Predicting Response to Neoadjuvant Chemoradiotherapy in Esophageal Cancer with Textural Features Derived from Pretreatment 18F-FDG PET/CT Imaging

Development and validation a radiomics combined clinical model predicts treatment response for esophageal squamous cell carcinoma patients

PURPOSE

This study is aimed to develop and validate a machine learning model, which combined radiomics and clinical characteristics to predicting the definitive chemoradiotherapy (dCRT) treatment response in esophageal squamous cell carcinoma (ESCC) patients.

METHODS

204 advanced ESCC patients were included who underwent dCRT at our hospital. Patients were randomly divided into training cohort and validation cohort with a ratio of 7:3. The radiomics features were selected by LASSO algorithm. The clinical features were selected by multivariate logistics analysis (p < 0.05). Subsequently, a combined radiomics and clinical model was established and validated to predict the treatment response in ESCC patients by logistic regression model. The performance of the model was evaluated by receiver operating characteristic (ROC) curve, decision curve analysis (DCA), nomogram, and calibration curve.

RESULTS

Total of 944 radiomics features were extracted from the pre-treatment contrasted enhanced CT images (CECT). After feature selection, 3 radiomics features and 3 clinical features were identified as the most predictive variables. The combined model shows better prediction performance among radiomics model or clinical model. The radiomics model's AUC values in training and validation cohort are 0.71,0.69. As for clinical model the AUC values were 0.74,0.75 in training and validation cohort. However, the AUC values in combined model are 0.79, 0.78 in training cohort and validation cohort, respectively. DCA and calibration curve also demonstrated good performance for the combined model.

CONCLUSION

The radiomics combined clinical features model demonstrates superior treatment response prediction ability for ESCC patients received dCRT. This model has the potential to assist clinicians in identifying non-responsive patients before treatment and guide individualized therapy for advanced ESCC patients.

A 18F-FDG PET/CT-based deep learning-radiomics-clinical model for prediction of cervical lymph node metastasis in esophageal squamous cell carcinoma

BACKGROUND

To develop an artificial intelligence (AI)-based model using Radiomics, deep learning (DL) features extracted from 18F-fluorodeoxyglucose (18F-FDG) Positron emission tomography/Computed Tomography (PET/CT) images of tumor and cervical lymph node with clinical feature for predicting cervical lymph node metastasis (CLNM) in patients with esophageal squamous cell carcinoma (ESCC).

METHODS

The study included 300 ESCC patients from the First Affiliated Hospital of Zhengzhou University who were divided into a training cohort and an internal testing cohort with an 8:2 ratio. Another 111 patients from Shanghai Chest Hospital were included as the external cohort. For each sample, we extracted 428 PET/CT-based Radiomics features from the gross tumor volume (GTV) and cervical lymph node (CLN) delineated layer by layer and 256 PET/CT-based DL features from the maximum cross-section of GTV and CLN images We input these features into seven different machine learning algorithms and ultimately selected logistic regression (LR) as the model classifier. Subsequently, we evaluated seven models (Clinical, Radiomics, Radiomics-Clinical, DL-Clinical, DL-Radiomics, DL-Radiomics-Clinical) using Radiomics features, DL features and clinical feature.

RESULTS

The DL-Radiomics-Clinical (DRC) model demonstrated higher AUC of 0.955 and 0.916 compared to the other six models in both internal and external testing cohorts respectively. The DRC model achieved the highest accuracy among the seven models in both the internal and external test sets, with scores of 0.951 and 0.892, respectively.

CONCLUSIONS

Through the combination of Radiomics features and DL features from PET/CT imaging and clinical feature, we developed a predictive model exhibiting exceptional classification capabilities. This model can be considered as a non-invasive method for predication of CLNM in patients with ESCC. It might facilitate decision-making regarding to the extend of lymph node dissection, and to select candidates for postoperative adjuvant therapy.

Maximum standardised uptake value of positron emission tomography as a predictor of oesophageal cancer outcomes

OBJECTIVES

This study aimed to analyse the value of pre-operative 18F-fluorodeoxyglucose positron emission tomography (PET)-computed tomography that can predict tumour pathological complete response, tumour histology grade, overall survival, and recurrence-free survival in patients with locally advanced oesophageal squamous cell carcinoma who underwent neoadjuvant chemoradiotherapy (NCRT) followed by surgery.

METHODS

We retrospectively reviewed the cases of patients with locally advanced oesophageal squamous cell carcinoma undergoing NCRT followed by surgery. Patients who did not undergo PET within 3 months of surgery were excluded. We set a pre-operative PET maximum standardised uptake value (SUVmax) of > 5 as the threshold and classified the patients into two groups. We analysed the tumour response and histology grade, and compared the overall survival and recurrence-free survival between the two groups.

RESULTS

This cohort included 92 patients with oesophageal squamous cell carcinoma who underwent NCRT followed by surgery; 49 patients had a pre-operative PET SUVmax < 5, and 43 patients had a pre-operative PET SUVmax > 5. The patients' pre-operative PET SUVmax correlated with tumour histology, ypT stage, and tumour response. Patients with a pre-operative SUVmax < 5 had better 2-year-overall survival (78% vs. 62%, P < 0.05) and 2-year recurrence-free survival (62% vs. 34%, P < 0.05) than those with a pre-operative SUV > 5.

CONCLUSIONS

Pre-operative SUVmax may be useful to predict tumour response, survival, and recurrence in patients with locally advanced oesophageal squamous cell carcinoma who undergo NCRT followed by surgery.

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.

Prognostic value of 18F-FDG PET radiomics and sarcopenia in patients with oral squamous cell carcinoma

BACKGROUND

Oral cancer is one of the most common malignancies in the head and neck region. Approximately 90% of oral cancers are oral squamous cell carcinomas (OSCC). 18F-FDG PET/CT has been used in OSCC patients for its high value in detecting metastatic lymph nodes and distant metastases. PET radiomics and sarcopenia can be measured on the PET and CT components of 18F-FDG PET/CT.

PURPOSE

This study aimed to investigate the prognostic value of radiomics and sarcopenia measured on the PET and CT components of pre-operation 18F-FDG PET/CT in OSCC.

METHODS

A total of 116 patients eventually enrolled in our study were randomly divided into two cohorts: training cohort (n = 58) and validation cohort (n = 58). The Cox model combined with the least absolute shrinkage and selection operator (LASSO) algorithm was applied to construct the radiomics score (Rad_score). The third lumber skeletal muscle index (L3 SMI) was calculated to identify sarcopenia. Univariate and multivariate Cox regression analyses were performed to identify the independent prognostic factors. Based on the clinical factors, the clinical model was constructed, and the combined model was developed through the combination of the clinical model and Rad_score. C index, time-dependent C-index curves, receiver operating characteristic (ROC) curve, calibration curves, and decision curve analysis were used to evaluate the performance of prediction models.

RESULTS

Three radiomics features constitute the Rad_score for overall survival (OS) and progression-free survival (PFS), respectively. Multivariate Cox regression analysis revealed that Rad_score was an independent prognostic factor, whereas sarcopenia was not. The combined models showed satisfactory performance in both the training cohort (C-index: OS:0.836, PFS:0.776) and the validation cohort (C-index: OS:0.744, PFS:0.712). The combined models were visualized as nomograms. Nomogram scores can realize the risk stratification of OSCC patients. Lower nomogram score is significantly related to the poorer OS (training cohort: p < 0.0001, validation cohort: p < 0.0001, overall cohort: p < 0.0001) and PFS (training cohort: p < 0.0001, validation cohort: p = 0.00017, overall cohort: p < 0.0001).

CONCLUSIONS

Rad_score, but not sarcopenia, was an independent prognostic factor for patients with OSCC. The nomograms had a satisfactory performance, which might be helpful for OSCC patients and clinicians in personalized prognostic prediction and treatment decision-making.

Multi-omics approaches for biomarker discovery in predicting the response of esophageal cancer to neoadjuvant therapy: A multidimensional perspective

Neoadjuvant chemoradiotherapy (NCRT) followed by surgery has been established as the standard treatment strategy for operable locally advanced esophageal cancer (EC). However, achieving pathologic complete response (pCR) or near pCR to NCRT is significantly associated with a considerable improvement in survival outcomes, while pCR patients may help organ preservation for patients by active surveillance to avoid planned surgery. Thus, there is an urgent need for improved biomarkers to predict EC chemoradiation response in research and clinical settings. Advances in multiple high-throughput technologies such as next-generation sequencing have facilitated the discovery of novel predictive biomarkers, specifically based on multi-omics data, including genomic/transcriptomic sequencings and proteomic/metabolomic mass spectra. The application of multi-omics data has shown the benefits in improving the understanding of underlying mechanisms of NCRT sensitivity/resistance in EC. Particularly, the prominent development of artificial intelligence (AI) has introduced a new direction in cancer research. The integration of multi-omics data has significantly advanced our knowledge of the disease and enabled the identification of valuable biomarkers for predicting treatment response from diverse dimension levels, especially with rapid advances in biotechnological and AI methodologies. Herein, we summarize the current status of research on the use of multi-omics technologies in predicting NCRT response for EC patients. Current limitations, challenges, and future perspectives of these multi-omics platforms will be addressed to assist in experimental designs and clinical use for further integrated analysis.

