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

Photon-counting computed tomography in esophageal cancer: correlation of iodine concentration with histopathology and treatment response to neoadjuvant radiochemotherapy

OBJECTIVES

Evaluating esophageal cancer (EC) response to neoadjuvant radiochemotherapy (NARC) has been challenging, but photon-counting CT (PCCT) provides multiparametric data, including iodine concentration (IC), which can be utilized for evaluation. This study explored the relationship between IC and histopathological features of EC, assessing its role in predicting NARC responses.

MATERIALS AND METHODS

Of 105 patients with EC, 85 (67 men; mean age 66.0 ± 11.0 years) met the inclusion criteria and underwent PCCT scans during the portal venous phase. Normalized iodine concentration (NIC) was calculated, and tumor characteristics, including stage, grade, and lymphovascular invasion, were analyzed. Statistical analyses included Mann-Whitney U tests, sensitivity, specificity, and area under the curve (AUC) calculations. Interobserver reliability of NIC measurements was assessed.

RESULTS

Interobserver reliability for NIC was excellent (ICC = 0.99 for all tumors, p < 0.01). In adenocarcinoma, NIC was lower in good therapy responders (Becker 1a/1b: 0.40 ± 0.13) than poor responders (Becker 2/3: 0.51 ± 0.12, p = 0.01). An NIC cutoff ≤ 0.41 predicted good regression (OR = 4.77, p = 0.03; AUC = 0.704, sensitivity = 72.2%, specificity 64.7%). Poor response prediction showed moderate accuracy (AUC = 0.662).

CONCLUSION

NIC values show excellent interobserver agreement and can predict treatment response to NARC in EC, particularly for adenocarcinomas, where lower NIC values are linked to better outcomes. While NIC provides good predictive value, further studies with larger sample sizes are needed to confirm these findings and explore additional factors influencing outcomes.

KEY POINTS

Question Does photon-counting CT-derived iodine concentration correlate with key histopathological features in esophageal cancer and predict response to neoadjuvant radiochemotherapy? Findings Photon-counting CT-derived normalized iodine concentration in esophageal adenocarcinoma moderately predicted treatment response, despite no correlation with tumor grade or Ki-67. Clinical relevance Normalized iodine concentration values from photon-counting CT, obtained during routine staging exams, offer an objective method for predicting treatment response in esophageal adenocarcinoma, enabling more precise therapy planning and personalized patient management.

Multi-modal radiomics features to predict overall survival of locally advanced esophageal cancer after definitive chemoradiotherapy

PURPOSE

To establish prediction models to predict 2-year overall survival (OS) and stratify patients with different risks based on radiomics features extracted from magnetic resonance imaging (MRI) and computed tomography (CT) before definite chemoradiotherapy (dCRT) in locally advanced esophageal squamous cell carcinoma (ESCC).

METHODS

Patients with locally advanced ESCC were recruited. We extracted 547 radiomics features from MRI and CT images. The least absolute shrinkage and selection operator (LASSO) for COX algorithm was used to obtain features highly correlated with survival outcomes in the training cohort. Based on MRI, CT, and the hybrid image data, three prediction models were built. The predictive performance of the radiomics models was evaluated in the training cohort and verified in the validation cohort using AUC values.

RESULTS

A total of 192 patients were included and randomized into the training and validation cohorts. In predicting 2-year OS, the AUCs of the CT-based model were 0.733 and 0.654 for the training and validation sets. The MRI radiomics-based model was observed with similar AUCs of 0.750 and 0.686 in the training and validation sets. The AUC values of hybrid model combining MRI and CT radiomics features in predicting 2-year OS were 0.792 and 0.715 in the training and validation cohorts. It showed significant differences in 2-year OS in the high-risk and low-risk groups divided by the best cutoff value in the hybrid radiomics-based model.

CONCLUSIONS

The hybrid radiomics-based model demontrated the best performance of predicting 2-year OS and can differentiate the high-risk and low-risk patients.

