DCE-MRI radiomics nomogram can predict response to neoadjuvant chemotherapy in esophageal cancer

Radiomics-clinical nomogram for preoperative tumor-node-metastasis staging prediction in breast cancer patients using dynamic enhanced magnetic resonance imaging

Background

Breast cancer is one of the most commonly diagnosed malignancies in women worldwide, and the disease burden continues to aggravate. The tumor-node-metastasis (TNM) staging information is crucial for oncology physicians to develop appropriate clinical strategies. This study aimed to investigate the value of a radiomics-clinical model for predicting TNM stage in breast cancer patients using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).

Methods

DCE-MRI images from 166 patients with pathologically confirmed breast cancer were retrospectively collected, including early stage (TNM0-TNM2) and locally advanced or advanced stage (TNM3-TNM4). Included patients were divided into a training cohort (n=116) and a test cohort (n=50). The radiomics, clinical and integrated models were constructed and a nomogram was established to distinguish the TNM0-TNM2 stage from the TNM3-TNM4 stage. Receiver operating characteristic (ROC) curves, calibration curves and decision curve analysis (DCA) were employed to assess the predictability of the models.

Results

Eighty-five patients were at the early stages, while 81 patients were at the other stages. In the training and test cohorts, the area under the curve (AUC) values for distinguishing early and advanced breast cancer were 0.870 and 0.818 for the nomogram, respectively. The nomogram calibration curves showed good agreement between the predicted and observed TNM stages in the training and test cohorts. The Hosmer-Lemeshow test showed that the nomogram fit perfectly in the two cohorts. DCA indicated that the nomogram displayed clear superiority in forecasting TNM staging over clinical and radiomic signatures.

Conclusions

Compared to traditional imaging methods, the clinical-radiomics nomogram acquired by DCE-MRI could potentially be utilized to preoperatively evaluate the TNM stage of breast cancer with relatively high accuracy. It can be an effective method to guide clinical decisions.

Meta-Analysis of MRI in Predicting Early Response to Radiotherapy and Chemotherapy in Esophageal Cancer

RATIONALE AND OBJECTIVES

At present, the application of magnetic resonance imaging (MRI) in the prediction of response to neoadjuvant therapy and concurrent chemoradiotherapy for the treatment of esophageal cancer still needs to be further explored, and its early differential value remains controversial, thus we carried out this systematic review with a meta-analysis. In the application, different MRI sequences and corresponding parameters are used for the differential diagnosis of the response to neoadjuvant therapy and concurrent chemoradiotherapy.

METHODS

All relevant studies evaluated the efficacy and response to MRI in neoadjuvant therapy or concurrent chemoradiotherapy for esophageal cancer on Pubmed, Embase, Cohrane Library, and Web of Science databases published before October 10, 2023 (inclusive) were systematically searched. A revised tool was used to assess the quality of diagnostic accuracy studies (QUADAS-2) to assess the risk of bias in the included original studies. A subgroup analysis of MRI sequences diffusion weighted imaging (DWI), dynamic contrast enhanced (DCE) and their corresponding different parameters, as well as the acquisition timepoints (before and after treatment) for different parameters, was performed during the meta-analysis. The bivariate mixed-effects model was used for meta-analysis.

RESULTS

21 studies were finally included, involving 1128 patients with esophageal cancer. The sensitivity, specificity, and area under receiver operating characteristic curve (ROC curve) of DWI sequence for identifying response to concurrent chemoradiotherapy were 0.82 (95% CI: 0.74-0.87), 0.81 (95% CI: 0.72-0.87) and 0.88 (95% CI: 0.56-0.98), respectively. The sensitivity, specificity, and area under ROC curve of DCE sequence for identifying response to concurrent chemoradiotherapy were 0.78 (95% CI: 0.70-0.84), 0.65 (95% CI: 0.59-0.70) and 0.73 (95% CI: 0.50-0.88), respectively. In patients with esophageal cancer, the sensitivity, specificity, and area under the ROC curve of DWI sequences for identifying response to neoadjuvant therapy were 0.80 (95% CI: 0.69 - 0.88), 0.81 (95% CI: 0.69 - 0.89), and 0.88 (95% CI: 0.34 - 0.99), respectively; the sensitivity, specificity, and area under the ROC curve of DCE sequences for identifying response to neoadjuvant therapy were 0.84 (95% CI: 0.76 - 0.90), 0.61 (95% CI: 0.53 - 0.68), and 0.70 (95% CI: 0.27 - 0.94), respectively.