A Radiotherapy Dose Map-Guided Deep Learning Method for Predicting Pathological Complete Response in Esophageal Cancer Patients after Neoadjuvant Chemoradiotherapy Followed by Surgery

Esophageal cancer is a deadly disease, and neoadjuvant chemoradiotherapy can improve patient survival, particularly for patients achieving a pathological complete response (ypCR). However, existing imaging methods struggle to accurately predict ypCR. This study explores computer-aided detection methods, considering both imaging data and radiotherapy dose variations to enhance prediction accuracy. It involved patients with node-positive esophageal squamous cell carcinoma undergoing neoadjuvant chemoradiotherapy and surgery, with data collected from 2014 to 2017, randomly split into five subsets for 5-fold cross-validation. The algorithm DCRNet, an advanced version of OCRNet, integrates RT dose distribution into dose contextual representations (DCR), combining dose and pixel representation with ten soft regions. Among the 80 enrolled patients (mean age 55.68 years, primarily male, with stage III disease and middle-part lesions), the ypCR rate was 28.75%, showing no significant demographic or disease differences between the ypCR and non-ypCR groups. Among the three summarization methods, the maximum value across the CTV method produced the best results with an AUC of 0.928. The HRNetV2p model with DCR performed the best among the four backbone models tested, with an AUC of 0.928 (95% CI, 0.884-0.972) based on 5-fold cross-validation, showing significant improvement compared to other models. This underscores DCR-equipped models' superior AUC outcomes. The study highlights the potential of dose-guided deep learning in ypCR prediction, necessitating larger, multicenter studies to validate the results.

Pretreatment radiomic biomarker for immunotherapy responder prediction in stage IB-IV NSCLC (LCDigital-IO Study): a multicenter retrospective study

BACKGROUND

The predictive efficacy of current biomarker of immune checkpoint inhibitors (ICIs) is not sufficient. This study investigated the causality between radiomic biomarkers and immunotherapy response status in patients with stage IB-IV non-small cell lung cancer (NSCLC), including its biological context for ICIs treatment response prediction.

METHODS

CT images from 319 patients with pretreatment NSCLC receiving immunotherapy between January 2015 and November 2021 were retrospectively collected and composed a discovery (n=214), independent validation (n=54), and external test cohort (n=51). A set of 851 features was extracted from tumorous and peritumoral volumes of interest (VOIs). The reference standard is the durable clinical benefit (DCB, sustained disease control for more than 6 months assessed via radiological evaluation). The predictive value of combined radiomic signature (CRS) for pathological response was subsequently assessed in another cohort of 98 patients with resectable NSCLC receiving ICIs preoperatively. The association between radiomic features and tumor immune landscape on the online data set (n=60) was also examined. A model combining clinical predictor and radiomic signatures was constructed to improve performance further.

RESULTS

CRS discriminated DCB and non-DCB patients well in the training and validation cohorts with an area under the curve (AUC) of 0.82, 95% CI: 0.75 to 0.88, and 0.75, 95% CI: 0.64 to 0.87, respectively. In this study, the predictive value of CRS was better than programmed cell death ligand-1 (PD-L1) expression (AUC of PD-L1 subset: 0.59, 95% CI: 0.50 to 0.69) or clinical model (AUC: 0.66, 95% CI: 0.51 to 0.81). After combining the clinical signature with CRS, the predictive performance improved further with an AUC of 0.837, 0.790 and 0.781 in training, validation and D2 cohorts, respectively. When predicting pathological response, CRS divided patients into a major pathological response (MPR) and non-MPR group (AUC: 0.76, 95% CI: 0.67 to 0.81). Moreover, CRS showed a promising stratification ability on overall survival (HR: 0.49, 95% CI: 0.27 to 0.89; p=0.020) and progression-free survival (HR: 0.43, 95% CI: 0.26 to 0.74; p=0.002).

CONCLUSION

By analyzing both tumorous and peritumoral regions of CT images in a radiomic strategy, we developed a non-invasive biomarker for distinguishing responders of ICIs therapy and stratifying their survival outcome efficiently, which may support the clinical decisions on the use of ICIs in advanced as well as patients with resectable NSCLC.

The gap before real clinical application of imaging-based machine-learning and radiomic models for chemoradiation outcome prediction in esophageal cancer: a systematic review and meta-analysis

BACKGROUND

Due to tumoral heterogeneity and the lack of robust biomarkers, the prediction of chemoradiotherapy response and prognosis in patients with esophageal cancer (EC) is challenging. The goal of this study was to assess the study quality and clinical value of machine learning and radiomic-based quantitative imaging studies for predicting the outcomes of EC patients after chemoradiotherapy.

MATERIALS AND METHODS

PubMed, Embase, and Cochrane were searched for eligible articles. The methodological quality and risk of bias were evaluated using the Radiomics Quality Score (RQS), Image Biomarkers Standardization Initiative (IBSI) Guideline, and Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) statement, as well as the modified Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. A meta-analysis of the evidence focusing on predicting chemoradiotherapy response and outcome in EC patients was implemented.

RESULTS

Forty-six studies were eligible for qualitative synthesis. The mean RQS score was 9.07, with an adherence rate of 42.52%. The adherence rates of the TRIPOD and IBSI were 61.70 and 43.17%, respectively. Ultimately, 24 studies were included in the meta-analysis, of which 16 studies had a pooled sensitivity, specificity, and area under the curve (AUC) of 0.83 (0.76-0.89), 0.83 (0.79-0.86), and 0.84 (0.81-0.87) in neoadjuvant chemoradiotherapy datasets, as well as 0.84 (0.75-0.93), 0.89 (0.83-0.93), and 0.93 (0.90-0.95) in definitive chemoradiotherapy datasets, respectively. Moreover, radiomics could distinguish patients from the low-risk and high-risk groups with different disease-free survival (DFS) (pooled hazard ratio: 3.43, 95% CI 2.39-4.92) and overall survival (pooled hazard ratio: 2.49, 95% CI 1.91-3.25). The results of subgroup and regression analyses showed that some of the heterogeneity was explained by the combination with clinical factors, sample size, and usage of the deep learning (DL) signature.

CONCLUSIONS

Noninvasive radiomics offers promising potential for optimizing treatment decision-making in EC patients. However, it is necessary to make scientific advancements in EC radiomics regarding reproducibility, clinical usefulness analysis, and open science categories. Improved model reporting of study objectives, blind assessment, and image processing steps are required to help promote real clinical applications of radiomics in EC research.

Performance of radiomics-based artificial intelligence systems in the diagnosis and prediction of treatment response and survival in esophageal cancer: a systematic review and meta-analysis of diagnostic accuracy

Radiomics can interpret radiological images with more detail and in less time compared to the human eye. Some challenges in managing esophageal cancer can be addressed by incorporating radiomics into image interpretation, treatment planning, and predicting response and survival. This systematic review and meta-analysis provides a summary of the evidence of radiomics in esophageal cancer. The systematic review was carried out using Pubmed, MEDLINE, and Ovid EMBASE databases-articles describing radiomics in esophageal cancer were included. A meta-analysis was also performed; 50 studies were included. For the assessment of treatment response using 18F-FDG PET/computed tomography (CT) scans, seven studies (443 patients) were included in the meta-analysis. The pooled sensitivity and specificity were 86.5% (81.1-90.6) and 87.1% (78.0-92.8). For the assessment of treatment response using CT scans, five studies (625 patients) were included in the meta-analysis, with a pooled sensitivity and specificity of 86.7% (81.4-90.7) and 76.1% (69.9-81.4). The remaining 37 studies formed the qualitative review, discussing radiomics in diagnosis, radiotherapy planning, and survival prediction. This review explores the wide-ranging possibilities of radiomics in esophageal cancer management. The sensitivities of 18F-FDG PET/CT scans and CT scans are comparable, but 18F-FDG PET/CT scans have improved specificity for AI-based prediction of treatment response. Models integrating clinical and radiomic features facilitate diagnosis and survival prediction. More research is required into comparing models and conducting large-scale studies to build a robust evidence base.

Delta-radiomics based on CT predicts pathologic complete response in ESCC treated with neoadjuvant immunochemotherapy and surgery

Background and purpose

Unnecessary surgery can be avoided, and more appropriate treatment plans can be developed for patients if the efficacy of neoadjuvant immunochemotherapy for esophageal cancer (EC) can be predicted before surgery. The purpose of this study was to evaluate the ability of machine learning models based on delta features of immunochemotherapy CT images to predict the efficacy of neoadjuvant immunochemotherapy in patients with esophageal squamous cell carcinoma (ESCC) compared with machine learning models based solely on postimmunochemotherapy CT images.

Materials and methods

A total of 95 patients were enrolled in our study and randomly divided into a training group (n = 66) and test group (n = 29). We extracted preimmunochemotherapy radiomics features from preimmunochemotherapy enhanced CT images in the preimmunochemotherapy group (pregroup) and postimmunochemotherapy radiomics features from postimmunochemotherapy enhanced CT images in the postimmunochemotherapy group (postgroup). We then subtracted the preimmunochemotherapy features from the postimmunochemotherapy features and obtained a series of new radiomics features that were included in the delta group. The reduction and screening of radiomics features were carried out by using the Mann-Whitney U test and LASSO regression. Five pairwise machine learning models were established, the performance of which was evaluated by receiver operating characteristic (ROC) curve and decision curve analyses.

Results

The radiomics signature of the postgroup was composed of 6 radiomics features; that of the delta-group was composed of 8 radiomics features. The area under the ROC curve (AUC) of the machine learning model with the best efficacy was 0.824 (0.706-0.917) in the postgroup and 0.848 (0.765-0.917) in the delta group. The decision curve showed that our machine learning models had good predictive performance. The delta group performed better than the postgroup for each corresponding machine learning model.