The application of artificial intelligence in upper gastrointestinal cancers

Upper gastrointestinal cancers, mainly comprising esophageal and gastric cancers, are among the most prevalent cancers worldwide. There are many new cases of upper gastrointestinal cancers annually, and the survival rate tends to be low. Therefore, timely screening, precise diagnosis, appropriate treatment strategies, and effective prognosis are crucial for patients with upper gastrointestinal cancers. In recent years, an increasing number of studies suggest that artificial intelligence (AI) technology can effectively address clinical tasks related to upper gastrointestinal cancers. These studies mainly focus on four aspects: screening, diagnosis, treatment, and prognosis. In this review, we focus on the application of AI technology in clinical tasks related to upper gastrointestinal cancers. Firstly, the basic application pipelines of radiomics and deep learning in medical image analysis were introduced. Furthermore, we separately reviewed the application of AI technology in the aforementioned aspects for both esophageal and gastric cancers. Finally, the current limitations and challenges faced in the field of upper gastrointestinal cancers were summarized, and explorations were conducted on the selection of AI algorithms in various scenarios, the popularization of early screening, the clinical applications of AI, and large multimodal models.

Integrating Clinical Variables, Radiomics, and Tumor-derived Cell-Free DNA for Enhanced Prediction of Resectable Esophageal Adenocarcinoma Outcomes

PURPOSE

The value of integrating clinical variables, radiomics, and tumor-derived cell-free DNA (cfDNA) for the prediction of survival and response to chemoradiation of patients with resectable esophageal adenocarcinoma is not yet known. Our aim was to investigate if radiomics and cfDNA metrics combined with clinical variables can improve personalized predictions.

METHODS AND MATERIALS

A cohort of 111 patients with resectable esophageal adenocarcinoma from 2 centers treated with neoadjuvant chemoradiation therapy was used for exploratory retrospective analyses. Models combining the clinical variables of the SOURCE survival model with radiomic features and cfDNA were built using elastic net regression and internally validated using 5-fold cross-validation. Model performance for overall survival (OS) and time to progression (TTP) were evaluated with the C-index and the area under the curve for pathologic complete response.

RESULTS

The best-performing baseline models for OS and TTP were based on the combination of SOURCE-cfDNA that reached a C-index of 0.55 and 0.59 compared with 0.44 to 0.45 with SOURCE alone. The addition of restaging positron emission tomography radiomics to SOURCE was the most promising addition for predicting OS (C-index: 0.65) and TTP (C-index: 0.60). Baseline risk stratification was achieved for OS and TTP by combining SOURCE with radiomics or cfDNA, log-rank P < .01. The best-performing combination model for the prediction of pathologic complete response reached an area under the curve of 0.61 compared with 0.47 with SOURCE variables alone.

CONCLUSIONS

The addition of radiomics and cfDNA can improve the performance of an established survival model. External validity needs to be further assessed in future studies together with the optimization of radiomic pipelines.

Subtype cluster analysis unveiled the correlation between m6A- and cuproptosis-related lncRNAs and the prognosis, immune microenvironment, and treatment sensitivity of esophageal cancer

Objective

Esophageal cancer (EC) is characterized by a high degree of malignancy and poor prognosis. N6-methyladenosine (m6A), a prominent post-transcriptional modification of mRNA in mammalian cells, plays a pivotal role in regulating various cellular and biological processes. Similarly, cuproptosis has garnered attention for its potential implications in cancer biology. This study seeks to elucidate the impact of m6A- and cuproptosis-related long non-coding RNAs (m6aCRLncs) on the prognosis of patients with EC.

Methods

The EC transcriptional data and corresponding clinical information were retrieved from The Cancer Genome Atlas (TCGA) database, comprising 11 normal samples and 159 EC samples. Data on 23 m6A regulators and 25 cuproptosis-related genes were sourced from the latest literature. The m6aCRLncs linked to EC were identified through co-expression analysis. Differentially expressed m6aCRLncs associated with EC prognosis were screened using the limma package in R and univariate Cox regression analysis. Subtype clustering was performed to classify EC patients, enabling the investigation of differences in clinical outcomes and immune microenvironment across patient clusters. A risk prognostic model was constructed using least absolute shrinkage and selection operator (LASSO) regression. Its robustness was evaluated through survival analysis, risk stratification curves, and receiver operating characteristic (ROC) curves. Additionally, the model's applicability across various clinical features and molecular subtypes of EC patients was assessed. To further explore the model's utility in predicting the immune microenvironment, single-sample gene set enrichment analysis (ssGSEA), immune cell infiltration analysis, and immune checkpoint differential expression analysis were conducted. Drug sensitivity analysis was performed to identify potential therapeutic agents for EC. Finally, the mRNA expression levels of m6aCRLncs in EC cell lines were validated using reverse transcription quantitative polymerase chain reaction (RT-qPCR).

