Multiple Level CT Radiomics Features Preoperatively Predict Lymph Node Metastasis in Esophageal Cancer: A Multicentre Retrospective Study

Tumor-lymph cross-plane projection reveals spatial relationship features: a ResNet-CBAM model for prognostic prediction in esophageal cancer

Background

Prognostic models for esophageal cancer based on contrast-enhanced chest CT can aid thoracic surgeons in developing personalized treatment plans to optimize patient outcomes. However, the extensive lymphatic drainage and early lymph node metastasis of the esophagus present significant challenges in extracting and analyzing meaningful lymph node characteristics. Previous studies have primarily focused on tumor and lymph node features separately, overlooking spatial correlations such as position, direction, and volumetric ratio.

Methods

A total of 285 patients who underwent radical resection surgery at Fujian Provincial Hospital from 2018 to 2022 were retrospectively analyzed. This study introduced a tumor-lymph node projection plane, created by projecting lymph node ROIs onto the tumor ROI plane. A ResNet-CBAM model, integrating a residual convolutional neural network with a CBAM attention module, was employed for feature extraction and survival prediction. The PJ group utilized tumor-lymph node projection planes as training data, while the TM and ZC groups utilized tumor ROIs and concatenated images of tumor and lymph node ROIs, respectively, as controls. Additional comparisons were made with traditional machine learning models (support vector machines, logistic regression, and K-nearest neighbors). Survival outcomes (median, 1-year, 3-year, 5-year) were used as target labels to evaluate model performance in distinguishing high-risk patients and predicting both short- and long-term survival.

Results

In the PJ group, the ResNet-CBAM model achieved accuracy rates of 0.766, 0.981, 0.883, and 0.778 for predicting median, 1-year, 3-year, and 5-year survival, respectively. Its corresponding AUC values for 1-, 3-, and 5-year survival were 0.992, 0.913, and 0.835. Kaplan-Meier survival analysis revealed significant differences between high- and low-risk groups identified by the model. The ResNet-CBAM model outperformed those in the TM and ZC groups in distinguishing high-risk patients and predicting both short- and long-term survival. Compared to machine learning models, it demonstrated superior performance in long-term survival prediction.

Conclusion

The ResNet-CBAM model trained on tumor-lymph projection planes effectively distinguished high-risk esophageal cancer patients and outperformed traditional models in predicting survival outcomes. By capturing spatial relationships between tumors and lymph nodes, it demonstrated enhanced predictive efficiency.

Established the prediction model of early-stage non-small cell lung cancer spread through air spaces (STAS) by radiomics and genomics features

BACKGROUND

This study was aimed to establish a prediction model for spread through air spaces (STAS) in early-stage non-small cell lung cancer based on imaging and genomic features.

METHODS

We retrospectively collected 204 patients (47 STAS+ and 157 STAS-) with non-small cell lung cancer who underwent surgical treatment in the Jinling Hospital from January 2021 to December 2021. Their preoperative CT images, genetic testing data (including next-generation sequencing data from other hospitals), and clinical data were collected. Patients were randomly divided into training and testing cohorts (7:3).

RESULTS

The study included a total of 204 eligible patients. STAS were found in 47 (23.0%) patients, and no STAS were found in 157 (77.0%) patients. The receiver operating characteristic curve showed that radiomics model, clinical genomics model, and mixed model had good predictive performance (area under the curve [AUC] = 0.85; AUC = 0.70; AUC = 0.85).

CONCLUSIONS

The prediction model based on radiomics and genomics features has a good prediction performance for STAS.

Application of artificial intelligence model in pathological staging and prognosis of clear cell renal cell carcinoma

This study aims to develop a deep learning (DL) model based on whole-slide images (WSIs) to predict the pathological stage of clear cell renal cell carcinoma (ccRCC). The histopathological images of 513 ccRCC patients were downloaded from The Cancer Genome Atlas (TCGA) database and randomly divided into training set and validation set according to the ratio of 8∶2. The CLAM algorithm was used to establish the DL model, and the stability of the model was evaluated in the external validation set. DL features were extracted from the model to construct a prognostic risk model, which was validated in an external dataset. The results showed that the DL model showed excellent prediction ability with an area under the curve (AUC) of 0.875 and an average accuracy score of 0.809, indicating that the model could reliably distinguish ccRCC patients at different stages from histopathological images. In addition, the prognostic risk model constructed by DL characteristics showed that the overall survival rate of patients in the high-risk group was significantly lower than that in the low-risk group (P = 0.003), and AUC values for predicting 1-, 3- and 5-year overall survival rates were 0.68, 0.69 and 0.69, respectively, indicating that the prediction model had high sensitivity and specificity. The results of the validation set are consistent with the above results. Therefore, DL model can accurately predict the pathological stage and prognosis of ccRCC patients, and provide certain reference value for clinical diagnosis.

Automatic segmentation of esophageal cancer, metastatic lymph nodes and their adjacent structures in CTA images based on the UperNet Swin network

OBJECTIVE

To create a deep-learning automatic segmentation model for esophageal cancer (EC), metastatic lymph nodes (MLNs) and their adjacent structures using the UperNet Swin network and computed tomography angiography (CTA) images and to improve the effectiveness and precision of EC automatic segmentation and TN stage diagnosis.

METHODS

Attention U-Net, UperNet Swin, UNet++ and UNet were used to train the EC segmentation model to automatically segment the EC, esophagus, pericardium, aorta and MLN from CTA images of 182 patients with postoperative pathologically proven EC. The Dice similarity coefficient (DSC), sensitivity, and positive predictive value (PPV) were used to assess their segmentation effectiveness. The volume of EC was calculated using the segmentation results, and the outcomes and times of automatic and human segmentation were compared. All statistical analyses were completed using SPSS 25.0 software.

RESULTS

Among the four EC autosegmentation models, the UperNet Swin had the best autosegmentation results with a DSC of 0.7820 and the highest values of EC sensitivity and PPV. The esophagus, pericardium, aorta and MLN had DSCs of 0.7298, 0.9664, 0.9496 and 0.5091. The DSCs of the UperNet Swin were 0.6164, 0.7842, 0.8190, and 0.7259 for T1-4 EC. The volume of EC and its adjacent structures between the ground truth and UperNet Swin model were not significantly different.

CONCLUSIONS

The UperNet Swin showed excellent efficiency in autosegmentation and volume measurement of EC, MLN and its adjacent structures in different T stage, which can help to T and N stage diagnose EC and will save clinicians time and energy.

CT-based different regions of interest radiomics analysis for acute radiation pneumonitis in patients with locally advanced NSCLC after chemoradiotherapy

Purpose

To establish a radiomics model using radiomics features from different region of interests (ROI) based on dosimetry-related regions in enhanced computed tomography (CT) simulated images to predict radiation pneumonitis (RP) in patients with non-small cell lung cancer (NSCLC).

Methods

Our retrospective study was conducted based on a cohort of 236 NSCLC patients (59 of them with RP≥2) who were treated in 2 institutions and divided into the primary cohort (n = 182,46 of them with RP≥2) and external validation cohort (n = 54,13 of them with RP≥2). Radiomic features extracted from three ROIs were defined as the whole lung (WL), the dose volume histogram (DVH) of the lung V20 (V20_Lung) and the DVH of the V30 of lung minus the planning target volume (PTV) (V30 Lung-PTV). A total of 107 radiomics features were extracted from each ROIs. The U test, correlation coefficient and least absolute shrinkage and selection operator (LASSO) were performed for features selection. Six models based on different classification algorithms were developed to select the best radiomics model (R model).In addition, we built a dosimetry model then combined it with the best R model to create a mixed model (R+D model) The receiver operating characteristic (ROC) curve was delineated to assess the predictive efficacy of the models. Decision curve analysis could benefit from the model proposals through the assessment of clinical utility.

Results

Among the three ROIs, the best R model constructed from the LightGBM algorithm demonstrated the strongest discriminative ability in the ROI of V30 Lung-PTV. The corresponding area under the curve (AUC) value was 0.930 (95 % confidence interval (CI): 0.829-0.941). The D model, R model and R+D model achieved AUC values of 0.798 (95 %CI: 0.732-0.865), 0.930 (95 %CI: 0.829-0.941) and 0.940 (95 %CI: 0.906-0.974) in primary cohort, and in external validation cohort, the AUC values were 0.793 (95 %CI:0.637-0.949), 0.887 (95 %CI:0.810-0.993), 0.951 (95CI%:0.891-1.000). Decision curve demonstrate that R+D model could benefit for patients through the assessment of clinical utility.

Conclusion

The radiomics model was able to predict the acute RP more effectively in comparison with the traditional dosimetry model. Especially the radiomics model based on the V30 Lung-PTV region was able to achieve a higher accuracy when compared to the other regions.