Conclusion

We established machine learning models that have good predictive efficacy and can provide certain reference values for clinical treatment decision-making. Our machine learning models based on delta imaging features performed better than those based on single time-stage postimmunochemotherapy imaging features.

Prognostic analysis of curatively resected pancreatic cancer using harmonized positron emission tomography radiomic features

BACKGROUND

Texture features reflecting tumour heterogeneity enable us to investigate prognostic factors. The R package ComBat can harmonize the quantitative texture features among several positron emission tomography (PET) scanners. We aimed to identify prognostic factors among harmonized PET radiomic features and clinical information from pancreatic cancer patients who underwent curative surgery.

METHODS

Fifty-eight patients underwent preoperative enhanced dynamic computed tomography (CT) scanning and fluorodeoxyglucose PET/CT using four PET scanners. Using LIFEx software, we measured PET radiomic parameters including texture features with higher order and harmonized these PET parameters. For progression-free survival (PFS) and overall survival (OS), we evaluated clinical information, including age, TNM stage, and neural invasion, and the harmonized PET radiomic features based on univariate Cox proportional hazard regression. Next, we analysed the prognostic indices by multivariate Cox proportional hazard regression (1) by using either significant (p < 0.05) or borderline significant (p = 0.05-0.10) indices in the univariate analysis (first multivariate analysis) or (2) by using the selected features with random forest algorithms (second multivariate analysis). Finally, we checked these multivariate results by log-rank test.

RESULTS

Regarding the first multivariate analysis for PFS after univariate analysis, age was the significant prognostic factor (p = 0.020), and MTV and GLCM contrast were borderline significant (p = 0.051 and 0.075, respectively). Regarding the first multivariate analysis of OS, neural invasion, Shape sphericity and GLZLM LZLGE were significant (p = 0.019, 0.042 and 0.0076). In the second multivariate analysis, only MTV was significant (p = 0.046) for PFS, whereas GLZLM LZLGE was significant (p = 0.047), and Shape sphericity was borderline significant (p = 0.088) for OS. In the log-rank test, age, MTV and GLCM contrast were borderline significant for PFS (p = 0.08, 0.06 and 0.07, respectively), whereas neural invasion and Shape sphericity were significant (p = 0.03 and 0.04, respectively), and GLZLM LZLGE was borderline significant for OS (p = 0.08).

CONCLUSIONS

Other than the clinical factors, MTV and GLCM contrast for PFS and Shape sphericity and GLZLM LZLGE for OS may be prognostic PET parameters. A prospective multicentre study with a larger sample size may be warranted.

The Oesophageal Cancer Multidisciplinary Team: Can Machine Learning Assist Decision-Making?

BACKGROUND

The complexity of the upper gastrointestinal (UGI) multidisciplinary team (MDT) is continually growing, leading to rising clinician workload, time pressures, and demands. This increases heterogeneity or 'noise' within decision-making for patients with oesophageal cancer (OC) and may lead to inconsistent treatment decisions. In recent decades, the application of artificial intelligence (AI) and more specifically the branch of machine learning (ML) has led to a paradigm shift in the perceived utility of statistical modelling within healthcare. Within oesophageal cancer (OC) care, ML techniques have already been applied with early success to the analyses of histological samples and radiology imaging; however, it has not yet been applied to the MDT itself where such models are likely to benefit from incorporating information-rich, diverse datasets to increase predictive model accuracy.

METHODS

This review discusses the current role the MDT plays in modern UGI cancer care as well as the utilisation of ML techniques to date using histological and radiological data to predict treatment response, prognostication, nodal disease evaluation, and even resectability within OC.

RESULTS

The review finds that an emerging body of evidence is growing in support of ML tools within multiple domains relevant to decision-making within OC including automated histological analysis and radiomics. However, to date, no specific application has been directed to the MDT itself which routinely assimilates this information.

CONCLUSIONS

The authors feel the UGI MDT offers an information-rich, diverse array of data from which ML offers the potential to standardise, automate, and produce more consistent, data-driven MDT decisions.

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.

Change in Density Not Size of Esophageal Adenocarcinoma During Neoadjuvant Chemotherapy Is Associated with Improved Survival Outcomes

BACKGROUND

Changes in the size and density of esophageal malignancy during neoadjuvant chemotherapy (NCT) may be useful in predicting overall survival (OS). The aim of this study was to explore this relationship in patients with adenocarcinoma.

METHODS

A retrospective single-centre cohort study was performed. Consecutive patients with esophageal adenocarcinoma who received NCT followed by en bloc resection with curative intent were identified. Pre- and post-NCT computed tomography scans were reviewed. The percentage difference between the greatest tumor diameter, esophageal wall thickness and tumor density was calculated. Multivariate Cox regression analysis identified variables independently associated with OS. A ROC analysis was performed on radiological markers to identify optimal cut-off points with Kaplan-Meier plots subsequently created.

RESULTS

Of the 167 identified, 88 (51.5%) had disease of the gastro-esophageal junction and 149 (89.2%) were clinical T3. In total, 122 (73.1%) had node-positive disease. Increased tumor density (HR 1.01 per % change, 95% CI 1.00-1.02, p = 0.007), lymphovascular invasion (HR 3.23, 95% CI 1.34-7.52, p = 0.006) and perineural invasion (HR 2.51, 95% CI 1.03-6.08, p = 0.048) were independently associated with a decrease in OS. Patients who had a decrease in their tumor density during the time they received NCT of ≥ 20% in Hounsfield units had significantly longer OS than those who did not (75.5 months versus 34.4 months, 95% CI 38.83-105.13/18.63-35.07, p = 0.025).

CONCLUSIONS

Interval changes in the density, not size, of esophageal adenocarcinoma during the time that NCT are independently associated with OS.

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.

Could 18-FDG PET-CT Radiomic Features Predict the Locoregional Progression-Free Survival in Inoperable or Unresectable Oesophageal Cancer?

Background: We evaluated the value of pre-treatment positron-emission tomography−computed tomography (PET-CT)-based radiomic features in predicting the locoregional progression-free survival (LR-PFS) of patients with inoperable or unresectable oesophageal cancer. Material and Methods: Forty-six patients were included and 230 radiomic parameters were extracted. After a principal component analysis (PCA), we identified the more robust radiomic parameters, and we used them to develop a heatmap. Finally, we correlated these radiomic features with LR-PFS. Results: The median follow-up time was 17 months. The two-year LR-PFS and PFS rates were 35.9% (95% CI: 18.9−53.3) and 21.6% (95%CI: 10.0−36.2), respectively. After the correlation analysis, we identified 55 radiomic parameters that were included in the heatmap. According to the results of the hierarchical clustering, we identified two groups of patients presenting statistically different median LR-PFSs (22.8 months vs. 9.9 months; HR = 2.64; 95% CI 0.97−7.15; p = 0.0573). We also identified two radiomic features (“F_rlm_rl_entr_per” and “F_rlm_2_5D_rl_entr”) significantly associated with LR-PFS. Patients expressing a “F_rlm_2_5D_rl_entr” of <3.3 had a better median LR- PFS (29.4 months vs. 8.2 months; p = 0.0343). Patients presenting a “F_rlm_rl_entr_per” of <4.7 had a better median LR-PFS (50.4 months vs. 9.9 months; p = 0.0132). Conclusion: We identified two radiomic signatures associated with a lower risk of locoregional relapse after CRT.

Combined 18 F-FDG PET/CT Radiomics and Sarcopenia Score in Predicting Relapse-Free Survival and Overall Survival in Patients With Esophagogastric Cancer

PURPOSE

The aim of this study was to determine if radiomic features combined with sarcopenia measurements on pretreatment 18 F-FDG PET/CT can improve outcome prediction in surgically treated adenocarcinoma esophagogastric cancer patients.

PATIENTS AND METHODS

One hundred forty-five esophageal adenocarcinoma patients with curative therapeutic intent and available pretreatment 18 F-FDG PET/CT were included. Textural features from PET and CT images were evaluated using LIFEx software ( lifexsoft.org ). Sarcopenia measurements were done by measuring the Skeletal Muscle Index at L3 level on the CT component. Univariable and multivariable analyses were conducted to create a model including the radiomic parameters, clinical features, and Skeletal Muscle Index score to predict patients' outcome.

RESULTS

In multivariable analysis, we combined clinicopathological parameters including ECOG, surgical T, and N staging along with imaging derived sarcopenia measurements and radiomic features to build a predictor model for relapse-free survival and overall survival. Overall, adding sarcopenic status to the model with clinical features only (likelihood ratio test P = 0.03) and CT feature ( P = 0.0037) improved the model fit for overall survival. Similarly, adding sarcopenic status ( P = 0.051), CT feature ( P = 0.042), and PET feature ( P = 0.011) improved the model fit for relapse-free survival.

CONCLUSIONS

PET and CT radiomics derived from combined PET/CT integrated with clinicopathological parameters and sarcopenia measurement might improve outcome prediction in patients with nonmetastatic esophagogastric adenocarcinoma.