Results

We developed a prognostic risk model based on five m6aCRLncs, namely ELF3-AS1, HNF1A-AS1, LINC00942, LINC01389, and MIR181A2HG, to predict survival outcomes and characterize the immune microenvironment in EC patients. Analysis of molecular subtypes and clinical features revealed significant differences in cluster distribution, disease stage, and N stage between high- and low-risk groups. Immune profiling further identified distinct immune cell populations and functional pathways associated with risk scores, including positive correlations with naive B cells, resting CD4+ T cells, and plasma cells, and negative correlations with macrophages M0 and M1. Additionally, we identified key immune checkpoint-related genes with significant differential expression between risk groups, including TNFRSF14, TNFSF15, TNFRSF18, LGALS9, CD44, HHLA2, and CD40. Furthermore, nine candidate drugs with potential therapeutic efficacy in EC were identified: Bleomycin, Cisplatin, Cyclopamine, PLX4720, Erlotinib, Gefitinib, RO.3306, XMD8.85, and WH.4.023. Finally, RT-qPCR validation of the mRNA expression levels of m6aCRLncs in EC cell lines demonstrated that ELF3-AS1 expression was significantly upregulated in the EC cell lines KYSE-30 and KYSE-180 compared to normal esophageal epithelial cells.

Conclusion

This study elucidates the role of m6aCRLncs in shaping the prognostic outcomes and immune microenvironment of EC. Furthermore, it identifies potential therapeutic agents with efficacy against EC. These findings hold significant promise for enhancing the survival of EC patients and provide valuable insights to inform clinical decision-making in the management of this disease.

CT-based habitat radiomics for predicting treatment response to neoadjuvant chemoimmunotherapy in esophageal cancer patients

Introduction

We used habitat radiomics as an innovative tumor biomarker to predict the outcome of neoadjuvant therapy for esophageal cancer.

Methods

This was a two-center retrospective clinical study in which pretreatment CT scans of 112 patients with esophageal cancer treated with neoadjuvant chemoimmunotherapy and surgery between November 2020 and July 2023 were retrospectively collected from two institutions. For training (n = 85) and external testing (n = 27), patients from both institutions were allocated. We employed unsupervised methods to delineate distinct heterogeneous regions within the tumor area.

Results

To represent the prediction effect of different models, we plotted the AUC curves. The AUCs of the habitat models were 0.909 (0.8418-0.9758, 95% CI) and 0.829 (0.6423-1.0000, 95% CI) in the training and external test cohorts, respectively. The AUCs of the nomogram models were 0.914 (0.8483-0.9801, 95% CI) and 0.849 (0.6752-1.0000, 95% CI) in the training and external test cohorts, respectively.

Discussion

The results revealed that the model based on habitat data outperforms traditional radiomic analysis models. In addition, when the model is combined with clinical features, it improves the predictive accuracy of pathological complete response in patients undergoing neoadjuvant chemoimmunotherapy.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Using high-repeatable radiomic features improves the cross-institutional generalization of prognostic model in esophageal squamous cell cancer receiving definitive chemoradiotherapy

OBJECTIVES

Repeatability is crucial for ensuring the generalizability and clinical utility of radiomics-based prognostic models. This study aims to investigate the repeatability of radiomic feature (RF) and its impact on the cross-institutional generalizability of the prognostic model for predicting local recurrence-free survival (LRFS) and overall survival (OS) in esophageal squamous cell cancer (ESCC) receiving definitive (chemo) radiotherapy (dCRT).

