Early regression index (ERI) on MR images as response predictor in esophageal cancer treated with neoadjuvant chemo-radiotherapy: Interim analysis of the prospective ESCAPE trial

PURPOSE

The early regression index (ERI) predicts treatment response in rectal cancer patients. Aim of current study was to prospectively assess tumor response to neoadjuvant chemo-radiotherapy (nCRT) of locally advanced esophageal cancer using ERI, based on MRI.

MATERIAL AND METHODS

From January 2020 to May 2023, 30 patients with esophageal cancer were enrolled in a prospective study (ESCAPE). PET-MRI was performed: i) before nCRT (tpre); ii) at mid-radiotherapy, tmid; iii) after nCRT, 2-6 weeks before surgery (tpost); nCRT delivered 41.4 Gy/23fr with concurrent carboplatin and paclitaxel. For patients that skipped surgery, complete clinical response (cCR) was assessed if patients showed no local relapse after 18 months; patients with pathological complete response (pCR) or with cCR were considered as complete responders (pCR + cCR). GTV volumes were delineated by two observers (Vpre, Vmid, Vpost) on T2w MRI: ERI and other volume regression parameters at tmid and tpost were tested as predictors of pCR + cCR.

RESULTS

Complete data of 25 patients were available at the time of the analysis: 3/25 with complete response at imaging refused surgery and 2/3 were cCR; in total, 10/25 patients showed pCR + cCR (pCR = 8/22). Both ERImid and ERIpost classified pCR + cCR patients, with ERImid showing better performance (AUC:0.78, p = 0.014): A two-variable logistic model combining ERImid and Vpre improved performances (AUC:0.93, p < 0.0001). Inter-observer variability in contouring GTV did not affect the results.

CONCLUSIONS

Despite the limited numbers, interim analysis of ESCAPE study suggests ERI as a potential predictor of complete response after nCRT for esophageal cancer. Further validation on larger populations is warranted.

Remarkable response as a new indicator for endoscopic evaluation of local efficacy of non-surgical treatments for esophageal cancer

In Japan, standard of care of the patients with resectable esophageal cancer is neoadjuvant chemotherapy (NAC) followed by esophagectomy. Patients unfitted for surgery or with unresectable locally advanced esophageal cancer are generally indicated with definitive chemoradiotherapy (CRT). Local disease control is undoubtful important for the management of patients with esophageal cancer, therefore endoscopic evaluation of local efficacy after non-surgical treatments must be essential. The significant shrink of primary site after NAC has been reported as a good indicator of pathological good response as well as favorable survival outcome after esophagectomy. And patients who could achieve remarkable shrink to T1 level after CRT had favorable outcomes with salvage surgery and could be good candidates for salvage endoscopic treatments. Based on these data, "Japanese Classification of Esophageal Cancer, 12th edition" defined the new endoscopic criteria "remarkable response (RR)", that means significant volume reduction after treatment, with the subjective endoscopic evaluation are proposed. In addition, the finding of local recurrence (LR) at primary site after achieving a CR was also proposed in the latest edition of Japanese Classification of Esophageal Cancer. The findings of LR are also important for detecting candidates for salvage endoscopic treatments at an early timing during surveillance after CRT. The endoscopic evaluation would encourage us to make concrete decisions for further treatment indications, therefore physicians treating patients with esophageal cancer should be well-acquainted with each finding.

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.

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.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

Methodological evaluation of original articles on radiomics and machine learning for outcome prediction based on positron emission tomography (PET)

AIM

Despite a vast number of articles on radiomics and machine learning in positron emission tomography (PET) imaging, clinical applicability remains limited, partly owing to poor methodological quality. We therefore systematically investigated the methodology described in publications on radiomics and machine learning for PET-based outcome prediction.

METHODS

A systematic search for original articles was run on PubMed. All articles were rated according to 17 criteria proposed by the authors. Criteria with >2 rating categories were binarized into "adequate" or "inadequate". The association between the number of "adequate" criteria per article and the date of publication was examined.

RESULTS

One hundred articles were identified (published between 07/2017 and 09/2023). The median proportion of articles per criterion that were rated "adequate" was 65% (range: 23-98%). Nineteen articles (19%) mentioned neither a test cohort nor cross-validation to separate training from testing. The median number of criteria with an "adequate" rating per article was 12.5 out of 17 (range, 4-17), and this did not increase with later dates of publication (Spearman's rho, 0.094; p = 0.35). In 22 articles (22%), less than half of the items were rated "adequate". Only 8% of articles published the source code, and 10% made the dataset openly available.

CONCLUSION

Among the articles investigated, methodological weaknesses have been identified, and the degree of compliance with recommendations on methodological quality and reporting shows potential for improvement. Better adherence to established guidelines could increase the clinical significance of radiomics and machine learning for PET-based outcome prediction and finally lead to the widespread use in routine clinical practice.

Baseline 18F-FDG PET/CT radiomics for prognosis prediction in diffuse large B cell lymphoma

BACKGROUND

Diffuse large B-cell lymphoma (DLBCL) is the most common subtype of non-Hodgkin lymphoma in adults. Standard treatment includes chemoimmunotherapy with R-CHOP or similar regimens. Despite treatment advancements, many patients with DLBCL experience refractory disease or relapse. While baseline 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) parameters have shown promise in predicting survival, they may not fully capture lesion heterogeneity. This study aimed to assess the prognostic value of baseline 18F-FDG PET radiomics features in comparison with clinical factors and metabolic parameters for assessing 2-year progression-free survival (PFS) and 5-year overall survival (OS) in patients with DLBCL.

RESULTS

A total of 201 patients with DLBCL were enrolled in this study, and 1328 radiomics features were extracted. The radiomics signatures, clinical factors, and metabolic parameters showed significant prognostic value for individualized prognosis prediction in patients with DLBCL. Radiomics signatures showed the lowest Akaike information criterion (AIC) value and highest Harrell's concordance index (C-index) value in comparison with clinical factors and metabolic parameters for both PFS (AIC: 571.688 vs. 596.040 vs. 576.481; C-index: 0.732 vs. 0.658 vs. 0.702, respectively) and OS (AIC: 339.843 vs. 363.671 vs. 358.412; C-index: 0.759 vs. 0.667 vs. 0.659, respectively). Statistically significant differences were observed in the area under the curve (AUC) values between the radiomics signatures and clinical factors for both PFS (AUC: 0.768 vs. 0.681, P = 0.017) and OS (AUC: 0.767 vs. 0.667, P = 0.023). For OS, the AUC of the radiomics signatures were significantly higher than those of metabolic parameters (AUC: 0.767 vs. 0.688, P = 0.007). However, for PFS, no significant difference was observed between the radiomics signatures and metabolic parameters (AUC: 0.768 vs. 0.756, P = 0.654). The combined model and the best-performing individual model (radiomics signatures) alone showed no significant difference for both PFS (AUC: 0.784 vs. 0.768, P = 0.163) or OS (AUC: 0.772 vs. 0.767, P = 0.403).