The Key Clinical Questions of Neoadjuvant Chemoradiotherapy for Resectable Esophageal Cancer—A Review

Over 50% of individuals with esophageal cancer (EC) present with advanced stages of the disease; therefore, their outcome following surgery alone is poor, with only 25%–36% being alive 5 years post-surgery. Based on the evidence that the CROSS and NEOCRTEC5010 trials provided, neoadjuvant chemoradiotherapy (nCRT) is now the standard therapy for patients with locally advanced EC. However, there are still many concerning clinical questions that remain controversial such as radiation dose, appropriate patient selection, the design of the radiation field, the time interval between chemoradiotherapy (CRT) and surgery, and esophageal retention. With immune checkpoint inhibitors (ICIs) rapidly becoming a mainstay of cancer therapy, along with radiation, chemotherapy, and surgery, the combination mode of immunotherapy is also becoming a hot topic of discussion. Here, we try to provide constructive suggestions to answer the perplexing problems and clinical concerns for the progress of nCRT for EC in the future.

A prediction model for pathological findings after neoadjuvant chemoradiotherapy for resectable locally advanced esophageal squamous cell carcinoma based on endoscopic images using deep learning

OBJECTIVES

To propose deep-learning (DL)-based predictive model for pathological complete response rate for resectable locally advanced esophageal squamous cell carcinoma (SCC) after neoadjuvant chemoradiotherapy (NCRT) with endoscopic images.

METHODS AND MATERIAL

This retrospective study analyzed 98 patients with locally advanced esophagus cancer treated by preoperative chemoradiotherapy followed by surgery from 2004 to 2016. The patient data were split into two sets: 72 patients for the training of models and 26 patients for testing of the model. Patients was classified into two groups with the LC (Group I: responder and Group II: non-responder). The scanned images were converted into joint photographic experts group (JPEG) format and resized to 150 × 150 pixels. The input image without imaging filter (w/o filter) and with Laplacian, Sobel, and wavelet imaging filters deep-learning model to predict the pathological CR with a convolution neural network (CNN). The accuracy, sensitivity, and specificity, the area under the curve (AUC) of the receiver operating characteristic were evaluated.

RESULTS

The average of accuracy for the cross-validation was 0.64 for w/o filter, 0.69 for Laplacian filter, 0.71 for Sobel filter, and 0.81 for wavelet filter, respectively. The average of sensitivity for the cross-validation was 0.80 for w/o filter, 0.81 for Laplacian filter, 0.67 for Sobel filter, and 0.80 for wavelet filter, respectively. The average of specificity for the cross-validation was 0.37 for w/o filter, 0.55 for Laplacian filter, 0.68 for Sobel filter, and 0.81 for wavelet filter, respectively. From the ROC curve, the average AUC for the cross-validation was 0.58 for w/o filter, 0.67 for Laplacian filter, 0.73 for Sobel filter, and 0.83 for wavelet filter, respectively.

CONCLUSIONS

The current study proposed the improvement the accuracy of the DL-based prediction model with the imaging filters. With the imaging filters, the accuracy was significantly improved. The model can be supported to assist clinical oncologists to have a more accurate expectations of the treatment outcome.

ADVANCES IN KNOWLEDGE

The accuracy of the prediction for the local control after radiotherapy can improve with the input image with the imaging filter for deep learning.

Radiomics in Oncological PET Imaging: A Systematic Review-Part 2, Infradiaphragmatic Cancers, Blood Malignancies, Melanoma and Musculoskeletal Cancers

The objective of this review was to summarize published radiomics studies dealing with infradiaphragmatic cancers, blood malignancies, melanoma, and musculoskeletal cancers, and assess their quality. PubMed database was searched from January 1990 to February 2022 for articles performing radiomics on PET imaging of at least 1 specified tumor type. Exclusion criteria includd: non-oncological studies; supradiaphragmatic tumors; reviews, comments, cases reports; phantom or animal studies; technical articles without a clinically oriented question; studies including <30 patients in the training cohort. The review database contained PMID, first author, year of publication, cancer type, number of patients, study design, independent validation cohort and objective. This database was completed twice by the same person; discrepant results were resolved by a third reading of the articles. A total of 162 studies met inclusion criteria; 61 (37.7%) studies included >100 patients, 13 (8.0%) were prospective and 61 (37.7%) used an independent validation set. The most represented cancers were esophagus, lymphoma, and cervical cancer (n = 24, n = 24 and n = 19 articles, respectively). Most studies focused on 18F-FDG, and prognostic and response to treatment objectives. Although radiomics and artificial intelligence are technically challenging, new contributions and guidelines help improving research quality over the years and pave the way toward personalized medicine.

Prediction of Non-Response to Neoadjuvant Chemoradiotherapy in Esophageal Cancer Patients with 18F-FDG PET Radiomics Based Machine Learning Classification

Background: Approximately 26% of esophageal cancer (EC) patients do not respond to neoadjuvant chemoradiotherapy (nCRT), emphasizing the need for pre-treatment selection. The aim of this study was to predict non-response using a radiomic model on baseline ¹⁸F-FDG PET. Methods: Retrospectively, 143 ¹⁸F-FDG PET radiomic features were extracted from 199 EC patients (T1N1-3M0/T2–4aN0-3M0) treated between 2009 and 2019. Non-response (n = 57; 29%) was defined as Mandard Tumor Regression Grade 4–5 (n = 44; 22%) or interval progression (n = 13; 7%). Randomly, 139 patients (70%) were allocated to explore all combinations of 24 feature selection strategies and 6 classification methods towards the cross-validated average precision (AP). The predictive value of the best-performing model, i.e AP and area under the ROC curve analysis (AUC), was evaluated on an independent test subset of 60 patients (30%). Results: The best performing model had an AP (mean ± SD) of 0.47 ± 0.06 on the training subset, achieved by a support vector machine classifier trained on five principal components of relevant clinical and radiomic features. The model was externally validated with an AP of 0.66 and an AUC of 0.67. Conclusion: In the present study, the best-performing model on pre-treatment ¹⁸F-FDG PET radiomics and clinical features had a small clinical benefit to identify non-responders to nCRT in EC.

18F-FDG PET Radiomics as Predictor of Treatment Response in Oesophageal Cancer: A Systematic Review and Meta-Analysis

The best treatment strategy for oesophageal cancer patients achieving a complete clinical response after neoadjuvant chemoradiation is a burning topic. The available diagnostic tools, such as 18F-FDG PET/CT performed routinely, cannot accurately evaluate the presence or absence of the residual tumour. The emerging field of radiomics may encounter the critical challenge of personalised treatment. Radiomics is based on medical image analysis, executed by extracting information from many image features; it has been shown to provide valuable information for predicting treatment responses in oesophageal cancer. This systematic review with a meta-analysis aims to provide current evidence of 18F-FDG PET-based radiomics in predicting response treatments following neoadjuvant chemoradiotherapy in oesophageal cancer. A comprehensive literature review identified 1160 studies, of which five were finally included in the study. Our findings provided that pooled Area Under the Curve (AUC) of the five selected studies was relatively high at 0.821 (95% CI: 0.737–0.904) and not influenced by the sample size of the studies. Radiomics models exhibited a good performance in predicting pathological complete responses (pCRs). This review further strengthens the great potential of 18F-FDG PET-based radiomics to predict pCRs in oesophageal cancer patients who underwent neoadjuvant chemoradiotherapy. Additionally, our review imparts additional support to prospective studies on 18F-FDG PET radiomics for a tailored treatment strategy of oesophageal cancer patients.

Methodological quality of machine learning-based quantitative imaging analysis studies in esophageal cancer: a systematic review of clinical outcome prediction after concurrent chemoradiotherapy

PURPOSE

Studies based on machine learning-based quantitative imaging techniques have gained much interest in cancer research. The aim of this review is to critically appraise the existing machine learning-based quantitative imaging analysis studies predicting outcomes of esophageal cancer after concurrent chemoradiotherapy in accordance with PRISMA guidelines.

METHODS

A systematic review was conducted in accordance with PRISMA guidelines. The citation search was performed via PubMed and Embase Ovid databases for literature published before April 2021. From each full-text article, study characteristics and model information were summarized. We proposed an appraisal matrix with 13 items to assess the methodological quality of each study based on recommended best-practices pertaining to quality.

RESULTS

Out of 244 identified records, 37 studies met the inclusion criteria. Study endpoints included prognosis, treatment response, and toxicity after concurrent chemoradiotherapy with reported discrimination metrics in validation datasets between 0.6 and 0.9, with wide variation in quality. A total of 30 studies published within the last 5 years were evaluated for methodological quality and we found 11 studies with at least 6 "good" item ratings.

CONCLUSION

A substantial number of studies lacked prospective registration, external validation, model calibration, and support for use in clinic. To further improve the predictive power of machine learning-based models and translate into real clinical applications in cancer research, appropriate methodologies, prospective registration, and multi-institution validation are recommended.

Radiomics in the Setting of Neoadjuvant Radiotherapy: A New Approach for Tailored Treatment

INTRODUCTION

Neoadjuvant radiotherapy is currently used mainly in locally advanced rectal cancer and sarcoma and in a subset of non-small cell lung cancer and esophageal cancer, whereas in other diseases it is under investigation. The evaluation of the efficacy of the induction strategy is made possible by performing imaging investigations before and after the neoadjuvant therapy and is usually challenging. In the last decade, texture analysis (TA) has been developed to help the radiologist to quantify and identify the parameters related to tumor heterogeneity, which cannot be appreciated by the naked eye. The aim of this narrative is to review the impact of TA on the prediction of response to neoadjuvant radiotherapy and or chemoradiotherapy.