METHODS

Nine hundred and twelve patients from two hospitals were included as training and external validation sets, respectively. Image perturbations were applied to contrast-enhanced computed tomography to generate perturbed images. Six thousand five hundred ten RFs from different feature types, bin widths, and filters were extracted from the original and perturbed images separately to evaluate RF repeatability by intraclass correlation coefficient (ICC). The high-repeatable and low-repeatable RF groups grouped by the median ICC were further analyzed separately by feature selection and multivariate Cox proportional hazards regression model for predicting LRFS and OS.

RESULTS

First-order statistical features were more repeatable than texture features (median ICC: 0.70 vs 0.42-0.62). RFs from LoG had better repeatability than that of wavelet (median ICC: 0.70-0.84 vs 0.14-0.64). Features with smaller bin widths had higher repeatability (median ICC of 8-128: 0.65-0.47). For both LRFS and OS, the performance of the models based on high- and low-repeatable RFs remained stable in the training set with similar C-index (LRFS: 0.65 vs 0.67, p = 0.958; OS: 0.64 vs 0.65, p = 0.651), while the performance of the model based on the low-repeatable group was significantly lower than that based on the high-repeatable group in the external validation set (LRFS: 0.61 vs 0.67, p = 0.013; OS: 0.56 vs 0.63, p = 0.013).

CONCLUSIONS

Applying high-repeatable RFs in modeling could safeguard the cross-institutional generalizability of the prognostic model in ESCC.

CRITICAL RELEVANCE STATEMENT

The exploration of repeatable RFs in different diseases and different types of imaging is conducive to promoting the proper use of radiomics in clinical research.

KEY POINTS

The repeatability of RFs impacts the generalizability of the radiomic model. The high-repeatable RFs safeguard the cross-institutional generalizability of the model. Smaller bin width helps improve the repeatability of RFs.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

CT-based delta-radiomics nomogram to predict pathological complete response after neoadjuvant chemoradiotherapy in esophageal squamous cell carcinoma patients

BACKGROUND

This study developed a nomogram model using CT-based delta-radiomics features and clinical factors to predict pathological complete response (pCR) in esophageal squamous cell carcinoma (ESCC) patients receiving neoadjuvant chemoradiotherapy (nCRT).

METHODS

The study retrospectively analyzed 232 ESCC patients who underwent pretreatment and post-treatment CT scans. Patients were divided into training (n = 186) and validation (n = 46) sets through fivefold cross-validation. 837 radiomics features were extracted from regions of interest (ROIs) delineations on CT images before and after nCRT to calculate delta values. The LASSO algorithm selected delta-radiomics features (DRF) based on classification performance. Logistic regression constructed a nomogram incorporating DRFs and clinical factors. Receiver operating characteristic (ROC) and area under the curve (AUC) analyses evaluated nomogram performance for predicting pCR.

RESULTS

No significant differences existed between the training and validation datasets. The 4-feature delta-radiomics signature (DRS) demonstrated good predictive accuracy for pCR, with α-binormal-based and empirical AUCs of 0.871 and 0.869. T-stage (p = 0.001) and differentiation degree (p = 0.018) were independent predictors of pCR. The nomogram combined the DRS and clinical factors improved the classification performance in the training dataset (AUCαbin = 0.933 and AUCemp = 0.941). The validation set showed similar performance with AUCs of 0.958 and 0.962.

CONCLUSIONS

The CT-based delta-radiomics nomogram model with clinical factors provided high predictive accuracy for pCR in ESCC patients after nCRT.

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSION

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

Prediction of pCR based on clinical-radiomic model in patients with locally advanced ESCC treated with neoadjuvant immunotherapy plus chemoradiotherapy

Background

The primary objective of this research is to devise a model to predict the pathologic complete response in esophageal squamous cell carcinoma (ESCC) patients undergoing neoadjuvant immunotherapy combined with chemoradiotherapy (nICRT).