CONCLUSIONS

Radiomics signatures derived from PET images showed the high predictive power for progression in patients with DLBCL. The combination of radiomics signatures, clinical factors, and metabolic parameters may not significantly improve predictive value beyond that of radiomics signatures alone.

Non-invasive tumor microenvironment evaluation and treatment response prediction in gastric cancer using deep learning radiomics

The tumor microenvironment (TME) plays a critical role in disease progression and is a key determinant of therapeutic response in cancer patients. Here, we propose a noninvasive approach to predict the TME status from radiological images by combining radiomics and deep learning analyses. Using multi-institution cohorts of 2,686 patients with gastric cancer, we show that the radiological model accurately predicted the TME status and is an independent prognostic factor beyond clinicopathologic variables. The model further predicts the benefit from adjuvant chemotherapy for patients with localized disease. In patients treated with checkpoint blockade immunotherapy, the model predicts clinical response and further improves predictive accuracy when combined with existing biomarkers. Our approach enables noninvasive assessment of the TME, which opens the door for longitudinal monitoring and tracking response to cancer therapy. Given the routine use of radiologic imaging in oncology, our approach can be extended to many other solid tumor types.

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSION

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

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.

An artificial neural network-based radiomics model for predicting the radiotherapy response of advanced esophageal squamous cell carcinoma patients: a multicenter study

Radiotherapy benefits patients with advanced esophageal squamous cell carcinoma (ESCC) in terms of symptom relief and long-term survival. In contrast, a substantial proportion of ESCC patients have not benefited from radiotherapy. This study aimed to establish and validate an artificial neural network-based radiomics model for the pretreatment prediction of the radiotherapy response of advanced ESCC by using integrated data combined with feasible baseline characteristics of computed tomography. A total of 248 patients with advanced ESCC who underwent baseline CT and received radiotherapy were enrolled in this study and were analyzed by two types of radiomics models, machine learning and deep learning. As a result, the Att. Resnet50 pretrained network model indicated superior performance, with AUCs of 0.876, 0.802 and 0.732 in the training, internal validation, and external validation cohorts, respectively. Similarly, our Att. Resnet50 pretrained network model showed excellent calibration and significant clinical benefit according to the C index and decision curve analysis. Herein, a novel pretreatment radiomics model was established based on deep learning methods and could be used for radiotherapy response prediction in advanced ESCC patients, thus providing reliable evidence for therapeutic decision-making.

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.

Comparing a PD-L1 inhibitor plus chemotherapy to chemotherapy alone in neoadjuvant therapy for locally advanced ESCC: a randomized Phase II clinical trial : A randomized clinical trial of neoadjuvant therapy for ESCC

BACKGROUND

A Phase II study was undertaken to evaluate the safety and efficacy of the neoadjuvant socazolimab, a novel PD-L1 inhibitor, in combination with nab-paclitaxel and cisplatin for locally advanced esophageal squamous cell carcinoma (ESCC).

METHODS

Sixty-four patients were randomly divided between the Socazolimab + nab-paclitaxel + cisplatin (TP) arm (n = 32) and the control arm (n = 32), receiving either socazolimab (5 mg/kg intravenously (IV), day 1) or a placebo with nab-paclitaxel (125 mg/m² IV, day 1/8) and cisplatin (75 mg/m² IV, day 1) repeated every 21 days for four cycles before surgery. The primary endpoint was major pathological response (MPR), and the secondary endpoints were pathological complete response (pCR), R0 resection rate, event-free survival (EFS), overall survival (OS), and safety.

RESULTS

A total of 29 (90.6%) patients in each arm underwent surgery, and 29 (100%) and 28 (98.6%) patients underwent R0 resection in the Socazolimab + TP and Placebo + TP arms, respectively. The MPR rates were 69.0 and 62.1% (95% Confidence Interval (CI): 49.1-84.0% vs. 42.4-78.7%, P = 0.509), and the pCR rates were 41.4 and 27.6% (95% CI: 24.1-60.9% vs. 13.5-47.5%, P = 0.311) in the Socazolimab + TP and Placebo + TP arms, respectively. Significantly higher incidence rates of ypT0 (37.9% vs. 3.5%; P = 0.001) and T downstaging were observed in the Socazolimab + TP arm than in the Placebo + TP arm. The EFS and OS outcomes were not mature.

CONCLUSIONS

The neoadjuvant socazolimab combined with chemotherapy demonstrated promising MPR and pCR rates and significant T downstaging in locally advanced ESCC without increasing surgical complication rates.

TRIAL REGISTRATION

Registration name (on clinicaltrials.gov): A Study of Anti-PD-L1 Antibody in Neoadjuvant Chemotherapy of Esophageal Squamous Cell Carcinoma.

REGISTRATION NUMBER

NCT04460066.

A radiomics model for predicting the response to methylprednisolone in brain necrosis after radiotherapy for nasopharyngeal carcinoma

BACKGROUND

Methylprednisolone is recommended as the front-line therapy for radiation-induced brain necrosis (RN) after radiotherapy for nasopharyngeal carcinoma. However, some patients fail to benefit from methylprednisolone or even progress. This study aimed to develop and validate a radiomic model to predict the response to methylprednisolone in RN.

METHODS

Sixty-six patients receiving methylprednisolone were enrolled. In total, 961 radiomic features were extracted from the pre-treatment magnetic resonance imagings of the brain. Least absolute shrinkage and selection operator regression was then applied to construct the radiomics signature. Combined with independent clinical predictors, a radiomics model was built with multivariate logistic regression analysis. Discrimination, calibration and clinical usefulness of the model were assessed. The model was internally validated using 10-fold cross-validation.

RESULTS

The radiomics signature consisted of 16 selected features and achieved favorable discrimination performance. The radiomics model incorporating the radiomics signature and the duration between radiotherapy and RN diagnosis, yielded an AUC of 0.966 and an optimism-corrected AUC of 0.967 via 10-fold cross-validation, which also revealed good discrimination. Calibration curves showed good agreement. Decision curve analysis confirmed the clinical utility of the model.

CONCLUSIONS

The presented radiomics model can be conveniently used to facilitate individualized prediction of the response to methylprednisolone in patients with RN.