MATERIALS AND METHODS

Key references were derived from a PubMed query. Hand searching and ClinicalTrials.gov were also used.

RESULTS

This paper contains a narrative report and a critical discussion of radiomics approaches in different fields of neoadjuvant radiotherapy, including esophageal cancer, lung cancer, sarcoma, and rectal cancer.

CONCLUSIONS

Radiomics can shed a light on the setting of neoadjuvant therapies that can be used to tailor subsequent approaches or even to avoid surgery in the future. At the same, these results need to be validated in prospective and multicenter trials.

A Meta-Analysis for Using Radiomics to Predict Complete Pathological Response in Esophageal Cancer Patients Receiving Neoadjuvant Chemoradiation

BACKGROUND

Preservation of organ function is important in cancer treatment. The 'watch-and-wait' strategy is an important approach in management of esophageal cancer. However, clinical imaging cannot accurately evaluate the presence or absence of residual tumor after neoadjuvant chemoradiation. As a result, using radiomics to predict complete pathological response in esophageal cancer has gained in popularity in recent years. Given that the characteristics of patients and sites vary considerably, a meta-analysis is needed to investigate the predictive power of radiomics in esophageal cancer.

PATIENTS AND METHODS

PRISMA guidelines were used to conduct this study. PubMed, Cochrane, and Embase were searched for literature review. The quality of the selected studies was evaluated by the radiomics quality score. I² score and Cochran's Q test were used to evaluate heterogeneity between studies. A funnel plot was used for evaluation of publication bias.

RESULTS

A total of seven articles were collected for this meta-analysis. The pooled area under the receiver operating characteristics curve of the seven selected articles for predicting pathological complete response in eosphageal cancer patient was quite high, achieving a pooled value of 0.813 (95% confidence intervaI=0.761-0.866). The radiomics quality score ranged from -2 to 16 (maximum score: 36 points). Three out of the seven studies used machine learning algorithms, while the others used traditional biostatistics methods. One of the seven studies used morphology class features, while four studies used first-order features, and five used second-order features.

CONCLUSION

Using radiomics to predict complete pathological response after neoadjuvant chemoradiotherapy in esophageal cancer is feasible. In the future, prospective, multicenter studies should be carried out for predicting pathological complete response in patients with esophageal cancer.

Predicting the Local Response of Esophageal Squamous Cell Carcinoma to Neoadjuvant Chemoradiotherapy by Radiomics with a Machine Learning Method Using 18F-FDG PET Images

Background: This study aimed to propose a machine learning model to predict the local response of resectable locally advanced esophageal squamous cell carcinoma (LA-ESCC) treated by neoadjuvant chemoradiotherapy (NCRT) using pretreatment 18-fluorodeoxyglucose positron emission tomography (FDG PET) images. Methods: The local responses of 98 patients were categorized into two groups (complete response and noncomplete response). We performed a radiomics analysis using five segmentations created on FDG PET images, resulting in 4250 features per patient. To construct a machine learning model, we used the least absolute shrinkage and selection operator (LASSO) regression to extract radiomics features optimal for the prediction. Then, a prediction model was constructed by using a neural network classifier. The training model was evaluated with 5-fold cross-validation. Results: By the LASSO analysis of the training data, 22 radiomics features were extracted. In the testing data, the average accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve score of the five prediction models were 89.6%, 92.7%, 89.5%, and 0.95, respectively. Conclusions: The proposed machine learning model using radiomics showed promising predictive accuracy of the local response of LA-ESCC treated by NCRT.

Advanced analytics and artificial intelligence in gastrointestinal cancer: a systematic review of radiomics predicting response to treatment

PURPOSE

Advanced medical image analytics is increasingly used to predict clinical outcome in patients diagnosed with gastrointestinal tumors. This review provides an overview on the value of radiomics in predicting response to treatment in patients with gastrointestinal tumors.

METHODS

A systematic review was conducted, according to PRISMA guidelines. The protocol was prospectively registered (PROSPERO: CRD42019128408). PubMed, Embase, and Cochrane databases were searched. Original studies reporting on the value of radiomics in predicting response to treatment in patients with a gastrointestinal tumor were included. A narrative synthesis of results was conducted. Results were stratified by tumor type. Quality assessment of included studies was performed, according to the radiomics quality score.

RESULTS

The comprehensive literature search identified 1360 unique studies, of which 60 articles were included for analysis. In 37 studies, radiomics models and individual radiomic features showed good predictive performance for response to treatment (area under the curve or accuracy > 0.75). Various strategies to construct predictive models were used. Internal validation of predictive models was often performed, while the majority of studies lacked external validation. None of the studies reported predictive models implemented in clinical practice.

CONCLUSION

Radiomics is increasingly used to predict response to treatment in patients suffering from gastrointestinal cancer. This review demonstrates its great potential to help predict response to treatment and improve patient selection and early adjustment of treatment strategy in a non-invasive manner.

Machine Learning and Radiomics Applications in Esophageal Cancers Using Non-Invasive Imaging Methods-A Critical Review of Literature

Esophageal cancer (EC) is of public health significance as one of the leading causes of cancer death worldwide. Accurate staging, treatment planning and prognostication in EC patients are of vital importance. Recent advances in machine learning (ML) techniques demonstrate their potential to provide novel quantitative imaging markers in medical imaging. Radiomics approaches that could quantify medical images into high-dimensional data have been shown to improve the imaging-based classification system in characterizing the heterogeneity of primary tumors and lymph nodes in EC patients. In this review, we aim to provide a comprehensive summary of the evidence of the most recent developments in ML application in imaging pertinent to EC patient care. According to the published results, ML models evaluating treatment response and lymph node metastasis achieve reliable predictions, ranging from acceptable to outstanding in their validation groups. Patients stratified by ML models in different risk groups have a significant or borderline significant difference in survival outcomes. Prospective large multi-center studies are suggested to improve the generalizability of ML techniques with standardized imaging protocols and harmonization between different centers.

A Systematic Review of PET Textural Analysis and Radiomics in Cancer

Background: Although many works have supported the utility of PET radiomics, several authors have raised concerns over the robustness and replicability of the results. This study aimed to perform a systematic review on the topic of PET radiomics and the used methodologies. Methods: PubMed was searched up to 15 October 2020. Original research articles based on human data specifying at least one tumor type and PET image were included, excluding those that apply only first-order statistics and those including fewer than 20 patients. Each publication, cancer type, objective and several methodological parameters (number of patients and features, validation approach, among other things) were extracted. Results: A total of 290 studies were included. Lung (28%) and head and neck (24%) were the most studied cancers. The most common objective was prognosis/treatment response (46%), followed by diagnosis/staging (21%), tumor characterization (18%) and technical evaluations (15%). The average number of patients included was 114 (median = 71; range 20–1419), and the average number of high-order features calculated per study was 31 (median = 26, range 1–286). Conclusions: PET radiomics is a promising field, but the number of patients in most publications is insufficient, and very few papers perform in-depth validations. The role of standardization initiatives will be crucial in the upcoming years.

18F-FDG PET/CT Metrics Are Correlated to the Pathological Response in Esophageal Cancer Patients Treated With Induction Chemotherapy Followed by Neoadjuvant Chemo-Radiotherapy

Background and Objective

The aim of this study was to assess the ability of Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography (¹⁸F-FDG PET/CT) to provide functional information useful in predicting pathological response to an intensive neoadjuvant chemo-radiotherapy (nCRT) protocol for both esophageal squamous cell carcinoma (SCC) and adenocarcinoma (ADC) patients.

Material and Methods

Esophageal carcinoma (EC) patients, treated in our Center between 2014 and 2018, were retrospectively reviewed. The nCRT protocol schedule consisted of an induction phase of weekly administered docetaxel, cisplatin, and 5-fluorouracil (TCF) for 3 weeks, followed by a concomitant phase of weekly TCF for 5 weeks with concurrent radiotherapy (50–50.4 Gy in 25–28 fractions). Three ¹⁸F-FDG PET/CT scans were performed: before (PET₁) and after (PET₂) induction chemotherapy (IC), and prior to surgery (PET₃). Correlation between PET parameters [maximum and mean standardized uptake value (SUV_max and SUV_mean), metabolic tumor volume (MTV), and total lesion glycolysis (TLG)], radiomic features and tumor regression grade (TGR) was investigated.

Results

Fifty-four patients (35 ADC, 19 SCC; 48 cT3/4; 52 cN+) were eligible for the analysis. Pathological response to nCRT was classified as major (TRG1-2, 41/54, 75.9%) or non-response (TRG3-4, 13/54, 24.1%). A major response was statistically correlated with SCC subtype (p = 0.02) and smaller tumor length (p = 0.03). MTV and TLG measured prior to IC (PET₁) were correlated to TRG1-2 response (p = 0.02 and p = 0.02, respectively). After IC (PET₂), SUV_mean and TLG correlated with major response (p = 0.03 and p = 0.04, respectively). No significance was detected when relative changes of metabolic parameters between PET₁ and PET₂ were evaluated. At textural quantitative analysis, three independent radiomic features extracted from PET₁ images ([JointEnergy and InverseDifferenceNormalized of GLCM and LowGrayLevelZoneEmphasis of GLSZM) were statistically correlated with major response (p < 0.0002).

Conclusions

¹⁸F-FDG PET/CT traditional metrics and textural features seem to predict pathologic response (TRG) in EC patients treated with induction chemotherapy followed by neoadjuvant chemo-radiotherapy. Further investigations are necessary in order to obtain a reliable predictive model to be used in the clinical practice.