Impact of artificial intelligence in the management of esophageal, gastric and colorectal malignancies

The incidence of gastrointestinal malignancies has increased over the past decade at an alarming rate. Colorectal and gastric cancers are the third and fifth most commonly diagnosed cancers worldwide but are cited as the second and third leading causes of mortality. Early institution of appropriate therapy from timely diagnosis can optimize patient outcomes. Artificial intelligence (AI)-assisted diagnostic, prognostic, and therapeutic tools can assist in expeditious diagnosis, treatment planning/response prediction, and post-surgical prognostication. AI can intercept neoplastic lesions in their primordial stages, accurately flag suspicious and/or inconspicuous lesions with greater accuracy on radiologic, histopathological, and/or endoscopic analyses, and eliminate over-dependence on clinicians. AI-based models have shown to be on par, and sometimes even outperformed experienced gastroenterologists and radiologists. Convolutional neural networks (state-of-the-art deep learning models) are powerful computational models, invaluable to the field of precision oncology. These models not only reliably classify images, but also accurately predict response to chemotherapy, tumor recurrence, metastasis, and survival rates post-treatment. In this systematic review, we analyze the available evidence about the diagnostic, prognostic, and therapeutic utility of artificial intelligence in gastrointestinal oncology.

Artificial intelligence in gastrointestinal endoscopy: a comprehensive review

Integrating artificial intelligence (AI) into gastrointestinal (GI) endoscopy heralds a significant leap forward in managing GI disorders. AI-enabled applications, such as computer-aided detection and computer-aided diagnosis, have significantly advanced GI endoscopy, improving early detection, diagnosis and personalized treatment planning. AI algorithms have shown promise in the analysis of endoscopic data, critical in conditions with traditionally low diagnostic sensitivity, such as indeterminate biliary strictures and pancreatic cancer. Convolutional neural networks can markedly improve the diagnostic process when integrated with cholangioscopy or endoscopic ultrasound, especially in the detection of malignant biliary strictures and cholangiocarcinoma. AI's capacity to analyze complex image data and offer real-time feedback can streamline endoscopic procedures, reduce the need for invasive biopsies, and decrease associated adverse events. However, the clinical implementation of AI faces challenges, including data quality issues and the risk of overfitting, underscoring the need for further research and validation. As the technology matures, AI is poised to become an indispensable tool in the gastroenterologist's arsenal, necessitating the integration of robust, validated AI applications into routine clinical practice. Despite remarkable advances, challenges such as operator-dependent accuracy and the need for intricate examinations persist. This review delves into the transformative role of AI in enhancing endoscopic diagnostic accuracy, particularly highlighting its utility in the early detection and personalized treatment of GI diseases.

Radiomics-clinical nomogram for preoperative lymph node metastasis prediction in esophageal carcinoma

OBJECTIVES

This research aimed to develop a radiomics-clinical nomogram based on enhanced thin-section CT radiomics and clinical features for the purpose of predicting the presence or absence of metastasis in lymph nodes among patients with resectable esophageal squamous cell carcinoma (ESCC).

METHODS

This study examined the data of 256 patients with ESCC, including 140 cases with lymph node metastasis. Clinical information was gathered for each case, and radiomics features were derived from thin-section contrast-enhanced CT with the help of a 3D slicer. To validate risk factors that are independent of the clinical and radiomics models, least absolute shrinkage and selection operator logistic regression analysis was used. A nomogram pattern was constructed based on the radiomics features and clinical characteristics. The receiver operating characteristic curve and Brier Score were used to evaluate the model's discriminatory ability, the calibration plot to evaluate the model's calibration, and the decision curve analysis to evaluate the model's clinical utility. The confusion matrix was used to evaluate the applicability of the model. To evaluate the efficacy of the model, 1000 rounds of 5-fold cross-validation were conducted.

RESULTS

The clinical model identified esophageal wall thickness and clinical T (cT) stage as independent risk factors, whereas the radiomics pattern was built based on 4 radiomics features chosen at random. Area under the curve (AUC) values of 0.684 and 0.701 are observed for the radiomics approach and clinical model, respectively. The AUC of nomogram combining radiomics and clinical features was 0.711. The calibration plot showed good agreement between the incidence of lymph node metastasis predicted by the nomogram and the actual probability of occurrence. The nomogram model displayed acceptable levels of performance. After 1000 rounds of 5-fold cross-validation, the AUC and Brier score had median values of 0.702 (IQR: 0.65, 7.49) and 0.21 (IQR: 0.20, 0.23), respectively. High-risk patients (risk point >110) were found to have an increased risk of lymph node metastasis [odds ratio (OR) = 5.15, 95% CI, 2.95-8.99] based on the risk categorization.

CONCLUSION

A successful preoperative prediction performance for metastasis to the lymph nodes among patients with ESCC was demonstrated by the nomogram that incorporated CT radiomics, wall thickness, and cT stage.

ADVANCES IN KNOWLEDGE

This study demonstrates a novel radiomics-clinical nomogram for lymph node metastasis prediction in ESCC, which helps physicians determine lymph node status preoperatively.

Performance of CT-based deep learning in diagnostic assessment of suspicious lateral lymph nodes in papillary thyroid cancer: a prospective diagnostic study

BACKGROUND

Preoperative evaluation of the metastasis status of lateral lymph nodes (LNs) in papillary thyroid cancer is challenging. Strategies for using deep learning to diagnosis of lateral LN metastasis require additional development and testing. This study aimed to build a deep learning-based model to distinguish benign lateral LNs from metastatic lateral LNs in papillary thyroid cancer and test the model's diagnostic performance in a real-world clinical setting.

METHODS

This was a prospective diagnostic study. An ensemble model integrating a three-dimensional residual network algorithm with clinical risk factors available before surgery was developed based on computed tomography images of lateral LNs in an internal dataset and validated in two external datasets. The diagnostic performance of the ensemble model was tested and compared with the results of fine-needle aspiration (FNA) (used as the standard reference method) and the diagnoses made by two senior radiologists in 113 suspicious lateral LNs in patients enrolled prospectively.

RESULTS

The area under the receiver operating characteristic curve of the ensemble model for diagnosing suspicious lateral LNs was 0.829 (95% CI: 0.732-0.927). The sensitivity and specificity of the ensemble model were 0.839 (95% CI: 0.762-0.916) and 0.769 (95% CI: 0.607-0.931), respectively. The diagnostic accuracy of the ensemble model was 82.3%. With FNA results as the criterion standard, the ensemble model had excellent diagnostic performance ( P =0.115), similar to that of the two senior radiologists ( P =1.000 and P =0.392, respectively).

CONCLUSION

A three-dimensional residual network-based ensemble model was successfully developed for the diagnostic assessment of suspicious lateral LNs and achieved diagnostic performance similar to that of FNA and senior radiologists. The model appears promising for clinical application.

A radiomics nomogram for predicting postoperative recurrence in esophageal squamous cell carcinoma

Purpose

To establish and validate a radiomics nomogram for predicting recurrence of esophageal squamous cell carcinoma (ESCC) after esophagectomy with curative intent.

Materials and methods

The medical records of 155 patients who underwent surgical treatment for pathologically confirmed ESCC were collected. Patients were randomly divided into a training group (n=109) and a validation group (n=46) in a 7:3 ratio. ​Tumor regions are accurately segmented in computed tomography images of enrolled patients. Radiomic features were then extracted from the segmented tumors. We selected the features by Max-relevance and min-redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) methods. A radiomics signature was then built by logistic regression analysis. To improve predictive performance, a radiomics nomogram that incorporated the radiomics signature and independent clinical predictors was built. Model performance was evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analyses (DCA).

Results

We selected the five most relevant radiomics features to construct the radiomics signature. The radiomics model had general discrimination ability with an area under the ROC curve (AUC) of 0.79 in the training set that was verified by an AUC of 0.76 in the validation set. The radiomics nomogram consisted of the radiomics signature, and N stage showed excellent predictive performance in the training and validation sets with AUCs of 0.85 and 0.83, respectively. Furthermore, calibration curves and the DCA analysis demonstrated good fit and clinical utility of the radiomics nomogram.

Conclusion

We successfully established and validated a prediction model that combined radiomics features and N stage, which can be used to predict four-year recurrence risk in patients with ESCC who undergo surgery.

Artificial intelligence-based computer-aided diagnosis system supports diagnosis of lymph node metastasis in esophageal squamous cell carcinoma: A multicenter study

Background

This study aimed to develop an artificial intelligence-based computer-aided diagnosis system (AI-CAD) emulating the diagnostic logic of radiologists for lymph node metastasis (LNM) in esophageal squamous cell carcinoma (ESCC) patients, which contributed to clinical treatment decision-making.

Methods

A total of 689 ESCC patients with PET/CT images were enrolled from three hospitals and divided into a training cohort and two external validation cohorts. 452 CT images from three publicly available datasets were also included for pretraining the model. Anatomic information from CT images was first obtained automatically using a U-Net-based multi-organ segmentation model, and metabolic information from PET images was subsequently extracted using a gradient-based approach. AI-CAD was developed in the training cohort and externally validated in two validation cohorts.

