expression was found in 22. The areas under the ROC curve (AUCs) for the radiomics model, CR model and combined model for predicting Ang-2 expression were 0.800, 0.874, and 0.933, respectively. The DeLong test showed that there was no significant difference in the AUC between the radiomics model and the CR model (p > 0.05) but that the AUC for the combined model was significantly greater than those for the other 2 models (p < 0.05). The DCA results showed that the combined model outperformed the other 2 models and had the highest net benefit.

CONCLUSION

The DCE-MRI-based radiomics model has the potential to predict Ang-2 expression in HCC patients; the combined model integrating the radiomics score with CR factors can further improve the prediction performance.

Application of DKI and IVIM imaging in evaluating histologic grades and clinical stages of clear cell renal cell carcinoma

Purpose

To evaluate the value of quantitative parameters derived from diffusion kurtosis imaging (DKI) and intravoxel incoherent motion (IVIM) in differentiating histologic grades and clinical stages of clear cell renal cell carcinoma (ccRCC).

Materials and methods

A total of 65 patients who were surgically and pathologically diagnosed as ccRCC were recruited in this study. In addition to routine renal magnetic resonance imaging examination, all patients underwent preoperative IVIM and DKI. The corresponding diffusion coefficient (D), pseudo-diffusion coefficient (D*), perfusion fraction (f), mean diffusivity (MD), kurtosis anisotropy (KA), and mean kurtosis (MK) values were obtained. Independent-samples t-test or Mann-Whitney U test was used for comparing the differences in IVIM and DKI parameters among different histologic grades and clinical stages. The diagnostic efficacy of IVIM and DKI parameters was evaluated using the receiver operating characteristic (ROC) curve. Spearman's correlation analysis was used to separately analyze the correlation of each parameter with histologic grades and stages of ccRCC.

Results

The D and MD values were significantly higher in low-grade ccRCC than high-grade ccRCC (all p < 0.001) and in low-stage than high-stage ccRCC (all p < 0.05), and the f value of high-stage ccRCC was lower than that of low-stage ccRCC (p = 0.007). The KA and MK values were significantly higher in low-grade than high-grade ccRCC (p = 0.000 and 0.000, respectively) and in low-stage than high-stage ccRCC (p = 0.000 and 0.000, respectively). The area under the curve (AUC) values of D, D*, f, MD, KA, MK, DKI, and IVIM+DKI values were 0.825, 0.598, 0.626, 0.792, 0.750, 0.754, 0.803, and 0.857, respectively, in grading ccRCC and 0.837, 0.719, 0.710, 0.787, 0.796, 0.784, 0.864, 0.823, and 0.916, respectively, in staging ccRCC. The AUC of IVIM was 0.913 in staging ccRCC. The D, D*, and MD values were negatively correlated with the histologic grades and clinical stages (all p < 0.05), and the KA and MK values showed a positive correlation with histologic grades and clinical stages (all p < 0.05). The f value was also negatively correlated with the ccRCC clinical stage (p = 0.008).

Conclusion

Both the IVIM and DKI values can be used preoperatively to predict the degree of histologic grades and stages in ccRCC, and the D and MD values have better diagnostic performance in the grading and staging. Also, further slightly enhanced diagnostic efficacy was observed in the model with combined IVIM and DKI parameters.

Development of a Clinical-Radiomics Nomogram That Used Contrast-Enhanced Ultrasound Images to Anticipate the Occurrence of Preoperative Cervical Lymph Node Metastasis in Papillary Thyroid Carcinoma Patients

Background and Objectives

Papillary thyroid carcinoma (PTC) is a prevalent histological type of thyroid cancer; however, noninvasive assessment of cervical lymph node metastasis (LNM) poses a challenge. This study aims to develop a novel clinical-radiomics nomogram that utilizes ultrasound (US) images to predict the presence of cervical LNM metastasis in patients with PTC.

Methods

A total of 423 patients with PTC were recruited to participate in this study between January 2020 and December 2022, of which 282 were classified into the training group and 141 patients were classified into the validation set. Contrast-enhanced ultrasound (CEUS) and B-mode ultrasound (BMUS) images were subjected to radiomic analysis, leading to the extraction of 912 radiomic features. Thereafter, a radiomics score (Radscore) was developed to effectively integrate the information derived from BMUS and CEUS modalities. Univariate and multivariate backward stepwise logistic regression analysis techniques were used to construct the clinical and clinical-radiomics models, respectively.

Results

The findings revealed that the clinical-radiomics nomogram incorporated age, sex, CEUS Radscore, and US-reported LNM as risk factors. The nomogram demonstrated good performance using data from the training (AUC = 0.891) and validation (AUC = 0.870) sets. The decision curve analysis implied that this nomogram exhibited good clinical utility, which was further supported by the results of the calibration curves and Hosmer-Lemeshow test.

Conclusion

The CEUS Radscore-based clinical radiomics nomogram could serve as a valuable tool for predicting cervical LNM metastasis in patients with PTC, thereby tailoring individualized treatment strategies for them.

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.

Prediction of hepatic lymph node metastases based on magnetic resonance imaging before and after preoperative chemotherapy in patients with colorectal liver metastases underwent surgical resection

BACKGROUND

Patients with colorectal liver metastases (CRLM) combined with hepatic lymph node (HLN) metastases have a poor prognosis. In this study, we developed and validated a model using clinical and magnetic resonance imaging (MRI) parameters to predict HLN status before surgery.