Addition of HER2 and CD44 to 18F-FDG PET-based clinico-radiomic models enhances prediction of neoadjuvant chemoradiotherapy response in esophageal cancer

OBJECTIVES

To assess the complementary value of human epidermal growth factor receptor 2 (HER2)-related biological tumor markers to clinico-radiomic models in predicting complete response to neoadjuvant chemoradiotherapy (NCRT) in esophageal cancer patients.

METHODS

Expression of HER2 was assessed by immunohistochemistry in pre-treatment tumor biopsies of 96 patients with locally advanced esophageal cancer. Five other potentially active HER2-related biological tumor markers in esophageal cancer were examined in a sub-analysis on 43 patients. Patients received at least four of the five cycles of chemotherapy and full radiotherapy regimen followed by esophagectomy. Three reference clinico-radiomic models based on ¹⁸F-FDG PET were constructed to predict pathologic response, which was categorized into complete versus incomplete (Mandard tumor regression grade 1 vs. 2-5). The complementary value of the biological tumor markers was evaluated by internal validation through bootstrapping.

RESULTS

Pathologic examination revealed 21 (22%) complete and 75 (78%) incomplete responders. HER2 and cluster of differentiation 44 (CD44), analyzed in the sub-analysis, were univariably associated with pathologic response. Incorporation of HER2 and CD44 into the reference models improved the overall performance (R²s of 0.221, 0.270, and 0.225) and discrimination AUCs of 0.759, 0.857, and 0.816. All models exhibited moderate to good calibration. The remaining studied biological tumor markers did not yield model improvement.

CONCLUSIONS

Incorporation of HER2 and CD44 into clinico-radiomic prediction models improved NCRT response prediction in esophageal cancer. These biological tumor markers are promising in initial response evaluation.

KEY POINTS

• A multimodality approach, integrating independent genomic and radiomic information, is promising to improve prediction of γpCR in patients with esophageal cancer. • HER2 and CD44 are potential biological tumor markers in the initial work-up of patients with esophageal cancer. • Prediction models combining ¹⁸F-FDG PET radiomic features with HER2 and CD44 may be useful in the decision to omit surgery after neoadjuvant chemoradiotherapy in patients with esophageal cancer.

Assessment of Intratumoral and Peritumoral Computed Tomography Radiomics for Predicting Pathological Complete Response to Neoadjuvant Chemoradiation in Patients With Esophageal Squamous Cell Carcinoma

IMPORTANCE

For patients with locally advanced esophageal squamous cell carcinoma, neoadjuvant chemoradiation has been shown to improve long-term outcomes, but the treatment response varies among patients. Accurate pretreatment prediction of response remains an urgent need.

OBJECTIVE

To determine whether peritumoral radiomics features derived from baseline computed tomography images could provide valuable information about neoadjuvant chemoradiation response and enhance the ability of intratumoral radiomics to estimate pathological complete response.

DESIGN, SETTING, AND PARTICIPANTS

A total of 231 patients with esophageal squamous cell carcinoma, who underwent baseline contrast-enhanced computed tomography and received neoadjuvant chemoradiation followed by surgery at 2 institutions in China, were consecutively included. This diagnostic study used single-institution data between April 2007 and December 2018 to extract radiomics features from intratumoral and peritumoral regions and established intratumoral, peritumoral, and combined radiomics models using different classifiers. External validation was conducted using independent data collected from another hospital during the same period. Radiogenomics analysis using gene expression profile was done in a subgroup of the training set for pathophysiological explanation. Data were analyzed from June to December 2019.

EXPOSURES

Computed tomography-based radiomics.

MAIN OUTCOMES AND MEASURES

The discriminative performances of radiomics models were measured by area under the receiver operating characteristic curve.

RESULTS

Among the 231 patients included (192 men [83.1%]; mean [SD] age, 59.8 [8.7] years), the optimal intratumoral and peritumoral radiomics models yielded similar areas under the receiver operating characteristic curve of 0.730 (95% CI, 0.609-0.850) and 0.734 (0.613-0.854), respectively. The combined model was composed of 7 intratumoral and 6 peritumoral features and achieved better discriminative performance, with an area under the receiver operating characteristic curve of 0.852 (95% CI, 0.753-0.951), accuracy of 84.3%, sensitivity of 90.3%, and specificity of 79.5% in the test set. Gene sets associated with the combined model mainly involved lymphocyte-mediated immunity. The association of peritumoral area with response identification might be partially attributed to type I interferon-related biological process.

CONCLUSIONS AND RELEVANCE

A combination of peritumoral radiomics features appears to improve the predictive performance of intratumoral radiomics to estimate pathological complete response after neoadjuvant chemoradiation in patients with esophageal squamous cell carcinoma. This study underlines the significant application of peritumoral radiomics to assess treatment response in clinical practice.

Role of precision imaging in esophageal cancer

Esophageal cancer is a major cause of morbidity and mortality worldwide. Recent advancements in the management of esophageal cancer have allowed for earlier detection, improved ability to monitor progression, and superior treatment options. These innovations allow treatment teams to formulate more customized management plans and have led to an increase in patient survival rates. For example, in order for the most effective management plan to be constructed, accurate staging must be performed to determine tumor resectability. This article reviews the multimodality imaging approach involved in making a diagnosis, staging, evaluating treatment response and detecting recurrence in esophageal cancer.

Diagnostic advances of artificial intelligence and radiomics in gastroenterology

Traditional medical imaging, including ultrasound, computed tomography, magnetic resonance imaging, or positron emission tomography, remains widely used diagnostic modalities for gastrointestinal diseases at present. These modalities are used to assess changes in morphology, attenuation, signal intensity, and enhancement characteristics. Gastrointestinal tumors, especially malignant tumors, are commonly seen in clinical practice with an increasing number of deaths each year. Because the imaging manifestations of different diseases usually overlap, accurate early diagnosis of tumor lesions, noninvasive and effective evaluation of tumor staging, and prediction of prognosis remain challenging. Fortunately, traditional medical images contain a great deal of important information that cannot be recognized by human eyes but can be extracted by artificial intelligence (AI) technology, which can quantitatively assess the heterogeneity of lesions and provide valuable information, including therapeutic effects and patient prognosis. With the development of computer technology, the combination of medical imaging and AI technology is considered to represent a promising field in medical image analysis. This new emerging field is called “radiomics”, which makes big data mining and extraction from medical imagery possible and can help clinicians make effective decisions and develop personalized treatment plans. Recently, AI and radiomics have been gradually applied to lesion detection, qualitative and quantitative diagnosis, histopathological grading and staging of tumors, therapeutic efficacy assessment, and prognosis evaluation. In this minireview, we briefly introduce the basic principles and technology of radiomics. Then, we review the research and application of AI and radiomics in gastrointestinal diseases, especially diagnostic advancements of radiomics in the differential diagnosis, treatment option, assessment of therapeutic efficacy, and prognosis evaluation of esophageal, gastric, hepatic, pancreatic, and colorectal diseases.

Quantitative accuracy of radiomic features of low-dose 18F-FDG PET imaging

Background

¹⁸F-FDG PET based radiomics is promising for precision oncology imaging. This work aims to explore quantitative accuracies of radiomic features (RFs) for low-dose ¹⁸F-FDG PET imaging.

Methods

Twenty lung cancer patients were prospectively enrolled and underwent ¹⁸F-FDG PET/CT scans. Low-dose PET situations (true counts: 20×10⁶, 15×10⁶, 10×10⁶, 7.5×10⁶, 5×10⁶, 2×10⁶, 1×10⁶, 0.5×10⁶, 0.25×10⁶) were simulated by randomly discarding counts from the acquired list-mode data. Each PET image was created using the scanner default reconstruction parameters. Each lesion volume of interest (VOI) was obtained via an adaptive contouring method with a threshold of 50% peak standardized uptake value (SUVpeak) in the PET images with full count data and VOIs were copied to the PET images at the reduced count level. Conventional SUV measures, features calculated from first-order statistics (FOS) and texture features (TFs) were calculated. Texture based RF include features calculated from gray-level co-occurrence matrix (GLCM), gray-level run length matrix (GLRLM), gray-level size zone matrix (GLSZM), neighboring gray-level dependence matrix (NGLDM) and neighbor gray-tone difference matrix (NGTDM). Bias percentage (BP) at different count levels for each RF was calculated.

Results

Fifty-seven lesions with a volume greater than 1.5 cm³ were found (mean volume, 25.7 cm³, volume range, 1.5–245.4 cm³). In comparison with normal total counts, mean SUV (SUVmean) in the lesions, normal lungs and livers, Entropy and sum entropy from GLCM, busyness from NGTDM and run-length non-uniformity from GLRLM were the most robust features, with a BP of 5% at the count level of 1×10⁶ (equivalent to an effective dose of 0.04 mSv) RF including cluster shade from GLCM, long-run low grey-level emphasis, high grey-level run emphasis and short-run low grey-level emphasis from GLRM exhibited the worst performance with 50% of bias with 20×10⁶ counts (equivalent to an effective dose of 0.8 mSv).

Conclusions

In terms of the lesions included in this study, SUVmean, entropy and sum entropy from GLCM, busyness from NGTDM and run-length non-uniformity from GLRLM were the least sensitive features to lowering count.