Results

The AI-CAD achieved an accuracy of 0.744 for predicting pathological LNM in the external cohort and a good agreement with a human expert in two external validation cohorts (kappa = 0.674 and 0.587, p < 0.001). With the aid of AI-CAD, the human expert's diagnostic performance for LNM was significantly improved (accuracy [95% confidence interval]: 0.712 [0.669-0.758] vs. 0.833 [0.797-0.865], specificity [95% confidence interval]: 0.697 [0.636-0.753] vs. 0.891 [0.851-0.928]; p < 0.001) among patients underwent lymphadenectomy in the external validation cohorts.

Conclusions

The AI-CAD could aid in preoperative diagnosis of LNM in ESCC patients and thereby support clinical treatment decision-making.

A CT-Based Radiomics Nomogram Model for Differentiating Primary Malignant Melanoma of the Esophagus from Esophageal Squamous Cell Carcinoma

Objective

The diagnosis of primary malignant melanoma of the esophagus (PMME) before treatment is essential for clinical decision-making. However, PMME may be misdiagnosed as esophageal squamous cell carcinoma (ESCC) sometimes. This research is aimed at devising a radiomics nomogram model of CT for distinguishing PMME from ESCC.

Methods

In this retrospective analysis, 122 individuals with proven pathologically PMME (n = 28) and ESCC (n = 94) were registered from our hospital. PyRadiomics was applied to derive radiomics features from plain and enhanced CT images after resampling image into an isotropic resolution of 0.625 × 0.625 × 0.625 mm³. The diagnostic efficiency of the model was evaluated by an independent validation group.

Results

For the purpose of differentiation between PMME and ESCC, a radiomics model was constructed using 5 radiomics features obtained from nonenhanced CT and 4 radiomics features derived from enhanced CT. A radiomics model including multiple radiomics features showed excellent discrimination efficiency with AUCs of 0.975 and 0.906 in the primary and validation cohorts, respectively. Then, a radiomics nomogram model was developed. The decision curve analysis has shown remarkable performance of this nomogram model for distinguishing PMME from ESCC.

Conclusions

The proposed radiomics nomogram model based on CT could be used for distinguishing PMME from ESCC. Moreover, this model also contributed to helping clinicians determine an appropriate treatment strategy for esophageal neoplasms.

The Applications of Artificial Intelligence in Digestive System Neoplasms: A Review

Importance

Digestive system neoplasms (DSNs) are the leading cause of cancer-related mortality with a 5-year survival rate of less than 20%. Subjective evaluation of medical images including endoscopic images, whole slide images, computed tomography images, and magnetic resonance images plays a vital role in the clinical practice of DSNs, but with limited performance and increased workload of radiologists or pathologists. The application of artificial intelligence (AI) in medical image analysis holds promise to augment the visual interpretation of medical images, which could not only automate the complicated evaluation process but also convert medical images into quantitative imaging features that associated with tumor heterogeneity.

Highlights

We briefly introduce the methodology of AI for medical image analysis and then review its clinical applications including clinical auxiliary diagnosis, assessment of treatment response, and prognosis prediction on 4 typical DSNs including esophageal cancer, gastric cancer, colorectal cancer, and hepatocellular carcinoma.

Conclusion

AI technology has great potential in supporting the clinical diagnosis and treatment decision-making of DSNs. Several technical issues should be overcome before its application into clinical practice of DSNs.

Integrating Preoperative Computed Tomography and Clinical Factors for Lymph Node Metastasis Prediction in Esophageal Squamous Cell Carcinoma by Feature-Wise Attentional Graph Neural Network

PURPOSE

This study aimed to propose a regional lymph node (LN) metastasis prediction model for patients with esophageal squamous cell carcinoma (ESCC) that can learn and adaptively integrate preoperative computed tomography (CT) image features and nonimaging clinical parameters.

METHODS AND MATERIALS

Contrast-enhanced CT scans taken 2 weeks before surgery and 20 clinical factors, including general, pathologic, hematological, and diagnostic information, were collected from 357 patients with ESCC between October 2013 and November 2018. There were 999 regional LNs (857 negative, 142 positive) with pathologically confirmed status after surgery. All LNs were randomly divided into a training set (n = 738) and a validation set (n = 261) for testing. The feature-wise attentional graph neural network (FAGNN) was composed of (1) deep image feature extraction by the encoder of 3-dimensional UNet and high-level nonimaging factor representation by the clinical parameter encoder; (2) a feature-wise attention module for feature embedding with learnable adaptive weights; and (3) a graph attention layer to integrate the embedded features for final LN level metastasis prediction.

RESULTS

Among the 4 models we constructed, FAGNN using both CT and clinical parameters as input is the model with the best performance, and the area under the curve (AUC) reaches 0.872, which is better than manual CT diagnosis method, multivariable model using CT only (AUC = 0.797), multivariable model with combined CT and clinical parameters (AUC = 0.846), and our FAGNN using CT only (AUC = 0.853).

CONCLUSIONS

Our adaptive integration model improved the metastatic LN prediction performance based on CT and clinical parameters. Our model has the potential to foster effective fusion of multisourced parameters and to support early prognosis and personalized surgery or radiation therapy planning in patients with ESCC.

Machine learning models predict overall survival and progression free survival of non-surgical esophageal cancer patients with chemoradiotherapy based on CT image radiomics signatures

PURPOSE

To construct machine learning models for predicting progression free survival (PFS) and overall survival (OS) with esophageal squamous cell carcinoma (ESCC) patients.

METHODS

204 ESCC patients were randomly divided into training cohort (n = 143) and test cohort (n = 61) according to the ratio of 7:3. Two radiomics models were constructed by radiomics features, which were selected by LASSO Cox model to predict PFS and OS, respectively. Clinical features were selected by univariate and multivariate Cox proportional hazards model (p < 0.05). Combined radiomics and clinical model was developed by selected clinical and radiomics features. The receiver operating characteristic curve, Kaplan Meier curve and nomogram were used to display the capability of constructed models.

RESULTS

There were 944 radiomics features extracted based on volume of interest in CT images. There were six radiomics features and seven clinical features for PFS prediction and three radiomics features and three clinical features for OS prediction; The radiomics models showed general performance in training cohort and test cohort for prediction for prediction PFS (AUC, 0.664, 0.676. C-index, 0.65, 0.64) and OS (AUC, 0.634, 0.646.C-index, 0.64, 0.65). The combined models displayed high performance in training cohort and test cohort for prediction PFS (AUC, 0.856, 0.833. C-index, 0.81, 0.79) and OS (AUC, 0.742, 0.768. C-index, 0.72, 0.71).

CONCLUSION

We developed combined radiomics and clinical machine learning models with better performance than radiomics or clinical alone, which were used to accurate predict 3 years PFS and OS of non-surgical ESCC patients. The prediction results could provide a reference for clinical decision.

Deep learning based radiomics for gastrointestinal cancer diagnosis and treatment: A minireview

Gastrointestinal (GI) cancers are the major cause of cancer-related mortality globally. Medical imaging is an important auxiliary means for the diagnosis, assessment and prognostic prediction of GI cancers. Radiomics is an emerging and effective technology to decipher the encoded information within medical images, and traditional machine learning is the most commonly used tool. Recent advances in deep learning technology have further promoted the development of radiomics. In the field of GI cancer, although there are several surveys on radiomics, there is no specific review on the application of deep-learning-based radiomics (DLR). In this review, a search was conducted on Web of Science, PubMed, and Google Scholar with an emphasis on the application of DLR for GI cancers, including esophageal, gastric, liver, pancreatic, and colorectal cancers. Besides, the challenges and recommendations based on the findings of the review are comprehensively analyzed to advance DLR.

Prediction of Individual Lymph Node Metastatic Status in Esophageal Squamous Cell Carcinoma Using Routine Computed Tomography Imaging: Comparison of Size-Based Measurements and Radiomics-Based Models

BACKGROUND

Lymph node status is vital for prognosis and treatment decisions for esophageal squamous cell carcinoma (ESCC). This study aimed to construct and evaluate an optimal radiomics-based method for a more accurate evaluation of individual regional lymph node status in ESCC and to compare it with traditional size-based measurements.

METHODS

The study consecutively collected 3225 regional lymph nodes from 530 ESCC patients receiving upfront surgery from January 2011 to October 2015. Computed tomography (CT) scans for individual lymph nodes were analyzed. The study evaluated the predictive performance of machine-learning models trained on features extracted from two-dimensional (2D) and three-dimensional (3D) radiomics by different contouring methods. Robust and important radiomics features were selected, and classification models were further established and validated.