CONCLUSIONS

Based on the available evidence, MRI had a very good value in the early identification of response to neoadjuvant therapy and concurrent chemoradiotherapy for esophageal cancer, especially DWI. Apparent diffusion coefficient (ADC) value changes before and after treatment could be used as predictors of pathological response. Also, ADC value changes before and after treatment could be used as a tool to guide clinical decision-making.

Prediction of Neoadjuvant Chemoradiotherapy Sensitivity in Patients With Esophageal Squamous Cell Carcinoma Using CT-Based Radiomics Combined With Clinical Features

Background: For patients with resectable locally advanced esophageal squamous cell carcinoma (ESCC), the current standard treatment is neoadjuvant chemoradiotherapy (nCRT) plus radical surgery. Objective: This study aimed to establish a predictive model, based on computed tomography (CT) radiomics features and clinical parameters, to predict sensitivity to nCRT in patients with ESCC pre-treatment. The goal was to provide risk stratification and decision-making recommendations for clinical treatments and offer more valuable information for developing personalized therapies. Methods: This retrospective study involved 102 patients diagnosed with ESCC through biopsy who underwent nCRT. To select radiomics features, we used the least absolute shrinkage and selection operator (LASSO) algorithm. A combined model was constructed, integrating the selected clinically relevant parameters with the Rad-Score. To assess the performance of this combined model, we utilized calibration curves and receiver operating characteristic (ROC) curves. Results: Nine optimal radiomics features were selected using the LASSO algorithm. The support vector machine (SVM) classifier was identified as having the best predictive performance. The area under the curve (AUC) of the SVM training group was 0.937 (95% CI: 0.856-1.000), and of the validation group was 0.831 (95% CI: 0.679-0.983). Smoking and alcohol history, neutrophil to lymphocyte ratio, serum aspartate aminotransferase to alanine aminotransferase ratio, and carcinoembryonic antigen and fibrinogen levels were independent predictors of sensitivity to nCRT in patients with ESCC. The AUCs of the combined model for the training and validation groups were 0.870 (95% CI: 0.774-0.964) and 0.821 (95% CI: 0.669-0.972), respectively. The calibration curve showed that the nomogram's predictions were close to the actual clinical observations, indicating that the model exhibited good predictive performance. Conclusion: Our combined model based on Rad-Score and clinical characteristics showed high predictive performance for predicting sensitivity to nCRT in patients with ESCC. It may be useful for predicting treatment effects in clinical practice and demonstrates the significant potential of radiomics in predicting and optimizing treatment decisions.

Risk stratification and overall survival prediction in extensive stage small cell lung cancer after chemotherapy with immunotherapy based on CT radiomics

The prognosis of extensive-stage small cell lung cancer is usually poor. In this study, a combined model based on pre-treatment CT radiomics and clinical features was constructed to predict the OS of extensive-stage small cell lung cancer after chemotherapy with immunotherapy.Clinical data of 111 patients with extensive stage small-cell lung cancer who received first-line immunotherapy combined with chemotherapy in our hospital from December 2019 to December 2021 were retrospectively collected. Finally, 93 patients were selected for inclusion in the study, and CT images were obtained through PACS system before treatment. All patients were randomly divided into a training set (n = 66) and a validation set (n = 27). Images were imported into ITK-SNAP to outline areas of interest, and Python software was used to extract radiomics features. A total of 1781 radiomics features were extracted from each patient's images. The feature dimensions were reduced by MRMR and LASSO methods, and the radiomics features with the greatest predictive value were screened. The weight coefficient of radiomics features was calculated, and the linear combination of the feature parameters and the weight coefficient was used to calculate Radscore. Univariate cox regression analysis was used to screen out the factors significantly associated with prognosis from the radiomics and clinical features, and multivariate cox regression analysis was performed to establish the prognosis prediction model of extensive stage small cell lung cancer. The degree of metastases was selected as a significant clinical prognostic factor by univariate cox regression analysis. Seven radiomics features with significance were selected by LASSO-COX regression analysis, and the Radscore was calculated according to the coefficient of the radiomics features. An alignment diagram survival prediction model was constructed by combining Radscore with the number of metastatic lesions. The study population was stratified into those who survived less than 11 months, and those with a greater than 11 month survival. The C-index was 0.722 (se = 0.044) and 0.68(se = 0.074) in the training and the validation sets, respectively. The Log_rank test results of the combination model were as follows: training set: p < 0.0001, validation set: p = 0.00042. In this study, a combined model based on radiomics and clinical features could predict OS in patients with extensive stage small cell lung cancer after chemotherapy with immunotherapy, which could help guide clinical treatment strategies.