Heterogeneity in tumours: Validating the use of radiomic features on 18F-FDG PET/CT scans of lung cancer patients as a prognostic tool

AIM

The aim was to validate promising radiomic features (RFs)1 on 18F-flourodeoxyglucose positron emission tomography/computed tomography-scans (18F-FDG PET/CT) of non-small cell lung cancer (NSCLC) patients undergoing definitive chemo-radiotherapy.

METHODS

18F-FDG PET/CT scans performed for radiotherapy (RT) planning were retrieved. Auto-segmentation with visual adaption was used to define the primary tumour on PET images. Six pre-selected prognostic and reproducible PET texture -and shape-features were calculated using texture respectively shape analysis. The correlation between these RFs and metabolic active tumour volume (MTV)3, gross tumour volume (GTV)4 and maximum and mean of standardized uptake value (SUV)5 was tested with a Spearman's Rank test. The prognostic value of RFs was tested in a univariate cox regression analysis and a multivariate cox regression analysis with GTV, clinical stage and histology. P-value ≤ 0.05 were considered significant.

RESULTS

Image analysis was performed for 233 patients: 145 males and 88 females, mean age of 65.7 and clinical stage II-IV. Mean GTV was 129.87 cm3 (SD 130.30 cm3). Texture and shape-features correlated more strongly to MTV and GTV compared to SUV-measurements. Four RFs predicted PFS in the univariate analysis. No RFs predicted PFS in the multivariate analysis, whereas GTV and clinical stage predicted PFS (p = 0.001 and p = 0.008 respectively).

CONCLUSION

The pre-selected RFs were insignificant in predicting PFS in combination with GTV, clinical stage and histology. These results might be due to variations in technical parameters. However, it is relevant to question whether RFs are stable enough to provide clinically useful information.

Development and validation of a radiomics signature on differentially expressed features of 18F-FDG PET to predict treatment response of concurrent chemoradiotherapy in thoracic esophagus squamous cell carcinoma

BACKGROUND AND PURPOSE

To investigate potential image markers for early prediction of treatment response on thoracic esophagus squamous cell carcinoma (ESCC) treated with concurrent chemoradiotherapy (CCRT).

MATERIALS AND METHODS

159 thoracic ESCC patients enrolled from two institutions were divided into training and validation sets. A total of 944 radiomics features were extracted from pretreatment ¹⁸F-FDG PET images. We first performed the inter-observer reproducibility test in 10 pairs of patients (responders vs. nonresponders), and the limma package was used to identify differentially expressed features (DEFs). Then the least absolute shrinkage and selection operator (LASSO) logistic regression model with 10-fold cross-validation was used to construct a treatment response related radiomics signature. Finally, the performance was assessed in both sets with receiver operating characteristic (ROC) curves and Kaplan-Meier analysis.

RESULTS

After the inter-observer test, 691 features were considered reproducible and been retained (ICC > 0.9). 61 DEFs were selected from limma and entered into the LASSO logistic regression model. The radiomics signature was significantly associated with treatment response in the training (p < 0.001) and validation set (p = 0.026), which achieved area under curve (AUC) values of 0.844 and 0.835, respectively. Delong test results of two ROCs showed no significant difference (p = 0.918). The cut-off value of the radiomics signature could successfully divide patients into high-risk and low-risk groups in both sets.

CONCLUSION

This study indicated that the proposed radiomics signature could be a useful image marker to predict the therapeutic response of thoracic ESCC patients treated with CCRT.

Machine learning for radiomics-based multimodality and multiparametric modeling

Due to the recent developments of both hardware and software technologies, multimodality medical imaging techniques have been increasingly applied in clinical practice and research studies. Previously, the application of multimodality imaging in oncology has been mainly related to combining anatomical and functional imaging to improve diagnostic specificity and/or target definition, such as positron emission tomography/computed tomography (PET/CT) and single-photon emission CT (SPECT)/CT. More recently, the fusion of various images, such as multiparametric magnetic resonance imaging (MRI) sequences, different PET tracer images, PET/MRI, has become more prevalent, which has enabled more comprehensive characterization of the tumor phenotype. In order to take advantage of these valuable multimodal data for clinical decision making using radiomics, we present two ways to implement the multimodal image analysis, namely radiomic (handcrafted feature) based and deep learning (machine learned feature) based methods. Applying advanced machine (deep) learning algorithms across multimodality images have shown better results compared with single modality modeling for prognostic and/or prediction of clinical outcomes. This holds great potentials for providing more personalized treatment for patients and achieve better outcomes.

Staging and Response Evaluation to Neo-Adjuvant Chemoradiation in Esophageal Cancers Using 18FDG PET/ CT with Standardized Protocol

Background: Precise staging of esophageal cancer (EC) is important for selection of optimal treatment option and prognostication. Aim of this study was to assess the role of 18FDG PET/CT in staging and response evaluation to neoadjuvant chemoradiation (nCR) in EC patients using standardized imaging protocol. Material and methods: This prospective study was conducted at PET/CT Section of Department of Radiology, Aga Khan University Hospital Karachi, Pakistan from July 2017 till February 2018. We included 34 biopsy proven EC patients who had 18FDG PET/CT and CT of neck, chest and abdomen as part of initial staging. Eleven patients had post-nCR 18FDG PET/CT using standardized imaging protocol as per EANM guidelines. CT and PET/CT based staging was compared. Based on PERCIST criteria, response evaluation was assessed using change in highest SUVmax (%ΔSUVmax) in baseline and follow-up scans (primary lesion, node or extra-nodal metastases). Results: Mean age of cohort was 57 ± 14 years (23 males and 11 females) having adenocarcinoma (AC) in 23 and squamous cell cancer (SCC) in 11 patients. Mean 18FDG dose, uptake time and hepatic SUVmean for baseline scans were 169 ±54 MBq, 65 ±10 minute and 1.91 ± 0.49 which were within ± 10%, ± 15% and ± 20% for follow-up scans in 11 patients respectively. Mean size (craniocaudal dimension in mm) and SUVmax of primary tumor was 56 ±27 mm and 13.4 ± 4.7. Based on 18FDG PET/CT findings, patients were categorized into N0 (10/34), N1 (09/34), N2 (11/34) and N3 (04/34) while 11/32 had stage IV disease. No significant difference was seen in AC and SCC groups. CT found stage IV disease in 3/34 (09%) while PET/CT found in 11/34 (32%; p value: 0.019) cases. PET/CT showed concordance with CT in 41% while discordance (all with upstaging) seen in 59%. On follow-up PET/CT, complete metabolic response was seen in 5/11 (45%) and partial metabolic response was noted in 6/11 (55% - p value non-significant) patients. Median %ΔSUVmax over primary lesions was 49.84% (-32.69 -100%) while over nodal sites it was 41.18% (-82.60 -100%). Conclusion: We conclude that 18FDG PET/CT was found a sensitive tool in initial staging of EC. Compared with CT, it had higher diagnostic accuracy for distant nodal and extra-nodal metastasis. %ΔSUVmax between baseline and post-nCR studies acquired with standardized protocol had changed management in more than half of our patients. For response evaluation in EC more studies with standardized 18FDG PET/CT imaging protocols are warranted.

Exploratory radiomic features from integrated 18F-fluorodeoxyglucose positron emission tomography/magnetic resonance imaging are associated with contemporaneous metastases in oesophageal/gastroesophageal cancer

PURPOSE

The purpose of this study was to determine if 18F-fluorodeoxyglucose positron emission tomography/magnetic resonance imaging (18F-FDG PET/MRI) features are associated with contemporaneous metastases in patients with oesophageal/gastroesophageal cancer.

METHODS

Following IRB approval and informed consent, patients underwent a staging PET/MRI following 18F-FDG injection (326 ± 28 MBq) and 156 ± 23 min uptake time. First-order histogram and second-order grey level co-occurrence matrix features were computed for PET standardized uptake value (SUV) and MRI T1-W, T2-W, diffusion weighted (DWI) and apparent diffusion coefficient (ADC) images for the whole tumour volume. K-means clustering assessed the correlation of feature-pairs with metastases. Multivariate analysis of variance (MANOVA) was performed to assess the statistical separability of the groups identified by feature-pairs. Sensitivity (SN), specificity (SP), positive predictive value (PPV), negative predictive value (NPV), and accuracy (ACC) were calculated for these features and compared with SUVmax, ADCmean and maximum diameter alone for predicting contemporaneous metastases.

RESULTS

Twenty patients (18 males, 2 female; median 67 years, range 52-86) comprised the final study cohort; ten patients had metastases. Lower second-order SUV entropy combined with higher second-order ADC entropy were the best feature-pair for discriminating metastatic patients, MANOVA p value <0.001 (SN = 80%, SP = 80%, PPV = 80%, NPV = 80%, ACC = 80%). SUVmax (SN = 30%, SP = 80%, PPV = 60%, NPV = 53%, ACC = 55%), ADCmean (SN = 20%, SP = 70%, PPV = 40%, NPV = 47%, ACC = 45%) and tumour maximum diameter (SN = 10%, SP = 90%, PPV = 50%, NPV = 50%, ACC = 50%) had poorer sensitivity and accuracy.

CONCLUSION

High ADC entropy combined with low SUV entropy is associated with a higher prevalence of metastases and a promising initial signature for future study.

Radiomics in esophageal and gastric cancer

Esophageal, esophago-gastric, and gastric cancers are major causes of cancer morbidity and cancer death. For patients with potentially resectable disease, multi-modality treatment is recommended as it provides the best chance of survival. However, quality of life may be adversely affected by therapy, and with a wide variation in outcome despite multi-modality therapy, there is a clear need to improve patient stratification. Radiomic approaches provide an opportunity to improve tumor phenotyping. In this review we assess the evidence to date and discuss how these approaches could improve outcome in esophageal, esophago-gastric, and gastric cancer.