Development and validation of an [18F]FDG-PET/CT radiomic model for predicting progression-free survival for patients with stage II - III thoracic esophageal squamous cell carcinoma who are treated with definitive chemoradiotherapy

BACKGROUND

Radiomics is a method for extracting a large amount of information from images and used to predict treatment outcomes, side effects and diagnosis. In this study, we developed and validated a radiomic model of [¹⁸F]FDG-PET/CT for predicting progression-free survival (PFS) of definitive chemoradiotherapy (dCRT) for patients with esophageal cancer.

MATERIAL AND METHODS

Patients with stage II - III esophageal cancer who underwent [¹⁸F]FDG-PET/CT within 45 days before dCRT between 2005 and 2017 were included. Patients were randomly assigned to a training set (85 patients) and a validation set (45 patients). Radiomic parameters inside the area of standard uptake value ≥ 3 were calculated. The open-source software 3D slicer and Pyradiomics were used for segmentation and calculating radiomic parameters, respectively. Eight hundred sixty radiomic parameters and general information were investigated.In the training set, a radiomic model for PFS was made from the LASSO Cox regression model and Rad-score was calculated. In the validation set, the model was applied to Kaplan-Meier curves. The median value of Rad-score in the training set was used as a cutoff value in the validation set. JMP was used for statistical analysis. RStudio was used for the LASSO Cox regression model. p < 0.05 was defined as significant.

RESULTS

The median follow-up periods were 21.9 months for all patients and 63.4 months for survivors. The 5-year PFS rate was 24.0%. In the training set, the LASSO Cox regression model selects 6 parameters and made a model. The low Rad-score group had significantly better PFS than that the high Rad-score group (p = 0.019). In the validation set, the low Rad-score group had significantly better PFS than that the high Rad-score group (p = 0.040).

CONCLUSIONS

The [¹⁸F]FDG-PET/CT radiomic model could predict PFS for patients with esophageal cancer who received dCRT.

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.

Recurrence risk stratification for locally advanced cervical cancer using multi-modality transformer network

Objectives

Recurrence risk evaluation is clinically significant for patients with locally advanced cervical cancer (LACC). We investigated the ability of transformer network in recurrence risk stratification of LACC based on computed tomography (CT) and magnetic resonance (MR) images.

Methods

A total of 104 patients with pathologically diagnosed LACC between July 2017 and December 2021 were enrolled in this study. All patients underwent CT and MR scanning, and their recurrence status was identified by the biopsy. We randomly divided patients into training cohort (48 cases, non-recurrence: recurrence = 37: 11), validation cohort (21 cases, non-recurrence: recurrence = 16: 5), and testing cohort (35 cases, non-recurrence: recurrence = 27: 8), upon which we extracted 1989, 882 and 315 patches for model's development, validation and evaluation, respectively. The transformer network consisted of three modality fusion modules to extract multi-modality and multi-scale information, and a fully-connected module to perform recurrence risk prediction. The model's prediction performance was assessed by six metrics, including the area under the receiver operating characteristic curve (AUC), accuracy, f1-score, sensitivity, specificity and precision. Univariate analysis with F-test and T-test were conducted for statistical analysis.

Results

The proposed transformer network is superior to conventional radiomics methods and other deep learning networks in both training, validation and testing cohorts. Particularly, in testing cohort, the transformer network achieved the highest AUC of 0.819 ± 0.038, while four conventional radiomics methods and two deep learning networks got the AUCs of 0.680 ± 0.050, 0.720 ± 0.068, 0.777 ± 0.048, 0.691 ± 0.103, 0.743 ± 0.022 and 0.733 ± 0.027, respectively.

Conclusions

The multi-modality transformer network showed promising performance in recurrence risk stratification of LACC and may be used as an effective tool to help clinicians make clinical 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.

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.

ACR Appropriateness Criteria® Staging and Follow-Up of Esophageal Cancer

This document provides recommendations regarding the role of imaging in the staging and follow-up of esophageal cancer. For initial clinical staging, locoregional extent and nodal disease are typically assessed with esophagogastroduodenoscopy and esophageal ultrasound. FDG-PET/CT or CT of the chest and abdomen is usually appropriate for use in initial clinical staging as they provide additional information regarding distant nodal and metastatic disease. The detection of metastatic disease is critical in the initial evaluation of patients with esophageal cancer because it will direct patients to a treatment pathway centered on palliative radiation rather than surgery. For imaging during treatment, particularly neoadjuvant chemotherapy, FDG-PET/CT is usually appropriate, because some studies have found that it can provide information regarding primary lesion response, but more importantly it can be used to detect metastases that have developed since the induction of treatment. For patients who have completed treatment, FDG-PET/CT or CT of the chest and abdomen is usually appropriate for evaluating the presence and extent of metastases in patients with no suspected or known recurrence and in those with a suspected or known recurrence. The ACR Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.

Prognostic Value of [18F]-FDG PET/CT Radiomics Combined with Sarcopenia Status among Patients with Advanced Gastroesophageal Cancer

We investigated, whether 18[18F]-FDG PET/CT-derived radiomics combined with sarcopenia measurements improves survival prognostication among patients with advanced, metastatic gastroesophageal cancer. In our study, 128 consecutive patients with advanced, metastatic esophageal and gastroesophageal cancer (n = 128; 26 females; 102 males; mean age 63.5 ± 11.7 years; age range: 29−91 years) undergoing 18[18F]-FDG PET/CT for staging between November 2008 and December 2019 were included. Segmentation of the primary tumor and radiomics analysis derived from PET and CT images was performed semi-automatically with a commonly used open-source software platform (LIFEX, Version 6.30, lifexsoft.org). Patients’ nutritional status was determined by measuring the skeletal muscle index (SMI) at the level of L3 on the CT component. Univariable and multivariable analyses were performed to establish a survival prediction model including radiomics, clinical data, and SMI score. Univariable Cox proportional hazards model revealed ECOG (<0.001) and bone metastasis (p = 0.028) to be significant clinical parameters for overall survival (OS) and progression free survival (PFS). Age (p = 0.017) was an additional prognostic factor for OS. Multivariable analysis showed improved prognostication for overall and progression free survival when adding sarcopenic status, PET and CT radiomics to the model with clinical parameters only. PET and CT radiomics derived from hybrid 18[18F]-FDG PET/CT combined with sarcopenia measurements and clinical parameters may improve survival prediction among patients with advanced, metastatic gastroesophageal cancer.