METHODS

A total of 104 CRLM patients undergoing hepatic lymphonodectomy with pathologically confirmed HLN status after preoperative chemotherapy were enrolled in this study. The patients were further divided into a training group (n = 52) and a validation group (n = 52). The apparent diffusion coefficient (ADC) values, including ADC_mean and ADC_min of the largest HLN before and after treatment, were measured. rADC was calculated referring to the target liver metastases, spleen, and psoas major muscle (rADC_-LM, rADC_-SP, rADC_-m). In addition, ADC change rate (Δ% ADC) was quantitatively calculated. A multivariate logistic regression model for predicting HLN status in CRLM patients was constructed using the training group and further tested in the validation group.

RESULTS

In the training cohort, post-ADC_mean (P = 0.018) and the short diameter of the largest lymph node after treatment (P = 0.001) were independent predictors for metastatic HLN in CRLM patients. The model's AUC was 0.859 (95% CI, 0.757-0.961) and 0.767 (95% CI 0.634-0.900) in the training and validation cohorts, respectively. Patients with metastatic HLN showed significantly worse overall survival (p = 0.035) and recurrence-free survival (p = 0.015) than patients with negative HLN.

CONCLUSIONS

The developed model using MRI parameters could accurately predict HLN metastases in CRLM patients and could be used to preoperatively assess the HLN status and facilitate surgical treatment decisions in patients with CRLM.

MRI-Based Artificial Intelligence in Rectal Cancer

Rectal cancer (RC) accounts for approximately one-third of colorectal cancer (CRC), with death rates increasing in patients younger than 50 years old. Magnetic resonance imaging (MRI) is routinely performed for tumor evaluation. However, the semantic features from images alone remain insufficient to guide treatment decisions. Functional MRIs are useful for revealing microstructural and functional abnormalities and nevertheless have low or modest repeatability and reproducibility. Therefore, during the preoperative evaluation and follow-up treatment of patients with RC, novel noninvasive imaging markers are needed to describe tumor characteristics to guide treatment strategies and achieve individualized diagnosis and treatment. In recent years, the development of artificial intelligence (AI) has created new tools for RC evaluation based on MRI. In this review, we summarize the research progress of AI in the evaluation of staging, prediction of high-risk factors, genotyping, response to therapy, recurrence, metastasis, prognosis, and segmentation with RC. We further discuss the challenges of clinical application, including improvement in imaging, model performance, and the biological meaning of features, which may also be major development directions in the future. EVIDENCE LEVEL: 5 TECHNICAL EFFICACY: Stage 2.

Preoperative Prediction Value of Pelvic Lymph Node Metastasis of Endometrial Cancer: Combining of ADC Value and Radiomics Features of the Primary Lesion and Clinical Parameters

Objective

To investigate the value of apparent diffusion coefficient (ADC) value of endometrial cancer (EC) primary lesion and magnetic resonance imaging (MRI) three-dimensional (3D) radiomics features combined with clinical parameters for preoperative prediction of pelvic lymph node metastasis (PLNM).

Methods

A total of 136 patients with EC confirmed by postoperative pathology were retrospectively reviewed and analyzed. Patients were randomly divided into training set (n = 95) and test set (n = 41) at a ratio of 7 : 3. Radiomics features based on T₂WI, DWI, and contrast-enhanced T₁WI (CE-T₁WI) sequence were extracted and screened, and then radiomics score (Rads-score) was calculated. Clinical parameters and ADC value of EC primary lesion were measured and collected, and their correlation with PLNM was analyzed. Receiver operating characteristic (ROC) curve was plotted to assess the diagnostic efficacy of the model. A nomogram for PLNM was created based on the multivariate logistic regression model.

Results

The ADC value of the EC primary lesion showed inverse correlation with PLNM, while CA125 and Rads-score were positively associated with PLNM. A predictive model was proposed based on ADC value, Rads-score, CA125, and MR-reported pelvic lymph node status (PLNS) for PLNM in EC. The area under the curve (AUC) of the model is 0.940; the sensitivity and specificity (87.1% and 90.6%) of the model were significantly higher than that of the MRI morphological signs.

Conclusion

A combination of ADC value, MRI 3D radiomics features of the EC primary lesion, and clinical parameters generated a prediction model for PLNM in EC and had a good diagnostic performance; it was a useful supplement to MR-reported PLNS based on MRI morphological signs.

Combining Clinicopathology, IVIM-DWI and Texture Parameters for a Nomogram to Predict Treatment Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer Patients

Objectives

This study aimed to create a nomogram for the risk prediction of neoadjuvant chemoradiotherapy (nCRT) resistance in locally advanced rectal cancer (LARC).

Methods

Clinical data in this retrospective study were collected from a total of 135 LARC patients admitted to our hospital from June 2016 to December 2020. After screening by inclusion and exclusion criteria, 62 patients were included in the study. Texture analysis (TA) was performed on T2WI and DWI images. Patients were divided into response group (CR+PR) and no-response group (SD+PD) according to efficacy assessment. Multivariate analysis was performed on clinicopathology, IVIM-DWI and texture parameters for screening of independent predictors. A nomogram was created and model fit and clinical net benefit were assessed.

Results

Multivariate analysis of clinicopathology parameters showed that the differentiation and T stage were independent predictors (OR values were 14.516 and 11.589, resp.; P<0.05). Multivariate analysis of IVIM-DWI and texture parameters showed that f value and Rads-score were independent predictors (OR values were 0.855, 2.790, resp.; P<0.05). In this study, clinicopathology together with IVIM-DWI and texture parameters showed the best predictive efficacy (AUC=0.979). The nomogram showed good predictive performance and stability in identifying high-risk LARC patients who are resistant to nCRT (C-index=0.979). Decision curve analyses showed that the nomogram had the best clinical net benefit. Ten-fold cross-validation results showed that the average AUC value was 0.967, and the average C-index was 0.966.

Conclusions

The nomogram combining the differentiation, T stage, f value and Rads-score can effectively estimate the risk of nCRT resistance in patients with LARC.