Sub-region based radiomics analysis for survival prediction in oesophageal tumours treated by definitive concurrent chemoradiotherapy

BACKGROUND

Evaluating clinical outcome prior to concurrent chemoradiotherapy remains challenging for oesophageal squamous cell carcinoma (OSCC) as traditional prognostic markers are assessed at the completion of treatment. Herein, we investigated the potential of using sub-region radiomics as a novel tumour biomarker in predicting overall survival of OSCC patients treated by concurrent chemoradiotherapy.

METHODS

Independent patient cohorts from two hospitals were included for training (n = 87) and validation (n = 46). Radiomics features were extracted from sub-regions clustered from patients' tumour regions using K-means method. The LASSO regression for 'Cox' method was used for feature selection. The survival prediction model was constructed based on the sub-region radiomics features using the Cox proportional hazards model. The clinical and biological significance of radiomics features were assessed by correlation analysis of clinical characteristics and copy number alterations(CNAs) in the validation dataset.

FINDINGS

The overall survival prediction model combining with seven sub-regional radiomics features was constructed. The C-indexes of the proposed model were 0.729 (0.656-0.801, 95% CI) and 0.705 (0.628-0.782, 95%CI) in the training and validation cohorts, respectively. The 3-year survival receiver operating characteristic (ROC) curve showed an area under the ROC curve of 0.811 (0.670-0.952, 95%CI) in training and 0.805 (0.638-0.973, 95%CI) in validation. The correlation analysis showed a significant correlation between radiomics features and CNAs.

INTERPRETATION

The proposed sub-regional radiomics model could predict the overall survival risk for patients with OSCC treated by definitive concurrent chemoradiotherapy. FUND: This work was supported by the Zhejiang Provincial Foundation for Natural Sciences, National Natural Science Foundation of China.

A PET Radiomics Model to Predict Refractory Mediastinal Hodgkin Lymphoma

First-order radiomic features, such as metabolic tumor volume (MTV) and total lesion glycolysis (TLG), are associated with disease progression in early-stage classical Hodgkin lymphoma (HL). We hypothesized that a model incorporating first- and second-order radiomic features would more accurately predict outcome than MTV or TLG alone. We assessed whether radiomic features extracted from baseline PET scans predicted relapsed or refractory disease status in a cohort of 251 patients with stage I-II HL who were managed at a tertiary cancer center. Models were developed and tested using a machine-learning algorithm. Features extracted from mediastinal sites were highly predictive of primary refractory disease. A model incorporating 5 of the most predictive features had an area under the curve (AUC) of 95.2% and total error rate of 1.8%. By comparison, the AUC was 78% for both MTV and TLG and was 65% for maximum standardize uptake value (SUV_max). Furthermore, among the patients with refractory mediastinal disease, our model distinguished those who were successfully salvaged from those who ultimately died of HL. We conclude that our PET radiomic model may improve upfront stratification of early-stage HL patients with mediastinal disease and thus contribute to risk-adapted, individualized management.

Prognostic factors in breast cancer patients evaluated by positron-emission tomography/computed tomography before neoadjuvant chemotherapy

Neoadjuvant chemotherapy (NAC) is a significant modality in breast cancer therapy. We sought to characterize prognostic factors in patients scheduled for NAC who had a pretreatment positron-emission tomography paired with diagnostic quality contrast-enhanced computed tomography (CT) (positron-emission tomography/CT [PET/CT]). A total of 118 breast cancer patients were analyzed through chart review who underwent pretreatment PET/CT imaging and received NAC from 2008 to 2014. We collected information on molecular markers, PET/CT, pathologic complete response (pCR), survival, and disease status. Pretreatment standard uptake value (SUV) max of the primary breast tumor showed no relationship to pCR; however, there was a statistically significant relationship with relapse-free survival (RFS) using univariate cox regression (P = 0.03, odds ratio (OR) = 1.06 [1.01-1.12]) with comparable findings observed with overall survival (OS). Multivariate analysis revealed SUV max to be significantly correlated with shortened OS (P = 0.022, OR = 1.08 [1.01-1.16]), with a similar trend reported for RFS. By pathological subtype, this correlation was the strongest within hormone receptor (HR+)/human epidermal growth factor receptor 2 (HER2-) tumors. In addition, Kaplan-Meier estimates demonstrated a significant difference between the RFS of triple-negative tumors and HER2 positive tumors (P = 0.001). Interestingly, within this cohort, multivariate Cox regression analysis showed HER2 positivity to be associated with favorable outcome (P = 0.04, HR = 0.22 [0.05-0.94]). These findings demonstrate a significant association between SUV max of HR+/HER2-- tumors and relapse-free and OS. Furthermore, highlighted here is the favorable survival in the once classically aggressive HER2+ breast cancer subgroup.

Challenges and Promises of PET Radiomics

PURPOSE

Radiomics describes the extraction of multiple, otherwise invisible, features from medical images that, with bioinformatic approaches, can be used to provide additional information that can predict underlying tumor biology and behavior.

METHODS AND MATERIALS

Radiomic signatures can be used alone or with other patient-specific data to improve tumor phenotyping, treatment response prediction, and prognosis, noninvasively. The data describing 18F-fluorodeoxyglucose positron emission tomography radiomics, often using texture or heterogeneity parameters, are increasing rapidly.

RESULTS

In relation to radiation therapy practice, early data have reported the use of radiomic approaches to better define tumor volumes and predict radiation toxicity and treatment response.

CONCLUSIONS

Although at an early stage of development, with many technical challenges remaining and a need for standardization, promise nevertheless exists that PET radiomics will contribute to personalized medicine, especially with the availability of increased computing power and the development of machine-learning approaches for imaging.

A meta-analysis on salvage surgery as a potentially curative procedure in patients with isolated local recurrent or persistent esophageal cancer after chemoradiotherapy

BACKGROUND

Isolated local recurrent or persistent esophageal cancer (EC) after curative intended definitive (dCRT) or neoadjuvant chemoradiotherapy (nCRT) with initially omitted surgery, is a potential indication for salvage surgery. We aimed to evaluate safety and efficacy of salvage surgery in these patients.

MATERIAL AND METHODS

A systematic literature search following PRISMA guidelines was performed using databases of PubMed/Medline. All included studies were performed in patients with persistent or recurrent EC after initial treatment with dCRT or nCRT, between 2007 and 2017. Survival analysis was performed with an inverse-variance weighting method.

RESULTS

Of the 278 identified studies, 28 were eligible, including a total of 1076 patients. Postoperative complications after salvage esophagectomy were significantly more common among patients with isolated persistent than in those with locoregional recurrent EC, including respiratory (36.6% versus 22.7%; difference in proportion 10.9 with 95% confidence interval (CI) [3.1; 18.7]) and cardiovascular complications (10.4% versus 4.5%; difference in proportion 5.9 with 95% CI [1.5; 10.2]). The pooled estimated 30- and 90-day mortality was 2.6% [1.6; 3.6] and 8.0% [6.3; 9.8], respectively. The pooled estimated 3-year and 5-year overall survival (OS) were 39.0% (95% CI: [35.8; 42.2]) and 19.4% [95% CI:16.5; 22.4], respectively. Patients with isolated persistent or recurrent EC after initial CRT had similar 5-year OS (14.0% versus 19.7%, difference in proportion -5.7, 95% CI [-13.7; 2.3]).

CONCLUSIONS

Salvage surgery is a potentially curative procedure in patients with locally recurrent or persistent esophageal cancer and can be performed safely after definitive or neoadjuvant chemoradiotherapy when surgery was initially omitted.

Modified fractal analysis of methionine positron emission tomography images for predicting prognosis in newly diagnosed patients with glioma

OBJECTIVE

To assess intratumoural metabolic heterogeneity using modified fractal analysis and to determine its prognostic significance in patients with glioma.

PATIENTS AND METHODS

A total of 57 patients with newly diagnosed glioma who underwent methionine PET-computed tomography between August 2012 and January 2017 were enrolled. The requirement for informed consent was waived for this retrospective study. Tumour-to-normal tissue ratio, metabolic tumour volume, total lesion methionine uptake and modified fractal dimension (m-FD) were calculated for each tumour using methionine PET-computed tomography. Associations between these indices and tumour grade and overall survival were analysed.

RESULTS

Overall, eight patients had grade II, 20 had grade III and 29 had grade IV tumours. The tumour-to-normal tissue ratios of grade III and grade IV tumours were significantly greater than that of grade II tumours. The metabolic tumour volume and total lesion methionine uptake of grade III tumours were significantly greater than those of grade II and grade IV tumours. The m-FD of grade IV tumours was significantly greater than those of grade II and grade III tumours. A total of 47 patients were followed up, and their prognoses were evaluated. Only the m-FD was significantly associated with a poor prognosis (P<0.05). Multivariate analyses identified age (>58 years) (hazard ratio: 5.73; 95.0% confidence interval: 1.4-29.9; P=0.015) and the m-FD (>0.87) (hazard ratio: 4.80; 95.0% confidence interval: 1.12-32.9; P=0.033) as independent prognostic factors for overall survival.

CONCLUSION

Intratumoural metabolic heterogeneity is a useful imaging biomarker in patients with glioma.