Non-invasive measurement of tumor immune microenvironment and prediction of survival and chemotherapeutic benefits from 18F fluorodeoxyglucose PET/CT images in gastric cancer

BACKGROUND

The tumor immune microenvironment could provide prognostic and predictive information. It is necessary to develop a noninvasive radiomics-based biomarker of a previously validated tumor immune microenvironment signature of gastric cancer (GC) with immunohistochemistry staining.

METHODS

A total of 230 patients (training (n = 153) or validation (n = 77) cohort) with gastric cancer were subjected to (Positron Emission Tomography-Computed Tomography) radiomics feature extraction (80 features). A radiomics tumor immune microenvironment score (RTIMS) was developed to predict the tumor immune microenvironment signature with LASSO logistic regression. Furthermore, we evaluated its relation with prognosis and chemotherapy benefits.

RESULTS

A 8-feature radiomics signature was established and validated (area under the curve=0.692 and 0.713). The RTIMS signature was significantly associated with disease-free survival and overall survival both in the training and validation cohort (all P<0.001). RTIMS was an independent prognostic factor in the Multivariate analysis. Further analysis revealed that high RTIMS patients benefitted from adjuvant chemotherapy (for DFS, stage II: HR 0.208(95% CI 0.061-0.711), p=0.012; stage III: HR 0.321(0.180-0.570), p<0.001, respectively); while there were no benefits from chemotherapy in a low RTIMS patients.

CONCLUSION

This PET/CT radiomics model provided a promising way to assess the tumor immune microenvironment and to predict clinical outcomes and chemotherapy response. The RTIMS signature could be useful in estimating tumor immune microenvironment and predicting survival and chemotherapy benefit for patients with gastric cancer, when validated by further prospective randomized trials.

Quantitative RECIST derived from multiparametric MRI in evaluating response of esophageal squamous cell carcinoma to neoadjuvant therapy

OBJECTIVE

To develop a quantitative Response Evaluation Criteria in Solid Tumors (qRECIST) for evaluating response to neoadjuvant therapy (nT) in ESCCs relying on multiparametric (mp) MRI.

METHODS

Patients with cT2-T4a/N0-N3/M0 ESCC undergoing pre-nT and post-nT esophageal mpMRI before radical resection were prospectively included. Images were reviewed by two experienced radiologists. qRECIST was redefined using four methods including conventional criterion (cRECIST) and three model-dependent RECIST relying on quantitative MRI measurements at pre-nT, post-nT, and delta pre-post nT, respectively. Pathological tumor regression grades (TRGs) were used as a reference standard. The rates of agreement between four qRECIST methods and TRGs were determined with a Cronbach's alpha test, area under the curve (AUC), and a diagnostic odds ratio meta-analysis.

RESULTS

Ninety-one patients were enrolled. All four methods revealed high inter-reader agreements between the two radiologists, with a Kappa coefficient of 0.96, 0.87, 0.88, and 0.97 for cRECIST, pre-nT RECIST, post-nT RECIST, and delta RECIST, respectively. Among them, delta RECIST achieved the highest overall agreement rate (67.0% [61/91]) with TRGs, followed by post-nT RECIST (63.8% [58/91]), cRECIST (61.5% [56/91]), and pre-nT RECIST (36.3% [33/91]). Especially, delta RECIST achieved the highest accuracy (97.8% [89/91]) in distinguishing responders from non-responders, with 97.3% (34/35) for responders and 98.2% (55/56) for non-responders. Post-nT RECIST achieved the highest accuracy (93.4% [85/91]) in distinguishing complete responders from non-pCRs, with 77.8% (11/18) for pCRs and 94.5% (69/73) for non-pCRs.

CONCLUSION

The qRECIST with mpMRI can assess treatment-induced changes and may be used for early prediction of response to nT in ESCC patients.

KEY POINTS

• Quantitative mpMRI can reliably assess tumor response, and delta RECIST model had the best performance in evaluating response to nT in ESCCs, with an AUC of 0.98, 0.95, 0.80, and 0.82 for predicting TRG0, TRG1, TRG2, and TRG3, respectively. • In distinguishing responders from non-responders, the rate of agreement between delta RECIST and pathology was 97.3% (34/35) for responders and 98.2% (55/56) for non-responders, resulting in an overall agreement rate of 97.8% (89/91). • In distinguishing pCRs from non-pCR, the rate of agreement between MRI and pathology was 77.8% (11/18) for pCRs and 94.5% (69/73) for non- pCRs, resulting in an overall agreement rate of 91.2% (83/91).

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.

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.

Radiomic assessment of oesophageal adenocarcinoma: a critical review of 18F-FDG PET/CT, PET/MRI and CT

Objectives

Radiomic models present an avenue to improve oesophageal adenocarcinoma assessment through quantitative medical image analysis. However, model selection is complicated by the abundance of available predictors and the uncertainty of their relevance and reproducibility. This analysis reviews recent research to facilitate precedent-based model selection for prospective validation studies.

Methods

This analysis reviews research on 18F-FDG PET/CT, PET/MRI and CT radiomics in oesophageal adenocarcinoma between 2016 and 2021. Model design, testing and reporting are evaluated according to the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) score and Radiomics Quality Score (RQS). Key results and limitations are analysed to identify opportunities for future research in the area.

Results

Radiomic models of stage and therapeutic response demonstrated discriminative capacity, though clinical applications require greater sensitivity. Although radiomic models predict survival within institutions, generalisability is limited. Few radiomic features have been recommended independently by multiple studies.

Conclusions

Future research must prioritise prospective validation of previously proposed models to further clinical translation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13244-022-01245-0.

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.

Response Prediction to Concurrent Chemoradiotherapy in Esophageal Squamous Cell Carcinoma Using Delta-Radiomics Based on Sequential Whole-Tumor ADC Map

Purpose

The purpose of this study was to investigate the association between the radiomics features (RFs) extracted from a whole-tumor ADC map during the early treatment course and response to concurrent chemoradiotherapy (cCRT) in patients with esophageal squamous cell carcinoma (ESCC).

Methods

Patients with ESCC who received concurrent chemoradiotherapy were enrolled in two hospitals. Whole-tumor ADC values and RFs were extracted from sequential ADC maps before treatment, after the 5th radiation, and after the 10th radiation, and the changes of ADC values and RFs were calculated as the relative difference between different time points. RFs were selected and further imported to a support vector machine classifier for building a radiomics signature. Radiomics signatures were obtained from both RFs extracted from pretreatment images and three sets of delta-RFs. Prediction models for different responders based on clinical characteristics and radiomics signatures were built up with logistic regression.