Methods

We retrospectively analyzed data from 60 ESCC patients who received nICRT between 2019 and 2023. These patients were divided into two cohorts: pCR-group (N = 28) and non-pCR group (N = 32). Radiomic features, discerned from the primary tumor region across plain, arterial, and venous phases of CT, and pertinent laboratory data were documented at two intervals: pre-treatment and preoperation. Concurrently, related clinical data was amassed. Feature selection was facilitated using the Extreme Gradient Boosting (XGBoost) algorithm, with model validation conducted via fivefold cross-validation. The model's discriminating capability was evaluated using the area under the receiver operating characteristic curve (AUC). Additionally, the clinical applicability of the clinical-radiomic model was appraised through decision curve analysis (DCA).

Results

The clinical-radiomic model incorporated seven significant markers: postHALP, ΔHB, post-ALB, firstorder_Skewness, GLCM_DifferenceAverage, GLCM_JointEntropy, GLDM_DependenceEntropy, and NGTDM_Complexity, to predict pCR. The XGBoost algorithm rendered an accuracy of 0.87 and an AUC of 0.84. Notably, the joint omics approach superseded the performance of solely radiomic or clinical model. The DCA further cemented the robust clinical utility of our clinical-radiomic model.

Conclusion

This study successfully formulated and validated a union omics methodology for anticipating the therapeutic outcomes of nICRT followed by radical surgical resection. Such insights are invaluable for clinicians in identifying potential nICRT responders among ESCC patients and tailoring optimal individualized treatment plans.

Role of CD44 in Chemotherapy Treatment Outcome: A Scoping Review of Clinical Studies

Cluster of differentiation 44 (CD44), a cell surface adhesion molecule overexpressed in cancer stem cells, has been implicated in chemoresistance. This scoping review, following PRISMA-ScR guidelines, systematically identified and evaluated clinical studies on the impact of CD44 expression on chemotherapy treatment outcomes across various cancer types. The search encompassed PubMed (1985-2023) and SCOPUS (1936-2023) databases, yielding a total of 12,659 articles, of which 40 met the inclusion criteria and were included in the qualitative synthesis using a predefined data extraction table. Data collected included the cancer type, sample size, interventions, control, treatment outcome, study type, expression of CD44 variants and isoforms, and effect of CD44 on chemotherapy outcome. Most of the studies demonstrated an association between increased CD44 expression and negative chemotherapeutic outcomes such as shorter overall survival, increased tumor recurrence, and resistance to chemotherapy, indicating a potential role of CD44 upregulation in chemoresistance in cancer patients. However, a subset of studies also reported non-significant relationships or conflicting results. In summary, this scoping review highlighted the breadth of the available literature investigating the clinical association between CD44 and chemotherapeutic outcomes. Further research is required to elucidate this relationship to aid clinicians in managing CD44-positive cancer patients.

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.

Multi-parametric MRI-based radiomics for preoperative prediction of multiple biological characteristics in endometrial cancer

Purpose

To develop and validate multi-parametric MRI (MP-MRI)-based radiomics models for the prediction of biological characteristics in endometrial cancer (EC).

Methods

A total of 292 patients with EC were divided into LVSI (n = 208), DMI (n = 292), MSI (n = 95), and Her-2 (n = 198) subsets. Total 2316 radiomics features were extracted from MP-MRI (T2WI, DWI, and ADC) images, and clinical factors (age, FIGO stage, differentiation degree, pathological type, menopausal state, and irregular vaginal bleeding) were included. Intra-class correlation coefficient (ICC), spearman's rank correlation test, univariate logistic regression, and least absolute shrinkage and selection operator (LASSO) were used to select radiomics features; univariate and multivariate logistic regression were used to identify clinical independent risk factors. Five classifiers were applied (logistic regression, random forest, decision tree, K-nearest neighbor, and Bayes) to construct radiomics models for predicting biological characteristics. The clinical model was built based on the clinical independent risk factors. The combined model incorporating the radiomics score (radscore) and the clinical independent risk factors was constructed. The model was evaluated by ROC curve, calibration curve (H-L test), and decision curve analysis (DCA).