RESULTS

The lymph node metastasis rate was 13.2% (427/3225). The average short-axis diameter was 6.4 mm for benign lymph nodes and 7.9 mm for metastatic lymph nodes. The division of lymph node stations into five regions according to anatomic lymph node drainage (cervical, upper mediastinal, middle mediastinal, lower mediastinal, and abdominal regions) improved the predictive performance. The 2D radiomics method showed optimal diagnostic results, with more efficient segmentation of nodal lesions. In the test set, this optimal model achieved an area under the receiver operating characteristic curve of 0.841-0.891, an accuracy of 84.2-94.7%, a sensitivity of 65.7-83.3%, and a specificity of 84.4-96.7%.

CONCLUSIONS

The 2D radiomics-based models noninvasively predicted the metastatic status of an individual lymph node in ESCC and outperformed the conventional size-based measurement. The 2D radiomics-based model could be incorporated into the current clinical workflow to enable better decision-making for treatment strategies.

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.

Radiomics models based on CT at different phases predicting lymph node metastasis of esophageal squamous cell carcinoma (GASTO-1089)

PURPOSE

To investigate the value of radiomics models based on CT at different phases (non-contrast-enhanced and contrast-enhanced images) in predicting lymph node (LN) metastasis in esophageal squamous cell carcinoma (ESCC).

METHODS AND MATERIALS

Two hundred and seventy-four eligible patients with ESCC were divided into a training set (n =193) and a validation set (n =81). The least absolute shrinkage and selection operator algorithm (LASSO) was used to select radiomics features. The predictive models were constructed with radiomics features and clinical factors through multivariate logistic regression analysis. The predictive performance and clinical application value of the models were evaluated by area under receiver operating characteristic curve (AUC) and decision curve analysis (DCA). The Delong Test was used to evaluate the differences in AUC among models.

RESULTS

Sixteen and eighteen features were respectively selected from non-contrast-enhanced CT (NECT) and contrast-enhanced CT (CECT) images. The model established using only clinical factors (Model 1) has an AUC value of 0.655 (95%CI 0.552-0.759) with a sensitivity of 0.585, a specificity of 0.725 and an accuracy of 0.654. The models contained clinical factors with radiomics features of NECT or/and CECT (Model 2,3,4) have significantly improved prediction performance. The values of AUC of Model 2,3,4 were 0.766, 0.811 and 0.809, respectively. It also achieved a great AUC of 0.800 in the model built with only radiomics features derived from NECT and CECT (Model 5). DCA suggested the potential clinical benefit of model prediction of LN metastasis of ESCC. A comparison of the receiver operating characteristic (ROC) curves using the Delong test indicated that Models 2, 3, 4, and 5 were superior to Model 1(P< 0.05), and no difference was found among Model 2, 3, 4 and Model 5(P > 0.05).

CONCLUSION

Radiomics models based on CT at different phases could accurately predict the lymph node metastasis in patients with ESCC, and their predictive efficiency was better than the clinical model based on tumor size criteria. NECT-based radiomics model could be a reasonable option for ESCC patients due to its lower price and availability for renal failure or allergic patients.

Radiomics Analysis of Lymph Nodes with Esophageal Squamous Cell Carcinoma Based on Deep Learning

Purpose

To assess the role of multiple radiomic features of lymph nodes in the preoperative prediction of lymph node metastasis (LNM) in patients with esophageal squamous cell carcinoma (ESCC).

Methods

Three hundred eight patients with pathologically confirmed ESCC were retrospectively enrolled (training cohort, n = 216; test cohort, n = 92). We extracted 207 handcrafted radiomic features and 1000 deep radiomic features of lymph nodes from their computed tomography (CT) images. The t-test and least absolute shrinkage and selection operator (LASSO) were used to reduce the dimensions and select key features. Handcrafted radiomics, deep radiomics, and clinical features were combined to construct models. Models I (handcrafted radiomic features), II (Model I plus deep radiomic features), and III (Model II plus clinical features) were built using three machine learning methods: support vector machine (SVM), adaptive boosting (AdaBoost), and random forest (RF). The best model was compared with the results of two radiologists, and its performance was evaluated in terms of sensitivity, specificity, accuracy, area under the curve (AUC), and receiver operating characteristic (ROC) curve analysis.

Results

No significant differences were observed between cohorts. Ten handcrafted and 12 deep radiomic features were selected from the extracted features (p < 0.05). Model III could discriminate between patients with and without LNM better than the diagnostic results of the two radiologists.

Conclusion

The combination of handcrafted radiomic features, deep radiomic features, and clinical features could be used clinically to assess lymph node status in patients with ESCC.

Development and validation of a deep learning model to predict survival of patients with esophageal cancer

Objective

To compare the performance of a deep learning survival network with the tumor, node, and metastasis (TNM) staging system in survival prediction and test the reliability of individual treatment recommendations provided by the network.

Methods

In this population-based cohort study, we developed and validated a deep learning survival model using consecutive cases of newly diagnosed stage I to IV esophageal cancer between January 2004 and December 2015 in a Surveillance, Epidemiology, and End Results (SEER) database. The model was externally validated in an independent cohort from Fujian Provincial Hospital. The C statistic was used to compare the performance of the deep learning survival model and TNM staging system. Two other deep learning risk prediction models were trained for treatment recommendations. A Kaplan–Meier survival curve was used to compare survival between the population that followed the recommended therapy and those who did not.

Results

A total of 9069 patients were included in this study. The deep learning network showed more promising results in predicting esophageal cancer-specific survival than the TNM stage in the internal test dataset (C-index=0.753 vs. 0.638) and external validation dataset (C-index=0.687 vs. 0.643). The population who received the recommended treatments had superior survival compared to those who did not, based on the internal test dataset (hazard ratio, 0.753; 95% CI, 0.556-0.987; P=0.042) and the external validation dataset (hazard ratio, 0.633; 95% CI, 0.459-0.834; P=0.0003).

Conclusion

Deep learning neural networks have potential advantages over traditional linear models in prognostic assessment and treatment recommendations. This novel analytical approach may provide reliable information on individual survival and treatment recommendations for patients with esophageal cancer.

3D convolutional neural network model from contrast-enhanced CT to predict spread through air spaces in non-small cell lung cancer

PURPOSE

The purpose of this study was to compare the efficacy of five non-invasive models, including three-dimensional (3D) convolutional neural network (CNN) model, to predict the spread through air spaces (STAS) status of non-small cell lung cancer (NSCLC), and to obtain the best prediction model to provide a basis for clinical surgery planning.

MATERIALS AND METHODS

A total of 203 patients (112 men, 91 women; mean age, 60 years; age range 22-80 years) with NSCLC were retrospectively included. Of these, 153 were used for training cohort and 50 for validation cohort. According to the image biomarker standardization initiative reference manual, the image processing and feature extraction were standardized using PyRadiomics. The logistic regression classifier was used to build the model. Five models (clinicopathological/CT model, conventional radiomics model, computer vision (CV) model, 3D CNN model and combined model) were constructed to predict STAS by NSCLC. Area under the receiver operating characteristic curves (AUC) were used to validate the capability of the five models to predict STAS.

RESULTS

For predicting STAS, the 3D CNN model was superior to the clinicopathological/CT model, conventional radiomics model, CV model and combined model and achieved satisfactory discrimination performance, with an AUC of 0.93 (95% CI: 0.70-0.82) in the training cohort and 0.80 (95% CI: 0.65-0.86) in the validation cohort. Decision curve analysis indicated that, when the probability of the threshold was over 10%, the 3D CNN model was beneficial for predicting STAS status compared to either treating all or treating none of the patients within certain ranges of risk threshold CONCLUSION: The 3D CNN model can be used for the preoperative prediction of STAS in patients with NSCLC, and was superior to the other four models in predicting patients' risk of developing STAS.

Can 18F-FDG PET/CT Radiomics Features Predict Clinical Outcomes in Patients with Locally Advanced Esophageal Squamous Cell Carcinoma?

Simple Summary

PET/CT is an important staging modality in the baseline assessment of locally advanced esophageal squamous cell carcinoma. Accurate staging and response prediction in these patients is essential for management. The aim of this retrospective study was to assess the usefulness of ¹⁸F-FDG PET/CT radiomics features in predicting outcomes such as tumor and nodal categories, PET-based response to induction chemotherapy, progression-free survival, and overall survival. In a final cohort of 74 patients, we found that the developed radiomics models can predict these clinical and prognostic outcomes with reasonable accuracy, similar or better than those derived from conventional imaging. Future studies with a larger cohort would be helpful in establishing the significance of these models.

Abstract

This study aimed to assess the usefulness of radiomics features of ¹⁸F-FDG PET/CT in patients with locally advanced esophageal cancers (ESCC) in predicting outcomes such as clinical tumor (cT) and nodal (cN) categories, PET response to induction chemotherapy (PET response), progression-free survival (PFS), and overall survival (OS). Pretreatment PET/CT images from patients who underwent concurrent chemoradiotherapy from July 2002 to February 2017 were segmented, and data were split into training and test sets. Model development was performed on the training datasets and a maximum of five features were selected. Final diagnostic accuracies were determined using the test dataset. A total of 86 PET/CTs (58 men and 28 women, mean age 65 years) were segmented. Due to small lesion size, 12 patients were excluded. The diagnostic accuracies as derived from the CT, PET, and combined PET/CT test datasets were as follows: cT category—70.4%, 70.4%, and 81.5%, respectively; cN category—69.0%, 86.2%, and 86.2%, respectively; PET response—60.0%, 66.7%, and 70.0%, respectively; PFS—60.7%, 75.0%, and 75.0%, respectively; and OS—51.7%, 55.2%, and 62.1%, respectively. A radiomics assessment of locally advanced ESCC has the potential to predict various clinical outcomes. External validation of these models would be further helpful.