Results

Patients (n=76) from hospital 1 were randomly assigned to training (n=53) and internal testing set (n=23) in a ratio of 7 to 3. In addition, to further test the performance of the model, data from another institute (n=17) were assigned to the external testing set. Neither ADC values nor delta-ADC values were correlated with treatment response in the three sets. It showed a predictive effect to treatment response that the AUC values of the radiomics signature built from delta-RFs over the first 2 weeks were 0.824, 0.744, and 0.742 in the training, the internal testing, and the external testing set, respectively. Compared with the evaluated response, the performance of response prediction in the internal testing set was acceptable (p = 0.048).

Conclusions

The ADC map-based delta-RFs during the early course of treatment were effective to predict the response to cCRT in patients with ESCC.

The Predictive Value of Tumor Mutation Burden on Clinical Efficacy of Immune Checkpoint Inhibitors in Melanoma: A Systematic Review and Meta-Analysis

Background: Tumor mutational burden (TMB) is a genomic biomarker that can predict favorable responses to immune checkpoint inhibitors (ICIs). Although we have better understanding of TMB in cancer immunity and cancer immunotherapy, the relationship between TMB and the clinical efficacy of ICIs remains unknown in the treatment of melanoma patients. Here, we conduct a systematic review and meta-analysis to evaluate the predictive value of TMB on the efficacy of ICIs in patients with melanoma.

Methods: We systematically collected data from PubMed, Embase, Cochrane Library, CNKI, China Biomedical Database (CBM), and Wanfang Database. The end date was set to 26 June 2021. We included retrospective studies or clinical trials of ICIs that reported hazard ratios (HRs) for overall survival and/or progression-free survival according to TMB. Data for 1,493 patients from 15 studies were included. In addition, pooled effect size, heterogeneity analysis, sensitivity analysis, publication bias detection, and subgroup analysis were performed based on the included data.

Results: Patients with high TMB showed significantly improved OS (HR = 0.49, 95% CI: 0.33, 0.73; p = 0.001) and PFS (HR = 0.47, 95% CI: 0.33, 0.68; p < 0.001) compared with patients with low TMB. This association was very good in patients treated with monotherapy, that is, anti-CTLA-4 or anti-PD-(L)-1 inhibitors, but not for the patients treated with a combination of the two drugs. The subgroup analysis results showed that heterogeneity was substantial in the targeted next-generation sequencing (NGS) group. Publication bias was detected, and the results were visualized using the funnel chart. And sensitivity analysis and trim-and-fill method analysis showed that our results were stable and reliable.

Conclusion: High TMB is associated with improved OS and PFS in melanoma patients treated with mono-drug ICIs. TMB determined by NGS should be standardized to eliminate heterogeneity. Therefore, the role of TMB in identifying melanoma patients who may benefit from ICI should be further determined in more randomized controlled trials in the future.

Multimodality MRI-based radiomics approach to predict the posttreatment response of lung cancer brain metastases to gamma knife radiosurgery

OBJECTIVES

To develop and validate a multimodality MRI-based radiomics approach to predicting the posttreatment response of lung cancer brain metastases (LCBM) to gamma knife radiosurgery (GKRS).

METHODS

We retrospectively analyzed 213 lesions from 137 patients with LCBM who received GKRS between January 2017 and November 2020. The data were divided into a primary cohort (102 patients with 173 lesions) and an independent validation cohort (35 patients with 40 lesions) according to the time of treatment. Benefit result was defined using pretreatment and 3-month follow-up MRI images based on the Response Assessment in Neuro-Oncology Brain Metastases criteria. Valuable radiomics features were extracted from pretreatment multimodality MRI images using random forests. Prediction performance among the radiomics features of tumor core (RFTC) and radiomics features of peritumoral edema (RFPE) together was evaluated separately. Then, the random forest radiomics score and nomogram were developed through the primary cohort and evaluated through an independent validation cohort. Prediction performance was evaluated by ROC curve, calibration curve, and decision curve.

RESULTS

Gender (p = 0.018), histological subtype (p = 0.009), epidermal growth factor receptor mutation (p = 0.034), and targeted drug treatment (p = 0.021) were significantly associated with posttreatment response. Adding RFPE to RFTC showed improved prediction performance than RFTC alone in primary cohort (AUC = 0.848 versus AUC = 0.750; p < 0.001). Finally, the radiomics nomogram had an AUC of 0.930, a C-index of 0.930 (specificity of 83.1%, sensitivity of 87.3%) in primary cohort, and an AUC of 0.852, a C-index of 0.848 (specificity of 84.2%, sensitivity of 76.2%) in validation cohort.

CONCLUSIONS

Multimodality MRI-based radiomics models can predict the posttreatment response of LCBM to GKRS.

KEY POINTS

• Among the selected radiomics features, texture features basically contributed the dominant force in prediction tasks (80%), especially gray-level co-occurrence matrix features (40%). • Adding RFPE to RFTC showed improved prediction performance than RFTC alone in primary cohort (AUC = 0.848 versus AUC = 0.750; p < 0.001). • The multimodality MRI-based radiomics nomogram showed high accuracy for distinguishing the posttreatment response of LCBM to GKRS (AUC = 0.930, in primary cohort; AUC = 0.852, in validation cohort).

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.

Computed Tomography-Based Radiomics in Predicting T Stage and Length of Esophageal Squamous Cell Carcinoma

Background

Because of the superficial and infiltrative spreading patterns of esophageal squamous cell carcinoma (ESCC), an accurate assessment of tumor extent is challenging using imaging-based clinical staging. Radiomics features extracted from pretreatment computed tomography (CT) or magnetic resonance imaging have shown promise in identifying tumor characteristics. Accurate staging is essential for planning cancer treatment, especially for deciding whether to offer surgery or radiotherapy (chemotherapy) in patients with locally advanced ESCC. Thus, this study aimed to evaluate the predictive potential of contrast-enhanced CT-based radiomics as a non-invasive approach for estimating pathological tumor extent in ESCC patients.

Methods

Patients who underwent esophagectomy between October 2011 and September 2017 were retrospectively studied and included 116 patients with pathologically confirmed ESCC. Contrast-enhanced CT from the neck to the abdomen was performed in all patients during the 2 weeks before the operation. Radiomics features were extracted from segmentations, which were contoured by radiologists. Cluster analysis was performed to obtain clusters with similar radiomics characteristics, and chi-squared tests were used to assess differences in clinicopathological features and survival among clusters. Furthermore, a least absolute shrinkage and selection operator was performed to select radiomics features and construct a radiomics model. Receiver operating characteristic analysis was used to evaluate the predictive ability of the radiomics signatures.