Radiomics in Oesogastric Cancer: Staging and Prediction of Preoperative Treatment Response: A Narrative Review and the Results of Personal Experience

BACKGROUND

Oesophageal, gastroesophageal, and gastric malignancies are often diagnosed at locally advanced stage and multimodal therapy is recommended to increase the chances of survival. However, given the significant variation in treatment response, there is a clear imperative to refine patient stratification. The aim of this narrative review was to explore the existing evidence and the potential of radiomics to improve staging and prediction of treatment response of oesogastric cancers.

METHODS

The references for this review article were identified via MEDLINE (PubMed) and Scopus searches with the terms "radiomics", "texture analysis", "oesophageal cancer", "gastroesophageal junction cancer", "oesophagogastric junction cancer", "gastric cancer", "stomach cancer", "staging", and "treatment response" until May 2024.

RESULTS

Radiomics proved to be effective in improving disease staging and prediction of treatment response for both oesophageal and gastric cancer with all imaging modalities (TC, MRI, and 18F-FDG PET/CT). The literature data on the application of radiomics to gastroesophageal junction cancer are very scarce. Radiomics models perform better when integrating different imaging modalities compared to a single radiology method and when combining clinical to radiomics features compared to only a radiomics signature.

CONCLUSIONS

Radiomics shows potential in noninvasive staging and predicting response to preoperative therapy among patients with locally advanced oesogastric cancer. As a future perspective, the incorporation of molecular subgroup analysis to clinical and radiomic features may even increase the effectiveness of these predictive and prognostic models.

A combined model using pre-treatment CT radiomics and clinicopathological features of non-small cell lung cancer to predict major pathological responses after neoadjuvant chemoimmunotherapy

OBJECTIVE

To investigate the relationship between clinical pathological characteristics, pretreatment CT radiomics, and major pathologic response (MPR) of non-small cell lung cancer (NSCLC) after neoadjuvant chemoimmunotherapy, and to establish a combined model to predict the major pathologic response of neoadjuvant chemoimmunotherapy.

METHODS

A retrospective study of 211 patients with NSCLC who underwent neoadjuvant chemoimmunotherapy and surgical treatment from January 2019 to April 2021 was conducted. The patients were divided into two groups: the MPR group and the non-MPR group. Pre-treatment CT images were segmented using ITK SNAP software to extract radiomics features using Python software. Then a radiomics model, a clinical model, and a combined model were constructed and validated using a receiver operating characteristic (ROC) curve. Finally, Delong's test was used to compare the three models.

RESULTS

The radiomics model achieved an AUC of 0.70 (95 % CI: 0.62-0.78) in the training group and 0.60 (95 % CI: 0.45-0.76) in the validation group. RECIST assessment results were screened from all clinical characteristics as independent factors for MPR with multivariate logistic regression analysis. The AUC of the clinical model for predicting MPR was 0.66 (95 % CI: 0.59-0.73) in the training group and 0.77 (95 % CI: 0.66-0.87) in the validation group. The combined model with combined radiomics and clinicopathological characteristics achieved an AUC was 0.76 (95 % CI: 0.68-0.84) in the training group, and 0.80 (95 % CI: 0.67-0.92) in the validation group. Delong's test showed that the AUC of the combined model was significantly higher than that of the radiomics model alone in both the training group (P = 0.0067) and the validation group (P = 0.0009).The calibration curve showed good agreement between predicted and actual MPR. Clinical decision curve analysis showed that the combined model was superior to radiomics alone.