Can 18F-FDG PET/CT Radiomics Features Predict Clinical Outcomes in Patients with Locally Advanced Esophageal Squamous Cell Carcinoma?

This study aimed to assess the usefulness of radiomics features of ¹⁸F-FDG PET/CT in patients with locally advanced esophageal cancers (ESCC) in predicting outcomes such as clinical tumor (cT) and nodal (cN) categories, PET response to induction chemotherapy (PET response), progression-free survival (PFS), and overall survival (OS). Pretreatment PET/CT images from patients who underwent concurrent chemoradiotherapy from July 2002 to February 2017 were segmented, and data were split into training and test sets. Model development was performed on the training datasets and a maximum of five features were selected. Final diagnostic accuracies were determined using the test dataset. A total of 86 PET/CTs (58 men and 28 women, mean age 65 years) were segmented. Due to small lesion size, 12 patients were excluded. The diagnostic accuracies as derived from the CT, PET, and combined PET/CT test datasets were as follows: cT category-70.4%, 70.4%, and 81.5%, respectively; cN category-69.0%, 86.2%, and 86.2%, respectively; PET response-60.0%, 66.7%, and 70.0%, respectively; PFS-60.7%, 75.0%, and 75.0%, respectively; and OS-51.7%, 55.2%, and 62.1%, respectively. A radiomics assessment of locally advanced ESCC has the potential to predict various clinical outcomes. External validation of these models would be further helpful.

Can 18F-FDG PET/CT Radiomics Features Predict Clinical Outcomes in Patients with Locally Advanced Esophageal Squamous Cell Carcinoma?

This study aimed to assess the usefulness of radiomics features of ¹⁸F-FDG PET/CT in patients with locally advanced esophageal cancers (ESCC) in predicting outcomes such as clinical tumor (cT) and nodal (cN) categories, PET response to induction chemotherapy (PET response), progression-free survival (PFS), and overall survival (OS). Pretreatment PET/CT images from patients who underwent concurrent chemoradiotherapy from July 2002 to February 2017 were segmented, and data were split into training and test sets. Model development was performed on the training datasets and a maximum of five features were selected. Final diagnostic accuracies were determined using the test dataset. A total of 86 PET/CTs (58 men and 28 women, mean age 65 years) were segmented. Due to small lesion size, 12 patients were excluded. The diagnostic accuracies as derived from the CT, PET, and combined PET/CT test datasets were as follows: cT category-70.4%, 70.4%, and 81.5%, respectively; cN category-69.0%, 86.2%, and 86.2%, respectively; PET response-60.0%, 66.7%, and 70.0%, respectively; PFS-60.7%, 75.0%, and 75.0%, respectively; and OS-51.7%, 55.2%, and 62.1%, respectively. A radiomics assessment of locally advanced ESCC has the potential to predict various clinical outcomes. External validation of these models would be further helpful.

Can 18F-FDG PET/CT Radiomics Features Predict Clinical Outcomes in Patients with Locally Advanced Esophageal Squamous Cell Carcinoma?

This study aimed to assess the usefulness of radiomics features of ¹⁸F-FDG PET/CT in patients with locally advanced esophageal cancers (ESCC) in predicting outcomes such as clinical tumor (cT) and nodal (cN) categories, PET response to induction chemotherapy (PET response), progression-free survival (PFS), and overall survival (OS). Pretreatment PET/CT images from patients who underwent concurrent chemoradiotherapy from July 2002 to February 2017 were segmented, and data were split into training and test sets. Model development was performed on the training datasets and a maximum of five features were selected. Final diagnostic accuracies were determined using the test dataset. A total of 86 PET/CTs (58 men and 28 women, mean age 65 years) were segmented. Due to small lesion size, 12 patients were excluded. The diagnostic accuracies as derived from the CT, PET, and combined PET/CT test datasets were as follows: cT category-70.4%, 70.4%, and 81.5%, respectively; cN category-69.0%, 86.2%, and 86.2%, respectively; PET response-60.0%, 66.7%, and 70.0%, respectively; PFS-60.7%, 75.0%, and 75.0%, respectively; and OS-51.7%, 55.2%, and 62.1%, respectively. A radiomics assessment of locally advanced ESCC has the potential to predict various clinical outcomes. External validation of these models would be further helpful.

Can 18F-FDG PET/CT Radiomics Features Predict Clinical Outcomes in Patients with Locally Advanced Esophageal Squamous Cell Carcinoma?

This study aimed to assess the usefulness of radiomics features of ¹⁸F-FDG PET/CT in patients with locally advanced esophageal cancers (ESCC) in predicting outcomes such as clinical tumor (cT) and nodal (cN) categories, PET response to induction chemotherapy (PET response), progression-free survival (PFS), and overall survival (OS). Pretreatment PET/CT images from patients who underwent concurrent chemoradiotherapy from July 2002 to February 2017 were segmented, and data were split into training and test sets. Model development was performed on the training datasets and a maximum of five features were selected. Final diagnostic accuracies were determined using the test dataset. A total of 86 PET/CTs (58 men and 28 women, mean age 65 years) were segmented. Due to small lesion size, 12 patients were excluded. The diagnostic accuracies as derived from the CT, PET, and combined PET/CT test datasets were as follows: cT category-70.4%, 70.4%, and 81.5%, respectively; cN category-69.0%, 86.2%, and 86.2%, respectively; PET response-60.0%, 66.7%, and 70.0%, respectively; PFS-60.7%, 75.0%, and 75.0%, respectively; and OS-51.7%, 55.2%, and 62.1%, respectively. A radiomics assessment of locally advanced ESCC has the potential to predict various clinical outcomes. External validation of these models would be further helpful.

Can 18F-FDG PET/CT Radiomics Features Predict Clinical Outcomes in Patients with Locally Advanced Esophageal Squamous Cell Carcinoma?

This study aimed to assess the usefulness of radiomics features of ¹⁸F-FDG PET/CT in patients with locally advanced esophageal cancers (ESCC) in predicting outcomes such as clinical tumor (cT) and nodal (cN) categories, PET response to induction chemotherapy (PET response), progression-free survival (PFS), and overall survival (OS). Pretreatment PET/CT images from patients who underwent concurrent chemoradiotherapy from July 2002 to February 2017 were segmented, and data were split into training and test sets. Model development was performed on the training datasets and a maximum of five features were selected. Final diagnostic accuracies were determined using the test dataset. A total of 86 PET/CTs (58 men and 28 women, mean age 65 years) were segmented. Due to small lesion size, 12 patients were excluded. The diagnostic accuracies as derived from the CT, PET, and combined PET/CT test datasets were as follows: cT category-70.4%, 70.4%, and 81.5%, respectively; cN category-69.0%, 86.2%, and 86.2%, respectively; PET response-60.0%, 66.7%, and 70.0%, respectively; PFS-60.7%, 75.0%, and 75.0%, respectively; and OS-51.7%, 55.2%, and 62.1%, respectively. A radiomics assessment of locally advanced ESCC has the potential to predict various clinical outcomes. External validation of these models would be further helpful.

Can 18F-FDG PET/CT Radiomics Features Predict Clinical Outcomes in Patients with Locally Advanced Esophageal Squamous Cell Carcinoma?

This study aimed to assess the usefulness of radiomics features of ¹⁸F-FDG PET/CT in patients with locally advanced esophageal cancers (ESCC) in predicting outcomes such as clinical tumor (cT) and nodal (cN) categories, PET response to induction chemotherapy (PET response), progression-free survival (PFS), and overall survival (OS). Pretreatment PET/CT images from patients who underwent concurrent chemoradiotherapy from July 2002 to February 2017 were segmented, and data were split into training and test sets. Model development was performed on the training datasets and a maximum of five features were selected. Final diagnostic accuracies were determined using the test dataset. A total of 86 PET/CTs (58 men and 28 women, mean age 65 years) were segmented. Due to small lesion size, 12 patients were excluded. The diagnostic accuracies as derived from the CT, PET, and combined PET/CT test datasets were as follows: cT category-70.4%, 70.4%, and 81.5%, respectively; cN category-69.0%, 86.2%, and 86.2%, respectively; PET response-60.0%, 66.7%, and 70.0%, respectively; PFS-60.7%, 75.0%, and 75.0%, respectively; and OS-51.7%, 55.2%, and 62.1%, respectively. A radiomics assessment of locally advanced ESCC has the potential to predict various clinical outcomes. External validation of these models would be further helpful.