Results

All 116 ESCC patients were divided into two groups according to the cluster analysis. The chi-squared test showed that cluster-based radiomics features were significantly correlated with T stage (p = 0.0254) and tumor length (p = 0.0002). Furthermore, CT radiomics signatures exhibited favorable predictive performance for T stage (area under the curve [AUC] = 0.86, sensitivity = 0.77, and specificity = 0.87) and tumor length (AUC = 0.95, sensitivity = 0.92, and specificity = 0.91).

Conclusions

CT contrast radiomics is a simple and non-invasive method that shows promise for predicting pathological T stage and tumor length preoperatively in ESCC patients and may aid in the accurate assessments of patients in combination with the existing examinations.

Computed tomography-based radiomic analysis for prediction of treatment response to salvage chemoradiotherapy for locoregional lymph node recurrence after curative esophagectomy

Objective

To investigate the capability of computed tomography (CT) radiomic features to predict the therapeutic response and local control of the locoregional recurrence lymph node (LN) after curative esophagectomy by chemoradiotherapy.

Methods

This retrospective study included 129 LN from 77 patients (training cohort: 102 LN from 59 patients; validation cohort: 27 LN from 18 patients) with postoperative esophageal squamous cell carcinoma (ESCC). The region of the tumor was contoured in pretreatment contrast‐enhanced CT images. The least absolute shrinkage and selection operator with logistic regression was used to identify radiomic predictors in the training cohort. Model performance was evaluated using the area under the receiver operating characteristic curves (AUC). The Kaplan–Meier method was used to determine the local recurrence time of cancer.

Results

The radiomic model suggested seven features that could be used to predict treatment response. The AUCs in training and validated cohorts were 0.777 (95% CI: 0.667–0.878) and 0.765 (95% CI: 0.556–0.975), respectively. A significant difference in the radiomic scores (Rad‐scores) between response and nonresponse was observed in the two cohorts (p < 0.001, 0.034, respectively). Two features were identified for classifying whether there will be relapse in 2 years. AUC was 0.857 (95% CI: 0.780–0.935) in the training cohort. The local control time of the high Rad‐score group was higher than the low group in both cohorts (p < 0.001 and 0.025, respectively). As inferred from the Cox regression analysis, the low Rad‐score was a high‐risk factor for local recurrence within 2 years.

Conclusions

The radiomic approach can be used as a potential imaging biomarker to predict treatment response and local control of recurrence LN in ESCC patients.

Three-dimensional MR Elastography Depicts Liver Inflammation, Fibrosis, and Portal Hypertension in Chronic Hepatitis B or C

Background The value of measuring mechanical properties to categorize various pathophysiologic states of the liver is as yet undetermined in chronic hepatitis B (CHB) or C (CHC). Purpose To evaluate multiparametric three-dimensional (3D) MR elastography as a means of detecting early necroinflammation, distinguishing necroinflammation from fibrosis, and gauging the severity of portal hypertension (PH) in CHB or CHC. Materials and Methods From January 2015 to September 2019, participants with CHB or CHC were prospectively enrolled from a single institution and were divided into two groups: those with liver biopsy and no evidence of PH (group 1) and those with PH and a hepatic venous pressure gradient (HVPG) measurement (group 2). For group 3, healthy volunteers were separately recruited from a nearby community. Multiple viscoelastic parameters (shear stiffness [SS], storage modulus, loss modulus, and damping ratio [DR]) were determined at 3D MR elastography at 60 Hz, and multivariable logistic or linear regression analysis was used to assess associations of mechanical parameters with histologic scores and HVPG. Results A total of 155 participants (median age, 41 years [interquartile range, 32-48 years]; 85 women) were in group 1 (training set: n = 78, validation set: n = 77), 85 participants (median age, 57 years [interquartile range, 43-61 years]; 51 women) in group 2, and 60 healthy volunteers (median age, 49 years [interquartile range, 27-64 years]; 38 men) in group 3. The liver DR was higher in participants with necroinflammation (DR, 0.13 ± 0.03) versus those without (at liver fibrosis stage F0) (DR, 0.10 ± 0.02; P < .001). Liver DR and SS together performed well in the diagnosis of necroinflammation (area under the receiver operating characteristic curve [AUC], 0.88 [95% CI: 0.79, 0.96]) and the scoring of moderate to severe activity (AUC, 0.88 [95% CI: 0.81, 0.95]) in the validation data set. Liver DR (regression coefficient [β] = -30.3 [95% CI: -58.0, -2.5]; P = .03) and splenic SS (β = 2.3 [95% CI: 1.7, 2.9]; P < .001) were independently associated with HVPG. Conclusion Three-dimensional MR elastography may detect early necroinflammation, distinguish necroinflammation from liver fibrosis, and correlate with hepatic venous pressure gradient in chronic hepatitis B and C. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Reeder in this issue.

Diffusion-weighted MRI and 18F-FDG PET/CT in assessing the response to neoadjuvant chemoradiotherapy in locally advanced esophageal squamous cell carcinoma

BACKGROUND

Neoadjuvant chemoradiotherapy (nCRT) followed by surgery is a currently widely used strategy for locally advanced esophageal cancer (EC). However, the conventional imaging methods have certain deficiencies in the evaluation and prediction of the efficacy of nCRT. This study aimed to explore the value of functional imaging in predicting the response to neoadjuvant chemoradiotherapy (nCRT) in locally advanced esophageal squamous cell carcinoma (ESCC).

METHODS

Fifty-four patients diagnosed with locally advanced ESCC from August 2017 to September 2019 and treated with nCRT were retrospectively analyzed. DW-MRI scanning was performed before nCRT, at 10-15 fractions of radiotherapy, and 4-6 weeks after the completion of nCRT. ¹⁸F-FDG PET/CT scans were performed before nCRT and 4-6 weeks after the completion of nCRT. These ¹⁸F-FDG PET/CT and DW-MRI parameters and relative changes were compared between patients with pathological complete response (pCR) and non-pCR.

RESULTS

A total of 8 of 54 patients (14.8%) were evaluated as disease progression in the preoperative assessment. The remaining forty-six patients underwent operations, and the pathological assessments of the surgical resection specimens demonstrated pathological complete response (pCR) in 10 patients (21.7%) and complete response of primary tumor (pCR-T) in 16 patients (34.8%). The change of metabolic tumor volume (∆MTV) and change of total lesion glycolysis (∆TLG) were significantly different between patients with pCR and non-pCR. The SUVmax-T_post, MTV-T_post, and TLG-T_post of esophageal tumors in ¹⁸F-FDG PET/CT scans after neoadjuvant chemoradiotherapy and the ∆ SUVmax-T and ∆MTV-T were significantly different between pCR-T versus non-pCR-T patients. The esophageal tumor apparent diffusion coefficient (ADC) increased after nCRT; the ADC_during, ADC_post and ∆ADC_during were significantly different between pCR-T and non-pCR-T groups. ROC analyses showed that the model that combined ADC_during with TLG-T_post had the highest AUC (0.914) for pCR-T prediction, with 90.0% and 86.4% sensitivity and specificity, respectively.