CONCLUSIONS

Radiomics model can predict MPR in NSCLC after neoadjuvant chemoimmunotherapy with similar accuracy to RECIST assessment criteria. The combined model based on pretreatment CT radiomics and clinicopathological features showed better predictive power than independent radiomics model or independent clinicopathological features, suggesting that it may be more useful for guiding personalized neoadjuvant chemoimmunotherapy treatment strategies.

A radiomics strategy based on CT intra-tumoral and peritumoral regions for preoperative prediction of neoadjuvant chemoradiotherapy for esophageal cancer

OBJECTIVE

Develop a method for selecting esophageal cancer patients achieving pathological complete response with pre-neoadjuvant therapy chest-enhanced CT scans.

METHODS

Two hundred and one patients from center 1 were enrolled, split into training and testing sets (7:3 ratio), with an external validation set of 30 patients from center 2. Radiomics features from intra-tumoral and peritumoral images were extracted and dimensionally reduced using Student's t-test and least absolute shrinkage and selection operator. Four machine learning classifiers were employed to build models, with the best-performing models selected based on accuracy and stability. ROC curves were utilized to determine the top prediction model, and its generalizability was evaluated on the external validation set.

RESULTS

Among 16 models, the integrated-XGBoost and integrated-random forest models performed the best, with average ROC AUCs of 0.906 and 0.918, respectively, and RSDs of 6.26 and 6.89 in the training set. In the testing set, AUCs were 0.845 and 0.871, showing no significant difference in ROC curves. External validation set AUCs for integrated-XGBoost and integrated-random forest models were 0.650 and 0.749.

CONCLUSION

Incorporating peritumoral radiomics features into the analysis enhances predictive performance for esophageal cancer patients undergoing neoadjuvant chemoradiotherapy, paving the way for improved treatment outcomes.

MR radiomics predicts pathological complete response of esophageal squamous cell carcinoma after neoadjuvant chemoradiotherapy: a multicenter study

BACKGROUND

More than 40% of patients with resectable esophageal squamous cell cancer (ESCC) achieve pathological complete response (pCR) after neoadjuvant chemoradiotherapy (nCRT), who have favorable prognosis and may benefit from an organ-preservation strategy. Our study aims to develop and validate a machine learning model based on MR radiomics to accurately predict the pCR of ESCC patients after nCRT.

METHODS

In this retrospective multicenter study, eligible patients with ESCC who underwent baseline MR (T2-weighted imaging) and nCRT plus surgery were enrolled between September 2014 and September 2022 at institution 1 (training set) and between December 2017 and August 2021 at institution 2 (testing set). Models were constructed using machine learning algorithms based on clinical factors and MR radiomics to predict pCR after nCRT. The area under the curve (AUC) and cutoff analysis were used to evaluate model performance.

RESULTS

A total of 155 patients were enrolled in this study, 82 in the training set and 73 in the testing set. The radiomics model was constructed based on two radiomics features, achieving AUCs of 0.968 (95%CI 0.933-0.992) in the training set and 0.885 (95%CI 0.800-0.958) in the testing set. The cutoff analysis resulted in an accuracy of 82.2% (95%CI 72.6-90.4%), a sensitivity of 75.0% (95%CI 58.3-91.7%), and a specificity of 85.7% (95%CI 75.5-96.0%) in the testing set.

CONCLUSION

A machine learning model based on MR radiomics was developed and validated to accurately predict pCR after nCRT in patients with ESCC.

Computed tomography-based radiomic analysis for predicting pathological response and prognosis after neoadjuvant chemotherapy in patients with locally advanced esophageal cancer

PURPOSE

Accurate prediction of prognosis and pathological response to neoadjuvant chemotherapy (NAC) is crucial for optimizing treatment strategies for patients with locally advanced esophageal cancer (LA-EC). This study aimed to investigate the use of radiomics for pretreatment CT in predicting the pathological response of patients with LA-EC to NAC.

METHODS

Overall, 144 patients (145 lesions) with LA-EC who underwent pretreatment contrast-enhanced CT and then received NAC followed by surgery with pathological tumor regression grade (TRG) analysis were enrolled. The obtained dataset was randomly divided into training and validation cohorts using fivefold cross-validation. CT-based radiomic features were extracted followed by the feature selection process using the variance threshold, SelectKBest, and least absolute shrinkage and selection operator methods. The radiomic model was constructed using six machine learning classifiers, and predictive performance was evaluated using ROC curve analysis in the training and validation cohorts.