Value of 18F-FDG PET/MRI in the Preoperative Assessment of Resectable Esophageal Squamous Cell Carcinoma: A Comparison With 18F-FDG PET/CT, MRI, and Contrast-Enhanced CT

Objectives

To investigate the value of ¹⁸F-FDG PET/MRI in the preoperative assessment of esophageal squamous cell carcinoma (ESCC) and compare it with ¹⁸F-FDG PET/CT, MRI, and CECT.

Methods

Thirty-five patients with resectable ESCC were prospectively enrolled and underwent PET/MRI, PET/CT, and CECT before surgery. The primary tumor and regional lymph nodes were assessed by PET/MRI, PET/CT, MRI, and CECT, respectively, and the diagnostic efficiencies were determined with postoperative pathology as a reference standard. The predictive role of imaging and clinical parameters on pathological staging was analyzed.

Results

For primary tumor staging, the accuracy of PET/MRI, MRI, and CECT was 85.7%, 77.1%, and 51.4%, respectively. For lymph node assessment, the accuracy of PET/MRI, PET/CT, MRI, and CECT was 96.2%, 92.0%, 86.8%, and 86.3%, respectively, and the AUCs were 0.883, 0.745, 0.697, and 0.580, respectively. PET/MRI diagnosed 13, 7, and 6 more stations of lymph node metastases than CECT, MRI, and PET/CT, respectively. There was a significant difference in SUV_max, TLG, and tumor wall thickness between T1-2 and T3 tumors (p = 0.004, 0.024, and < 0.001, respectively). Multivariate analysis showed that thicker tumor wall thickness was a predictor of a higher T stage (p = 0.040, OR = 1.6).

Conclusions

¹⁸F-FDG PET/MRI has advantages over ¹⁸F-FDG PET/CT, MRI, and CECT in the preoperative assessment of primary tumors and regional lymph nodes of ESCC. ¹⁸F-FDG PET/MRI may be a potential supplement or alternative imaging method for preoperative staging of ESCC.

Value of 18F-FDG PET/MRI in the Preoperative Assessment of Resectable Esophageal Squamous Cell Carcinoma: A Comparison With 18F-FDG PET/CT, MRI, and Contrast-Enhanced CT

Objectives

To investigate the value of ¹⁸F-FDG PET/MRI in the preoperative assessment of esophageal squamous cell carcinoma (ESCC) and compare it with ¹⁸F-FDG PET/CT, MRI, and CECT.

Methods

Thirty-five patients with resectable ESCC were prospectively enrolled and underwent PET/MRI, PET/CT, and CECT before surgery. The primary tumor and regional lymph nodes were assessed by PET/MRI, PET/CT, MRI, and CECT, respectively, and the diagnostic efficiencies were determined with postoperative pathology as a reference standard. The predictive role of imaging and clinical parameters on pathological staging was analyzed.

Results

For primary tumor staging, the accuracy of PET/MRI, MRI, and CECT was 85.7%, 77.1%, and 51.4%, respectively. For lymph node assessment, the accuracy of PET/MRI, PET/CT, MRI, and CECT was 96.2%, 92.0%, 86.8%, and 86.3%, respectively, and the AUCs were 0.883, 0.745, 0.697, and 0.580, respectively. PET/MRI diagnosed 13, 7, and 6 more stations of lymph node metastases than CECT, MRI, and PET/CT, respectively. There was a significant difference in SUV_max, TLG, and tumor wall thickness between T1-2 and T3 tumors (p = 0.004, 0.024, and < 0.001, respectively). Multivariate analysis showed that thicker tumor wall thickness was a predictor of a higher T stage (p = 0.040, OR = 1.6).

Conclusions

¹⁸F-FDG PET/MRI has advantages over ¹⁸F-FDG PET/CT, MRI, and CECT in the preoperative assessment of primary tumors and regional lymph nodes of ESCC. ¹⁸F-FDG PET/MRI may be a potential supplement or alternative imaging method for preoperative staging of ESCC.

Value of 18F-FDG PET/MRI in the Preoperative Assessment of Resectable Esophageal Squamous Cell Carcinoma: A Comparison With 18F-FDG PET/CT, MRI, and Contrast-Enhanced CT

Objectives

To investigate the value of ¹⁸F-FDG PET/MRI in the preoperative assessment of esophageal squamous cell carcinoma (ESCC) and compare it with ¹⁸F-FDG PET/CT, MRI, and CECT.

Methods

Thirty-five patients with resectable ESCC were prospectively enrolled and underwent PET/MRI, PET/CT, and CECT before surgery. The primary tumor and regional lymph nodes were assessed by PET/MRI, PET/CT, MRI, and CECT, respectively, and the diagnostic efficiencies were determined with postoperative pathology as a reference standard. The predictive role of imaging and clinical parameters on pathological staging was analyzed.

Results

For primary tumor staging, the accuracy of PET/MRI, MRI, and CECT was 85.7%, 77.1%, and 51.4%, respectively. For lymph node assessment, the accuracy of PET/MRI, PET/CT, MRI, and CECT was 96.2%, 92.0%, 86.8%, and 86.3%, respectively, and the AUCs were 0.883, 0.745, 0.697, and 0.580, respectively. PET/MRI diagnosed 13, 7, and 6 more stations of lymph node metastases than CECT, MRI, and PET/CT, respectively. There was a significant difference in SUV_max, TLG, and tumor wall thickness between T1-2 and T3 tumors (p = 0.004, 0.024, and < 0.001, respectively). Multivariate analysis showed that thicker tumor wall thickness was a predictor of a higher T stage (p = 0.040, OR = 1.6).

Conclusions

¹⁸F-FDG PET/MRI has advantages over ¹⁸F-FDG PET/CT, MRI, and CECT in the preoperative assessment of primary tumors and regional lymph nodes of ESCC. ¹⁸F-FDG PET/MRI may be a potential supplement or alternative imaging method for preoperative staging of ESCC.

Value of 18F-FDG PET/MRI in the Preoperative Assessment of Resectable Esophageal Squamous Cell Carcinoma: A Comparison With 18F-FDG PET/CT, MRI, and Contrast-Enhanced CT

Objectives

To investigate the value of ¹⁸F-FDG PET/MRI in the preoperative assessment of esophageal squamous cell carcinoma (ESCC) and compare it with ¹⁸F-FDG PET/CT, MRI, and CECT.

Methods

Thirty-five patients with resectable ESCC were prospectively enrolled and underwent PET/MRI, PET/CT, and CECT before surgery. The primary tumor and regional lymph nodes were assessed by PET/MRI, PET/CT, MRI, and CECT, respectively, and the diagnostic efficiencies were determined with postoperative pathology as a reference standard. The predictive role of imaging and clinical parameters on pathological staging was analyzed.

Results

For primary tumor staging, the accuracy of PET/MRI, MRI, and CECT was 85.7%, 77.1%, and 51.4%, respectively. For lymph node assessment, the accuracy of PET/MRI, PET/CT, MRI, and CECT was 96.2%, 92.0%, 86.8%, and 86.3%, respectively, and the AUCs were 0.883, 0.745, 0.697, and 0.580, respectively. PET/MRI diagnosed 13, 7, and 6 more stations of lymph node metastases than CECT, MRI, and PET/CT, respectively. There was a significant difference in SUV_max, TLG, and tumor wall thickness between T1-2 and T3 tumors (p = 0.004, 0.024, and < 0.001, respectively). Multivariate analysis showed that thicker tumor wall thickness was a predictor of a higher T stage (p = 0.040, OR = 1.6).

Conclusions

¹⁸F-FDG PET/MRI has advantages over ¹⁸F-FDG PET/CT, MRI, and CECT in the preoperative assessment of primary tumors and regional lymph nodes of ESCC. ¹⁸F-FDG PET/MRI may be a potential supplement or alternative imaging method for preoperative staging of ESCC.

Value of 18F-FDG PET/MRI in the Preoperative Assessment of Resectable Esophageal Squamous Cell Carcinoma: A Comparison With 18F-FDG PET/CT, MRI, and Contrast-Enhanced CT

Objectives

To investigate the value of ¹⁸F-FDG PET/MRI in the preoperative assessment of esophageal squamous cell carcinoma (ESCC) and compare it with ¹⁸F-FDG PET/CT, MRI, and CECT.

Methods

Thirty-five patients with resectable ESCC were prospectively enrolled and underwent PET/MRI, PET/CT, and CECT before surgery. The primary tumor and regional lymph nodes were assessed by PET/MRI, PET/CT, MRI, and CECT, respectively, and the diagnostic efficiencies were determined with postoperative pathology as a reference standard. The predictive role of imaging and clinical parameters on pathological staging was analyzed.