CONCLUSION

¹⁸F-FDG PET/CT is useful for re-staging after nCRT and for surgical decision. Integrating parameters of ¹⁸F-FDG PET/CT and DW-MRI can identify pathological response of primary tumor to nCRT more accurately in ESCC.

Contrast-enhanced CT radiomics features to predict recurrence of locally advanced oesophageal squamous cell cancer within 2 years after trimodal therapy: A case-control study

Radiomics transforms the medical images into high-dimensional quantitative features and provides potential information about tumor phenotypes and heterogeneity. We conducted a retrospective analysis to explore and validate radiomics model based on contrast-enhanced computed tomography (CECT) to predict recurrence of locally advanced oesophageal squamous cell cancer (SCC) within 2 years after trimodal therapy. This study collected CECT and clinical data of consecutive 220 patients with pathology-confirmed locally advanced oesophageal SCC (154 in the training cohort and 66 in the validation cohort). Univariate statistical test and the least absolute shrinkage and selection operator method were performed to select the optimal radiomics features. Logistic regression was conducted to build radiomics model, clinical model, and combined model of both the radiomics and clinical features. Predictive performance was judged by the area under receiver operating characteristics curve (AUC), accuracy, and F1-score in the training and validation cohorts. Ten optimal radiomics features and/or 7 clinical features were selected to build radiomics model, clinical model, and the combined model. The integrated model of radiomics and clinical features was superior to radiomics model or clinical model in predicting recurrence of locally advanced oesophageal SCC within 2 years in the training (AUC: 0.879 vs 0.815 or 0.763; accuracy: 0.844 vs 0.773 or 0.740; and F1-score: 0.886 vs 0.839 or 0.815, respectively) and validation (AUC: 0.857 vs 0.720 or 0.750; accuracy: 0.788 vs 0.700 or 0.697; and F1-score: 0.851 vs 0.800 or 0.787, respectively) cohorts. The combined model of radiomics and clinical features shows better performance than the radiomics or clinical model to predict the recurrence of locally advanced oesophageal SCC within 2 years after trimodal therapy.

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.

A different overview of staging PET/CT images in patients with esophageal cancer: the role of textural analysis with machine learning methods

OBJECTIVE

This study evaluates the ability of several machine learning (ML) algorithms, developed using volumetric and texture data extracted from baseline ¹⁸F-FDG PET/CT studies performed initial staging of patient with esophageal cancer (EC), to predict survival and histopathology.

METHODS

The initial staging ¹⁸F-FDG PET/CT images obtained on newly diagnosed EC patients between January 2008 and June 2019 were evaluated using LIFEx software. A region of interest (ROI) of the primary tumor was created and volumetric and textural features were obtained. A significant relationship between these features and pathological subtypes, 1-year, and 5-year survival was investigated. Due to the nonhomogeneity of the data, nonparametric test (The Mann-Whitney U test) was used for each feature, in pairwise comparisons of independent variables. A p value of < 0.05 was considered significant. Receiver operating curve (ROC) analysis was performed for features with p < 0.05. Correlation between the significant features was evaluated with Spearman correlation test; features with correlation coefficient < 0.8 were evaluated with several ML algorithms.

RESULTS

In predicting survival in a 1-year follow-up J48 was obtained as the most successful algorithm (AUC: 0.581, PRC: 0.565, MCC: 0.258, acc: 64.29%). 5-year survival results were more promising than 1-year survival results with (AUC: 0.820, PRC: 0.860, MCC: 271, acc: 81.36%) by logistic regression. It is revealed that the most successful algorithm was naive bayes (AUC: 0.680 PRC: 0.776, MCC: 0.298, acc: 82.66%) in the histopathological discrimination.

CONCLUSION

Texture analysis with ML algorithms could be predictive of overall survival and discriminating histopathological subtypes of EC.

Application of contrast-enhanced CT radiomics in prediction of early recurrence of locally advanced oesophageal squamous cell carcinoma after trimodal therapy

Background

Early recurrence of oesophageal squamous cell carcinoma (SCC) is defined as recurrence after surgery within 1 year, and appears as local recurrence, distant recurrence, and lymph node positive and disseminated recurrence. Contrast-enhanced computed tomography (CECT) is recommended for diagnosis of primary tumor and initial staging of oesophageal SCC, but it cannot be used to predict early recurrence. It is reported that radiomics can help predict preoperative stages of oesophageal SCC, lymph node metastasis before operation, and 3-year overall survival of oesophageal SCC patients following chemoradiotherapy by extracting high-throughput quantitative features from CT images. This study aimed to develop models based on CT radiomics and clinical features of oesophageal SCC to predict early recurrence of locally advanced cancer.

Methods

We collected electronic medical records and image data of 197 patients with confirmed locally advanced oesophageal SCC. These patients were randomly allocated to 137 patients in the training cohort and 60 in the test cohort. 352 radiomics features were extracted by delineating region-of-interest (ROI) around the lesion on CECT images and clinical signature was generated by medical records. The radiomics model, clinical model, the combined model of radiomics and clinical features were developed by radiomics features and/or clinical characteristics. Predicting performance of the three models was assessed with area under receiver operating characteristic curve (AUC), accuracy and F-1 score.

Results

Eleven radiomics features and/or six clinical signatures were selected to build prediction models related to recurrence of locally advanced oesophageal SCC after trimodal therapy. The AUC of integration of radiomics and clinical models was better than that of radiomics or clinical model for the training cohort (0.821 versus 0.754 or 0.679, respectively) and for the validation cohort (0.809 versus 0.646 or 0.658, respectively). Integrated model of radiomics and clinical features showed good performance in predicting early recurrence of locally advanced oesophageal SCC for both the training and validation cohorts (accuracy = 0.730 and 0.733, and F-1score = 0.730 and 0.778, respectively).

Conclusions

The integrated model of CECT radiomics and clinical features may be a potential imaging biomarker to predict early recurrence of locally advanced oesophageal SCC after trimodal therapy.

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.

Using Genomics Feature Selection Method in Radiomics Pipeline Improves Prognostication Performance in Locally Advanced Esophageal Squamous Cell Carcinoma-A Pilot Study

PURPOSE

To evaluate the prognostic value of baseline and restaging CT-based radiomics with features associated with gene expression in esophageal squamous cell carcinoma (ESCC) patients receiving neoadjuvant chemoradiation (nCRT) plus surgery.