Results

In the training cohort, the RF radiomics model performed best among the five classifiers for the three subsets (MSI, LVSI, and DMI) according to AUC values (AUCMSI: 0.844; AUCLVSI: 0.952; AUCDMI: 0.840) except for Her-2 subset (Decision tree: AUC=0.714), and the combined model had higher AUC than the clinical model in each subset (MSI: AUCcombined =0.907, AUCclinical =0.755; LVSI: AUCcombined =0.959, AUCclinical =0.835; DMI: AUCcombined = 0.883, AUCclinical =0.796; Her-2: AUCcombined =0.812, AUCclinical =0.717; all P<0.05). Nevertheless, in the validation cohort, significant differences between the two models (combined vs. clinical model) were found only in the DMI and LVSI subsets (DMI: AUCcombined =0.803, AUCclinical =0.698; LVSI: AUCcombined =0.926, AUCclinical =0.796; all P<0.05).

Conclusion

The radiomics analysis based on MP-MRI and clinical independent risk factors can potentially predict multiple biological features of EC, including DMI, LVSI, MSI, and Her-2, and provide valuable guidance for clinical decision-making.

Nuclear medicine radiomics in digestive system tumors: Concept, applications, challenges, and future perspectives

Radiomics aims to develop novel biomarkers and provide relevant deeper subvisual information about pathology, immunophenotype, and tumor microenvironment. It uses automated or semiautomated quantitative analysis of high-dimensional images to improve characterization, diagnosis, and prognosis. Recent years have seen a rapid increase in radiomics applications in nuclear medicine, leading to some promising research results in digestive system oncology, which have been driven by big data analysis and the development of artificial intelligence. Although radiomics advances one step further toward the non-invasive precision medical analysis, it is still a step away from clinical application and faces many challenges. This review article summarizes the available literature on digestive system tumors regarding radiomics in nuclear medicine. First, we describe the workflow and steps involved in radiomics analysis. Subsequently, we discuss the progress in clinical application regarding the utilization of radiomics for distinguishing between various diseases and evaluating their prognosis, and demonstrate how radiomics advances this field. Finally, we offer our viewpoint on how the field can progress by addressing the challenges facing clinical implementation.

A radiomics nomogram based on 18 F-FDG PET/CT and clinical risk factors for the prediction of peritoneal metastasis in gastric cancer

PURPOSE

Peritoneal metastasis (PM) is usually considered an incurable factor of gastric cancer (GC) and not fit for surgery. The aim of this study is to develop and validate an 18 F-FDG PET/CT-derived radiomics model combining with clinical risk factors for predicting PM of GC.

METHOD

In this retrospective study, 410 GC patients (PM - = 281, PM + = 129) who underwent preoperative 18 F-FDG PET/CT images from January 2015 to October 2021 were analyzed. The patients were randomly divided into a training cohort (n = 288) and a validation cohort (n = 122). The maximum relevance and minimum redundancy (mRMR) and the least shrinkage and selection operator method were applied to select feature. Multivariable logistic regression analysis was preformed to develop the predicting model. Discrimination, calibration, and clinical usefulness were used to evaluate the performance of the nomogram.

RESULT

Fourteen radiomics feature parameters were selected to construct radiomics model. The area under the curve (AUC) of the radiomics model were 0.86 [95% confidence interval (CI), 0.81-0.90] in the training cohort and 0.85 (95% CI, 0.78-0.92) in the validation cohort. After multivariable logistic regression, peritoneal effusion, mean standardized uptake value (SUVmean), carbohydrate antigen 125 (CA125) and radiomics signature showed statistically significant differences between different PM status patients( P  < 0.05). They were chosen to construct the comprehensive predicting model which showed a performance with an AUC of 0.92 (95% CI, 0.89-0.95) in the training cohort and 0.92 (95% CI, 0.86-0.98) in the validation cohort, respectively.

CONCLUSION

The nomogram based on 18 F-FDG PET/CT radiomics features and clinical risk factors can be potentially applied in individualized treatment strategy-making for GC patients before the surgery.

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

BACKGROUND

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

MATERIALS AND METHODS

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

RESULTS

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

CONCLUSIONS

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

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

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

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.