RESULTS

All patients were divided into responders (n = 40, 28%) and non-responders (n = 104, 72%) based on the TRG results and a statistically significant split by overall survival analysis (0.899 [0.754-0.961] vs. 0.630 [0.510-0.729], respectively). There were no significant differences between responders and non-responders in terms of age, sex, tumor size, tumor location, or histopathology. The mean AUC of fivefold in the validation cohort was 0.720 (confidence interval [CI]: 0.594-0.982), and the best AUC of the radiomic model using logistic regression to predict the non-responders was 0.815 (CI: 0.626-1.000, sensitivity 0.620, specificity 0.860).

CONCLUSION

A radiomic model derived from contrast-enhanced CT may help stratify chemotherapy effect prediction and improve clinical decision-making.

Endometrioid adenocarcinoma: combined multiparametric MRI and tumour marker HE4 to evaluate tumour grade and lymphovascular space invasion

AIM

To assess the value of semi-quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and quantitative diffusion-weighted imaging parameters combined with human epididymis protein 4 (HE4) in predicting the pathological grade and lymphovascular space invasion (LVSI) of endometrioid adenocarcinoma (EAC).

MATERIALS AND METHODS

Between October 2018 and December 2021, 60 women (mean age, 55 [range, 32-77] years) with EAC underwent preoperative pelvic MRI and HE4 level measurements. The positive enhancement integral (PEI), time to peak, maximum slope of increase (MSI), and maximum slope of decrease were measured by manually drawing a region of interest on the neoplastic tissue. The receiver operating characteristic curve was used to calculate the diagnostic efficiency of the single parameter and combined factors.

RESULTS

Lower apparent diffusion coefficients (ADCs) were observed in high-grade tumours (G3) than in low-grade tumours (G1/G2). PEI, MSI, and HE4 levels were higher in the high-grade tumours than in the low-grade tumours (p<0.05). The area under the curve (AUC) for G3 diagnosis using multiparametric MRI combined with HE4 was 0.929. ADC values were significantly lower in the EAC with LVSI than in those without LVSI. Tumours with LVSI showed higher PEI and HE4 levels than those without LVSI (p<0.05). The AUC for LVSI-positive diagnosis using multiparametric MRI combined with HE4 was 0.814.

CONCLUSION

Semi-quantitative DCE-MRI, ADC values, and serum HE4 levels can be used to predict tumour grade and LVSI, and the prediction efficiency of multiparametric MRI combined with serum HE4 is better than that of any single factor.

The application of radiomics in esophageal cancer: Predicting the response after neoadjuvant therapy

Esophageal cancer (EC) is one of the fatal malignant neoplasms worldwide. Neoadjuvant therapy (NAT) combined with surgery has become the standard treatment for locally advanced EC. However, the treatment efficacy for patients with EC who received NAT varies from patient to patient. Currently, the evaluation of efficacy after NAT for EC lacks accurate and uniform criteria. Radiomics is a multi-parameter quantitative approach for developing medical imaging in the era of precision medicine and has provided a novel view of medical images. As a non-invasive image analysis method, radiomics is an inevitable trend in NAT efficacy prediction and prognosis classification of EC by analyzing the high-throughput imaging features of lesions extracted from medical images. In this literature review, we discuss the definition and workflow of radiomics, the advances in efficacy prediction after NAT, and the current application of radiomics for predicting efficacy after NAT.

Role of radiomics in the diagnosis and treatment of gastrointestinal cancer

Gastrointestinal cancer (GIC) has high morbidity and mortality as one of the main causes of cancer death. Preoperative risk stratification is critical to guide patient management, but traditional imaging studies have difficulty predicting its biological behavior. The emerging field of radiomics allows the conversion of potential pathophysiological information in existing medical images that cannot be visually recognized into high-dimensional quantitative image features. Tumor lesion characterization, therapeutic response evaluation, and survival prediction can be achieved by analyzing the relationships between these features and clinical and genetic data. In recent years, the clinical application of radiomics to GIC has increased dramatically. In this editorial, we describe the latest progress in the application of radiomics to GIC and discuss the value of its potential clinical applications, as well as its limitations and future directions.