Results

For primary tumor staging, the accuracy of PET/MRI, MRI, and CECT was 85.7%, 77.1%, and 51.4%, respectively. For lymph node assessment, the accuracy of PET/MRI, PET/CT, MRI, and CECT was 96.2%, 92.0%, 86.8%, and 86.3%, respectively, and the AUCs were 0.883, 0.745, 0.697, and 0.580, respectively. PET/MRI diagnosed 13, 7, and 6 more stations of lymph node metastases than CECT, MRI, and PET/CT, respectively. There was a significant difference in SUV_max, TLG, and tumor wall thickness between T1-2 and T3 tumors (p = 0.004, 0.024, and < 0.001, respectively). Multivariate analysis showed that thicker tumor wall thickness was a predictor of a higher T stage (p = 0.040, OR = 1.6).

Conclusions

¹⁸F-FDG PET/MRI has advantages over ¹⁸F-FDG PET/CT, MRI, and CECT in the preoperative assessment of primary tumors and regional lymph nodes of ESCC. ¹⁸F-FDG PET/MRI may be a potential supplement or alternative imaging method for preoperative staging of ESCC.

Value of 18F-FDG PET/MRI in the Preoperative Assessment of Resectable Esophageal Squamous Cell Carcinoma: A Comparison With 18F-FDG PET/CT, MRI, and Contrast-Enhanced CT

Objectives

To investigate the value of ¹⁸F-FDG PET/MRI in the preoperative assessment of esophageal squamous cell carcinoma (ESCC) and compare it with ¹⁸F-FDG PET/CT, MRI, and CECT.

Methods

Thirty-five patients with resectable ESCC were prospectively enrolled and underwent PET/MRI, PET/CT, and CECT before surgery. The primary tumor and regional lymph nodes were assessed by PET/MRI, PET/CT, MRI, and CECT, respectively, and the diagnostic efficiencies were determined with postoperative pathology as a reference standard. The predictive role of imaging and clinical parameters on pathological staging was analyzed.

Results

For primary tumor staging, the accuracy of PET/MRI, MRI, and CECT was 85.7%, 77.1%, and 51.4%, respectively. For lymph node assessment, the accuracy of PET/MRI, PET/CT, MRI, and CECT was 96.2%, 92.0%, 86.8%, and 86.3%, respectively, and the AUCs were 0.883, 0.745, 0.697, and 0.580, respectively. PET/MRI diagnosed 13, 7, and 6 more stations of lymph node metastases than CECT, MRI, and PET/CT, respectively. There was a significant difference in SUV_max, TLG, and tumor wall thickness between T1-2 and T3 tumors (p = 0.004, 0.024, and < 0.001, respectively). Multivariate analysis showed that thicker tumor wall thickness was a predictor of a higher T stage (p = 0.040, OR = 1.6).

Conclusions

¹⁸F-FDG PET/MRI has advantages over ¹⁸F-FDG PET/CT, MRI, and CECT in the preoperative assessment of primary tumors and regional lymph nodes of ESCC. ¹⁸F-FDG PET/MRI may be a potential supplement or alternative imaging method for preoperative staging of ESCC.

Value of 18F-FDG PET/MRI in the Preoperative Assessment of Resectable Esophageal Squamous Cell Carcinoma: A Comparison With 18F-FDG PET/CT, MRI, and Contrast-Enhanced CT

Objectives

To investigate the value of ¹⁸F-FDG PET/MRI in the preoperative assessment of esophageal squamous cell carcinoma (ESCC) and compare it with ¹⁸F-FDG PET/CT, MRI, and CECT.

Methods

Thirty-five patients with resectable ESCC were prospectively enrolled and underwent PET/MRI, PET/CT, and CECT before surgery. The primary tumor and regional lymph nodes were assessed by PET/MRI, PET/CT, MRI, and CECT, respectively, and the diagnostic efficiencies were determined with postoperative pathology as a reference standard. The predictive role of imaging and clinical parameters on pathological staging was analyzed.

Results

For primary tumor staging, the accuracy of PET/MRI, MRI, and CECT was 85.7%, 77.1%, and 51.4%, respectively. For lymph node assessment, the accuracy of PET/MRI, PET/CT, MRI, and CECT was 96.2%, 92.0%, 86.8%, and 86.3%, respectively, and the AUCs were 0.883, 0.745, 0.697, and 0.580, respectively. PET/MRI diagnosed 13, 7, and 6 more stations of lymph node metastases than CECT, MRI, and PET/CT, respectively. There was a significant difference in SUV_max, TLG, and tumor wall thickness between T1-2 and T3 tumors (p = 0.004, 0.024, and < 0.001, respectively). Multivariate analysis showed that thicker tumor wall thickness was a predictor of a higher T stage (p = 0.040, OR = 1.6).

Conclusions

¹⁸F-FDG PET/MRI has advantages over ¹⁸F-FDG PET/CT, MRI, and CECT in the preoperative assessment of primary tumors and regional lymph nodes of ESCC. ¹⁸F-FDG PET/MRI may be a potential supplement or alternative imaging method for preoperative staging of ESCC.

Development and validation a radiomics nomogram for diagnosing occult brain metastases in patients with stage IV lung adenocarcinoma

Background

To develop and validate a radiomics model using computed tomography (CT) images acquired from the first diagnosis to estimate the status of occult brain metastases (BM) in patients with stage IV lung adenocarcinoma (LADC).

Methods

One hundred and ninety-three patients who were first diagnosed with stage IV LADC were enrolled and divided into a training cohort (n=135) and a validation cohort (n=58). Then, 725 radiomic features were extracted from contoured primary tumor volumes of LADCs. Intra- and interobserver reliabilities were calculated, and the least absolute shrinkage and selection operator (LASSO) was applied for feature selection. Subsequently, a radiomics signature (Rad-Score) was built. To improve performance, a nomogram incorporating a radiomics signature and an independent clinical predictor was developed. Finally, the established signature and nomogram were assessed using receiver operating characteristic (ROC) curves and precision-recall curves (PRC). Both empirical and α-binomial model-based ROCs and PRCs were plotted, and the area under the curve (AUC) and average precision (AP) of ROCs and PRCs were calculated and compared.

Results

A radiomics signature and Rad-Score were constructed using eight radiomic features, and these had significant correlations with occult BM status. A nomogram was developed by incorporating a Rad-Score and the primary tumor location. The nomogram yielded an optimal AUC of 0.911 [95% confidence interval (CI): 0.903–0.919] and an AP of 0.885 (95% CI: 0.876–0.894) in the training cohort, and an AUC of 0.873 (95% CI: 0.866–0.80) and an AP of 0.827 (95% CI: 0.820–0.834) in the validation cohort using α-binomial model-based method. The calibration curve demonstrated that the nomogram showed high agreement between the actual occult BM probability and predicted by the nomogram (P=0.427).

Conclusions

The nomogram incorporating a radiomics signature and a clinical risk factor achieved optimal performance after holistic assessment using unbiased indexes for diagnosing occult BM of patients who were first diagnosed with stage IV LADC.

Modern Management of Esophageal Cancer: Radio-Oncology in Neoadjuvancy, Adjuvancy and Palliation

The modern management of esophageal cancer is crucially based on a multidisciplinary and multimodal approach. Radiotherapy is involved in neoadjuvant and adjuvant settings; moreover, it includes radical and palliative treatment intention (with a focus on the use of a stent and its potential integration with radiotherapy). In this review, the above-mentioned settings and approaches will be described. Referring to available international guidelines, the background evidence bases will be reviewed, and the ongoing, more relevant trials will be outlined. Target definitions and radiotherapy doses to administer will be mentioned. Peculiar applications such as brachytherapy (interventional radiation oncology), and data regarding innovative approaches including MRI-guided-RT and radiomic analysis will be reported. A focus on the avoidance of surgery for major clinical responses (particularly for SCC) is detailed.

Modern Management of Esophageal Cancer: Radio-Oncology in Neoadjuvancy, Adjuvancy and Palliation

The modern management of esophageal cancer is crucially based on a multidisciplinary and multimodal approach. Radiotherapy is involved in neoadjuvant and adjuvant settings; moreover, it includes radical and palliative treatment intention (with a focus on the use of a stent and its potential integration with radiotherapy). In this review, the above-mentioned settings and approaches will be described. Referring to available international guidelines, the background evidence bases will be reviewed, and the ongoing, more relevant trials will be outlined. Target definitions and radiotherapy doses to administer will be mentioned. Peculiar applications such as brachytherapy (interventional radiation oncology), and data regarding innovative approaches including MRI-guided-RT and radiomic analysis will be reported. A focus on the avoidance of surgery for major clinical responses (particularly for SCC) is detailed.