Exploring biomarkers for prognosis and neoadjuvant chemosensitivity in rectal cancer: Multi-omics and ctDNA sequencing collaboration

Introduction

This study aimed to identified the key genes and sequencing metrics for predicting prognosis and efficacy of neoadjuvant chemotherapy (nCT) in rectal cancer (RC) based on genomic DNA sequencing in samples with different origin and multi-omics association database.

Methods

We collected 16 RC patients and obtained DNA sequencing data from cancer tissues and plasma cell-free DNA before and after nCT. Various gene variations were analyzed, including single nucleotide variants (SNV), copy number variation (CNV), tumor mutation burden (TMB), copy number instability (CNI) and mutant-allele tumor heterogeneity (MATH). We also identified genes by which CNV level can differentiate the response to nCT. The Cancer Genome Atlas database and the Clinical Proteomic Tumor Analysis Consortium database were used to further evaluate the specific role of therapeutic relevant genes and screen out the key genes in multi-omics levels. After the intersection of the screened genes from differential expression analysis, survival analysis and principal components analysis dimensionality reduction cluster analysis, the key genes were finally identified.

Results

The genes CNV level of principal component genes in baseline blood and cancer tissues could significantly distinguish the two groups of patients. The CNV of HSP90AA1, EGFR, SRC, MTOR, etc. were relatively gained in the better group compared with the poor group in baseline blood. The CNI and TMB was significantly different between the two groups. The increased expression of HSP90AA1, EGFR, and SRC was associated with increased sensitivity to multiple chemotherapeutic drugs. The nCT predictive score obtained by therapeutic relevant genes could be a potential prognostic indicator, and the combination with TMB could further refine prognostic prediction for patients. After a series of analysis in multi-omics association database, EGFR and HSP90AA1 with significant differences in multiple aspects were identified as the key predictive genes related to prognosis and the sensitivity of nCT.

Discussion

This work revealed that effective combined application and analysis in multi-omics data are critical to search for predictive biomarkers. The key genes EGFR and HSP90AA1 could serve as an effective biomarker to predict prognose and neoadjuvant chemosensitivity.

Enhanced CT-based radiomics predicts pathological complete response after neoadjuvant chemotherapy for advanced adenocarcinoma of the esophagogastric junction: a two-center study

Purpose

This study aimed to develop and validate CT-based models to predict pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) for advanced adenocarcinoma of the esophagogastric junction (AEG).

Methods

Pre-NAC clinical and imaging data of AEG patients who underwent surgical resection after preoperative-NAC at two centers were retrospectively collected from November 2014 to September 2020. The dataset included training (n = 60) and external validation groups (n = 32). Three models, including CT-based radiomics, clinical and radiomics–clinical combined models, were established to differentiate pCR (tumor regression grade (TRG) = grade 0) and nonpCR (TRG = grade 1–3) patients. For the radiomics model, tumor-region-based radiomics features in the arterial and venous phases were extracted and selected. The naïve Bayes classifier was used to establish arterial- and venous-phase radiomics models. The selected candidate clinical factors were used to establish a clinical model, which was further incorporated into the radiomics–clinical combined model. ROC analysis, calibration and decision curves were used to assess the model performance.

Results

For the radiomics model, the AUC values obtained using the venous data were higher than those obtained using the arterial data (training: 0.751 vs. 0.736; validation: 0.768 vs. 0.750). Borrmann typing, tumor thickness and degree of differentiation were utilized to establish the clinical model (AUC-training: 0.753; AUC-validation: 0.848). The combination of arterial- and venous-phase radiomics and clinical factors further improved the discriminatory performance of the model (AUC-training: 0.838; AUC-validation: 0.902). The decision curve reflects the higher net benefit of the combined model.

Conclusion

The combination of CT imaging and clinical factors pre-NAC for advanced AEG could help stratify potential responsiveness to NAC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13244-022-01273-w.

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

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

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.

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.

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSION

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

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

INTRODUCTION

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

MATERIALS AND METHODS

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

RESULTS

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

CONCLUSIONS

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

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

BACKGROUND

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

PATIENTS AND METHODS

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

RESULTS

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

CONCLUSION

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