METHODS

We enrolled 106 ESCC patients receiving nCRT from two institutions. Gene expression profiles of 28 patients in the training set were used to detect differentially expressed (DE) genes between patients with and without relapse. Radiomic features that were correlated to DE genes were selected, followed by additional machine learning selection. A radiomic nomogram for disease-free survival (DFS) prediction incorporating the radiomic signature and prognostic clinical characteristics was established for DFS estimation and validated.

RESULTS

The radiomic signature with DE genes feature selection achieved better performance for DFS prediction than without. The nomogram incorporating the radiomic signature and lymph nodal status significantly stratified patients into high and low-risk groups for DFS (p < 0.001). The areas under the curve (AUCs) for predicting 5-year DFS were 0.912 in the training set, 0.852 in the internal test set, 0.769 in the external test set.

CONCLUSIONS

Genomics association was useful for radiomic feature selection. The established radiomic signature was prognostic for DFS. The radiomic nomogram could provide a valuable prediction for individualized long-term survival.

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.

Radiomics Signature Facilitates Organ-Saving Strategy in Patients With Esophageal Squamous Cell Cancer Receiving Neoadjuvant Chemoradiotherapy

After neoadjuvant chemoradiotherapy (NCRT) in locally advanced esophageal squamous cell cancer (ESCC), roughly 40% of the patients may achieve pathologic complete response (pCR). Those patients may benefit from organ-saving strategy if the probability of pCR could be correctly identified before esophagectomy. A reliable approach to predict pathological response allows future studies to investigate individualized treatment plans.

Radiomics Features of 18F-Fluorodeoxyglucose Positron-Emission Tomography as a Novel Prognostic Signature in Colorectal Cancer

The aim of this study was to investigate the prognostic value of radiomics signatures derived from ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) positron-emission tomography (PET) in patients with colorectal cancer (CRC). From April 2008 to Jan 2014, we identified CRC patients who underwent ¹⁸F-FDG-PET before starting any neoadjuvant treatments and surgery. Radiomics features were extracted from the primary lesions identified on ¹⁸F-FDG-PET. Patients were divided into a training and validation set by random sampling. A least absolute shrinkage and selection operator Cox regression model was applied for prognostic signature building with progression-free survival (PFS) using the training set. Using the calculated radiomics score, a nomogram was developed, and its clinical utility was assessed in the validation set. A total of 381 patients with surgically resected CRC patients (training set: 228 vs. validation set: 153) were included. In the training set, a radiomics signature labeled as a rad_score was generated using two PET-derived features, such as gray-level run length matrix long-run emphasis (GLRLM_LRE) and gray-level zone length matrix short-zone low-gray-level emphasis (GLZLM_SZLGE). Patients with a high rad_score in the training and validation set had a shorter PFS. Multivariable analysis revealed that the rad_score was an independent prognostic factor in both training and validation sets. A radiomics nomogram, developed using rad_score, nodal stage, and lymphovascular invasion, showed good performance in the calibration curve and comparable predictive power with the staging system in the validation set. Textural features derived from ¹⁸F-FDG-PET images may enable detailed stratification of prognosis in patients with CRC.

Computed tomography-based deep-learning prediction of neoadjuvant chemoradiotherapy treatment response in esophageal squamous cell carcinoma

BACKGROUND

Deep learning is promising to predict treatment response. We aimed to evaluate and validate the predictive performance of the CT-based model using deep learning features for predicting pathologic complete response to neoadjuvant chemoradiotherapy (nCRT) in esophageal squamous cell carcinoma (ESCC).

MATERIALS AND METHODS

Patients were retrospectively enrolled between April 2007 and December 2018 from two institutions. We extracted deep learning features of six pre-trained convolutional neural networks, respectively, from pretreatment CT images in the training cohort (n = 161). Support vector machine was adopted as the classifier. Validation was performed in an external testing cohort (n = 70). We assessed the performance using the area under the receiver operating characteristics curve (AUC) and selected an optimal model, which was compared with a radiomics model developed from the training cohort. A clinical model consisting of clinical factors only was also built for baseline comparison. We further conducted a radiogenomics analysis using gene expression profiles to reveal underlying biology associated with radiological prediction.

RESULTS

The optimal model with features extracted from ResNet50 achieved an AUC and accuracy of 0.805 (95% CI, 0.696-0.913) and 77.1% (65.6%-86.3%) in the testing cohort, compared with 0.725 (0.605-0.846)) and 67.1% (54.9%-77.9%) for the radiomics model. All the radiological models showed better predictive performance than the clinical model. Radiogenomics analysis suggested a potential association mainly with WNT signaling pathway and tumor microenvironment.

CONCLUSIONS

The novel and noninvasive deep learning approach could provide efficient and accurate prediction of treatment response to nCRT in ESCC, and benefit clinical decision making of therapeutic strategy.

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.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00259-020-05142-w.

Fusobacterium nucleatum confers chemoresistance by modulating autophagy in oesophageal squamous cell carcinoma

Background

Fusobacterium nucleatum (F. nucleatum) is a gut microbe implicated in gastrointestinal tumorigenesis. Predicting the chemotherapeutic response is critical to developing personalised therapeutic strategies for oesophageal cancer patients. The present study investigated the relationship between F. nucleatum and chemotherapeutic resistance in oesophageal squamous cell carcinoma (ESCC).

Methods

We examined the relationship between F. nucleatum and chemotherapy response in 120 ESCC resected specimens and 30 pre-treatment biopsy specimens. In vitro studies using ESCC cell lines and co-culture assays further uncovered the mechanism underlying chemotherapeutic resistance.

Results

ESCC patients with F. nucleatum infection displayed lesser chemotherapeutic response. The infiltration and subsistence of F. nucleatum in the ESCC cells were observed by transmission electron microscopy and laser scanning confocal microscopy. We also observed that F. nucleatum modulates the endogenous LC3 and ATG7 expression, as well as autophagosome formation to induce chemoresistance against 5-FU, CDDP, and Docetaxel. ATG7 knockdown resulted in reversal of F. nucleatum-induced chemoresistance. In addition, immunohistochemical studies confirmed the correlation between F. nucleatum infection and ATG7 expression in 284 ESCC specimens.

Conclusions

F. nucleatum confers chemoresistance to ESCC cells by modulating autophagy. These findings suggest that targeting F. nucleatum, during chemotherapy, could result in variable therapeutic outcomes for ESCC patients.