Modern Management of Esophageal Cancer: Radio-Oncology in Neoadjuvancy, Adjuvancy and Palliation

The modern management of esophageal cancer is crucially based on a multidisciplinary and multimodal approach. Radiotherapy is involved in neoadjuvant and adjuvant settings; moreover, it includes radical and palliative treatment intention (with a focus on the use of a stent and its potential integration with radiotherapy). In this review, the above-mentioned settings and approaches will be described. Referring to available international guidelines, the background evidence bases will be reviewed, and the ongoing, more relevant trials will be outlined. Target definitions and radiotherapy doses to administer will be mentioned. Peculiar applications such as brachytherapy (interventional radiation oncology), and data regarding innovative approaches including MRI-guided-RT and radiomic analysis will be reported. A focus on the avoidance of surgery for major clinical responses (particularly for SCC) is detailed.

Modern Management of Esophageal Cancer: Radio-Oncology in Neoadjuvancy, Adjuvancy and Palliation

The modern management of esophageal cancer is crucially based on a multidisciplinary and multimodal approach. Radiotherapy is involved in neoadjuvant and adjuvant settings; moreover, it includes radical and palliative treatment intention (with a focus on the use of a stent and its potential integration with radiotherapy). In this review, the above-mentioned settings and approaches will be described. Referring to available international guidelines, the background evidence bases will be reviewed, and the ongoing, more relevant trials will be outlined. Target definitions and radiotherapy doses to administer will be mentioned. Peculiar applications such as brachytherapy (interventional radiation oncology), and data regarding innovative approaches including MRI-guided-RT and radiomic analysis will be reported. A focus on the avoidance of surgery for major clinical responses (particularly for SCC) is detailed.

Modern Management of Esophageal Cancer: Radio-Oncology in Neoadjuvancy, Adjuvancy and Palliation

The modern management of esophageal cancer is crucially based on a multidisciplinary and multimodal approach. Radiotherapy is involved in neoadjuvant and adjuvant settings; moreover, it includes radical and palliative treatment intention (with a focus on the use of a stent and its potential integration with radiotherapy). In this review, the above-mentioned settings and approaches will be described. Referring to available international guidelines, the background evidence bases will be reviewed, and the ongoing, more relevant trials will be outlined. Target definitions and radiotherapy doses to administer will be mentioned. Peculiar applications such as brachytherapy (interventional radiation oncology), and data regarding innovative approaches including MRI-guided-RT and radiomic analysis will be reported. A focus on the avoidance of surgery for major clinical responses (particularly for SCC) is detailed.

Modern Management of Esophageal Cancer: Radio-Oncology in Neoadjuvancy, Adjuvancy and Palliation

The modern management of esophageal cancer is crucially based on a multidisciplinary and multimodal approach. Radiotherapy is involved in neoadjuvant and adjuvant settings; moreover, it includes radical and palliative treatment intention (with a focus on the use of a stent and its potential integration with radiotherapy). In this review, the above-mentioned settings and approaches will be described. Referring to available international guidelines, the background evidence bases will be reviewed, and the ongoing, more relevant trials will be outlined. Target definitions and radiotherapy doses to administer will be mentioned. Peculiar applications such as brachytherapy (interventional radiation oncology), and data regarding innovative approaches including MRI-guided-RT and radiomic analysis will be reported. A focus on the avoidance of surgery for major clinical responses (particularly for SCC) is detailed.

Modern Management of Esophageal Cancer: Radio-Oncology in Neoadjuvancy, Adjuvancy and Palliation

The modern management of esophageal cancer is crucially based on a multidisciplinary and multimodal approach. Radiotherapy is involved in neoadjuvant and adjuvant settings; moreover, it includes radical and palliative treatment intention (with a focus on the use of a stent and its potential integration with radiotherapy). In this review, the above-mentioned settings and approaches will be described. Referring to available international guidelines, the background evidence bases will be reviewed, and the ongoing, more relevant trials will be outlined. Target definitions and radiotherapy doses to administer will be mentioned. Peculiar applications such as brachytherapy (interventional radiation oncology), and data regarding innovative approaches including MRI-guided-RT and radiomic analysis will be reported. A focus on the avoidance of surgery for major clinical responses (particularly for SCC) is detailed.

Modern Management of Esophageal Cancer: Radio-Oncology in Neoadjuvancy, Adjuvancy and Palliation

The modern management of esophageal cancer is crucially based on a multidisciplinary and multimodal approach. Radiotherapy is involved in neoadjuvant and adjuvant settings; moreover, it includes radical and palliative treatment intention (with a focus on the use of a stent and its potential integration with radiotherapy). In this review, the above-mentioned settings and approaches will be described. Referring to available international guidelines, the background evidence bases will be reviewed, and the ongoing, more relevant trials will be outlined. Target definitions and radiotherapy doses to administer will be mentioned. Peculiar applications such as brachytherapy (interventional radiation oncology), and data regarding innovative approaches including MRI-guided-RT and radiomic analysis will be reported. A focus on the avoidance of surgery for major clinical responses (particularly for SCC) is detailed.

Modern Management of Esophageal Cancer: Radio-Oncology in Neoadjuvancy, Adjuvancy and Palliation

The modern management of esophageal cancer is crucially based on a multidisciplinary and multimodal approach. Radiotherapy is involved in neoadjuvant and adjuvant settings; moreover, it includes radical and palliative treatment intention (with a focus on the use of a stent and its potential integration with radiotherapy). In this review, the above-mentioned settings and approaches will be described. Referring to available international guidelines, the background evidence bases will be reviewed, and the ongoing, more relevant trials will be outlined. Target definitions and radiotherapy doses to administer will be mentioned. Peculiar applications such as brachytherapy (interventional radiation oncology), and data regarding innovative approaches including MRI-guided-RT and radiomic analysis will be reported. A focus on the avoidance of surgery for major clinical responses (particularly for SCC) is detailed.

Modern Management of Esophageal Cancer: Radio-Oncology in Neoadjuvancy, Adjuvancy and Palliation

The modern management of esophageal cancer is crucially based on a multidisciplinary and multimodal approach. Radiotherapy is involved in neoadjuvant and adjuvant settings; moreover, it includes radical and palliative treatment intention (with a focus on the use of a stent and its potential integration with radiotherapy). In this review, the above-mentioned settings and approaches will be described. Referring to available international guidelines, the background evidence bases will be reviewed, and the ongoing, more relevant trials will be outlined. Target definitions and radiotherapy doses to administer will be mentioned. Peculiar applications such as brachytherapy (interventional radiation oncology), and data regarding innovative approaches including MRI-guided-RT and radiomic analysis will be reported. A focus on the avoidance of surgery for major clinical responses (particularly for SCC) is detailed.

OUP accepted manuscript

The recent, rapid advances in immuno-oncology have revolutionized cancer treatment and spurred further research into tumor biology. Yet, cancer patients respond variably to immunotherapy despite mounting evidence to support its efficacy. Current methods for predicting immunotherapy response are unreliable, as these tests cannot fully account for tumor heterogeneity and microenvironment. An improved method for predicting response to immunotherapy is needed. Recent studies have proposed radiomics—the process of converting medical images into quantitative data (features) that can be processed using machine learning algorithms to identify complex patterns and trends—for predicting response to immunotherapy. Because patients undergo numerous imaging procedures throughout the course of the disease, there exists a wealth of radiological imaging data available for training radiomics models. And because radiomic features reflect cancer biology, such as tumor heterogeneity and microenvironment, these models have enormous potential to predict immunotherapy response more accurately than current methods. Models trained on preexisting biomarkers and/or clinical outcomes have demonstrated potential to improve patient stratification and treatment outcomes. In this review, we discuss current applications of radiomics in oncology, followed by a discussion on recent studies that use radiomics to predict immunotherapy response and toxicity.

Integrating Clinical Data and Attentional CT Imaging Features for Esophageal Fistula Prediction in Esophageal Cancer

Background and Purpose

This study aims to develop a risk model to predict esophageal fistula in esophageal cancer (EC) patients by learning from both clinical data and computerized tomography (CT) radiomic features.

Materials and Methods

In this retrospective study, computerized tomography (CT) images and clinical data of 186 esophageal fistula patients and 372 controls (1:2 matched by the diagnosis time of EC, sex, marriage, and race) were collected. All patients had esophageal cancer and did not receive esophageal surgery. 70% patients were assigned into training set randomly and 30% into validation set. We firstly use a novel attentional convolutional neural network for radiographic descriptor extraction from nine views of planes of contextual CT, segmented tumor and neighboring structures. Then clinical factors including general, diagnostic, pathologic, therapeutic and hematological parameters are fed into neural network for high-level latent representation. The radiographic descriptors and latent clinical factor representations are finally associated by a fully connected layer for patient level risk prediction using SoftMax classifier.

Results

512 deep radiographic features and 32 clinical features were extracted. The integrative deep learning model achieved C-index of 0.901, sensitivity of 0.835, and specificity of 0.918 on validation set with superior performance than non-integrative model using CT imaging alone (C-index = 0.857) or clinical data alone (C-index = 0.780).

Conclusion

The integration of radiomic descriptors from CT and clinical data significantly improved the esophageal fistula prediction. We suggest that this model has the potential to support individualized stratification and treatment planning for EC patients.