CT-based radiomics scores predict response to neoadjuvant chemotherapy and survival in patients with gastric cancer

Computed tomography-based radiomic model for the prediction of neoadjuvant immunochemotherapy response in patients with advanced gastric cancer

BACKGROUND

Neoadjuvant immunochemotherapy (nICT) has emerged as a popular treatment approach for advanced gastric cancer (AGC) in clinical practice worldwide. However, the response of AGC patients to nICT displays significant heterogeneity, and no existing radiomic model utilizes baseline computed tomography to predict treatment outcomes.

AIM

To establish a radiomic model to predict the response of AGC patients to nICT.

METHODS

Patients with AGC who received nICT (n = 60) were randomly assigned to a training cohort (n = 42) or a test cohort (n = 18). Various machine learning models were developed using selected radiomic features and clinical risk factors to predict the response of AGC patients to nICT. An individual radiomic nomogram was established based on the chosen radiomic signature and clinical signature. The performance of all the models was assessed through receiver operating characteristic curve analysis, decision curve analysis (DCA) and the Hosmer-Lemeshow goodness-of-fit test.

RESULTS

The radiomic nomogram could accurately predict the response of AGC patients to nICT. In the test cohort, the area under curve was 0.893, with a 95% confidence interval of 0.803-0.991. DCA indicated that the clinical application of the radiomic nomogram yielded greater net benefit than alternative models.

CONCLUSION

A nomogram combining a radiomic signature and a clinical signature was designed to predict the efficacy of nICT in patients with AGC. This tool can assist clinicians in treatment-related decision-making.

Diagnostic accuracy of radiomics-based machine learning for neoadjuvant chemotherapy response and survival prediction in gastric cancer patients: A systematic review and meta-analysis

BACKGROUND

In recent years, researchers have explored the use of radiomics to predict neoadjuvant chemotherapy outcomes in gastric cancer (GC). Yet, a lingering debate persists regarding the accuracy of these predictions. Against this backdrop, this study was conducted to examine the accuracy of radiomics in predicting the response to neoadjuvant chemotherapy in GC patients.

METHODS

An exhaustive search of relevant studies was conducted in PubMed, Cochrane, Embase, and Web of Science databases up to February 21, 2023. The radiomics quality scoring (RQS) tool was employed to assess study quality. Tumor response to neoadjuvant chemotherapy and survival outcomes were examined as outcome measures.

RESULTS

Fourteen studies involving 3,373 GC patients who had received neoadjuvant chemotherapy were incorporated in our meta-analysis. The mean RQS score across all studies was 36.3%, ranging between 0 and 63.9%. On the validation cohort, when the modeling variables were restricted to radiomic features alone, the predictive performance was characterized by a c-index of 0.750 (95% CI: 0.710-0.790), with a sensitivity of 0.67 (95% CI: 0.58-0.75) and a specificity of 0.77 (95% CI: 0.69-0.84) for the prediction of neoadjuvant chemotherapy response. When clinical data was integrated with radiomic features as modeling variables, the predictive performance improved, yielding a c-index of 0.814 (95% CI: 0.780-0.847), a sensitivity of 0.78 [95% CI: 0.70-0.84], and a specificity of 0.73 [95% CI: 0.67-0.79].

CONCLUSIONS

Radiomics holds promise to noninvasively predict neoadjuvant chemotherapy response and survival outcomes among patients with locally advanced GC. Additionally, we underscore the need for future multicenter studies and the development of imaging-sourced tools for risk stratification in this patient population.

Deep learning or radiomics based on CT for predicting the response of gastric cancer to neoadjuvant chemotherapy: a meta-analysis and systematic review

Background

Artificial intelligence (AI) models, clinical models (CM), and the integrated model (IM) are utilized to evaluate the response to neoadjuvant chemotherapy (NACT) in patients diagnosed with gastric cancer.

Objective

The objective is to identify the diagnostic test of the AI model and to compare the accuracy of AI, CM, and IM through a comprehensive summary of head-to-head comparative studies.

Methods

PubMed, Web of Science, Cochrane Library, and Embase were systematically searched until September 5, 2023, to compile English language studies without regional restrictions. The quality of the included studies was evaluated using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) criteria. Forest plots were utilized to illustrate the findings of diagnostic accuracy, while Hierarchical Summary Receiver Operating Characteristic curves were generated to estimate sensitivity (SEN) and specificity (SPE). Meta-regression was applied to analyze heterogeneity across the studies. To assess the presence of publication bias, Deeks' funnel plot and an asymmetry test were employed.

Results

A total of 9 studies, comprising 3313 patients, were included for the AI model, with 7 head-to-head comparative studies involving 2699 patients. Across the 9 studies, the pooled SEN for the AI model was 0.75 (95% confidence interval (CI): 0.66, 0.82), and SPE was 0.77 (95% CI: 0.69, 0.84). Meta-regression was conducted, revealing that the cut-off value, approach to predicting response, and gold standard might be sources of heterogeneity. In the head-to-head comparative studies, the pooled SEN for AI was 0.77 (95% CI: 0.69, 0.84) with SPE at 0.79 (95% CI: 0.70, 0.85). For CM, the pooled SEN was 0.67 (95% CI: 0.57, 0.77) with SPE at 0.59 (95% CI: 0.54, 0.64), while for IM, the pooled SEN was 0.83 (95% CI: 0.79, 0.86) with SPE at 0.69 (95% CI: 0.56, 0.79). Notably, there was no statistical difference, except that IM exhibited higher SEN than AI, while maintaining a similar level of SPE in pairwise comparisons. In the Receiver Operating Characteristic analysis subgroup, the CT-based Deep Learning (DL) subgroup, and the National Comprehensive Cancer Network (NCCN) guideline subgroup, the AI model exhibited higher SEN but lower SPE compared to the IM. Conversely, in the training cohort subgroup and the internal validation cohort subgroup, the AI model demonstrated lower SEN but higher SPE than the IM. The subgroup analysis underscored that factors such as the number of cohorts, cohort type, cut-off value, approach to predicting response, and choice of gold standard could impact the reliability and robustness of the results.

Conclusion

AI has demonstrated its viability as a tool for predicting the response of GC patients to NACT Furthermore, CT-based DL model in AI was sensitive to extract tumor features and predict the response. The results of subgroup analysis also supported the above conclusions. Large-scale rigorously designed diagnostic accuracy studies and head-to-head comparative studies are anticipated.

Systematic review registration

PROSPERO, CRD42022377030.

A multi-institutional study to predict the benefits of DEB-TACE and molecular targeted agent sequential therapy in unresectable hepatocellular carcinoma using a radiological-clinical nomogram

OBJECTIVE

Exploring the efficacy of a Radiological-Clinical (Rad-Clinical) model in predicting prognosis of unresectable hepatocellular carcinoma (HCC) patients after drug eluting beads transcatheter arterial chemoembolization (DEB-TACE) to optimize the targeted sequential treatment.

METHODS

In this retrospective analysis, we included 202 patients with unresectable HCC who received DEB-TACE treatment in 17 institutions from June 2018 to December 2022. Progression-free survival (PFS)-related radiomics features were computationally extracted from HCC patients to build a radiological signature (Rad-signature) model with least absolute shrinkage and selection operator regression. A Rad-Clinical model for postoperative PFS was further constructed according to the Rad-signature and clinical variables by Cox regression analysis. It was presented as a nomogram and evaluated by receiver operating characteristic curves, calibration curves, and decision curve analysis. And further evaluate the application value of Rad-Clinical model in clinical stages and targeted sequential therapy of HCC.

RESULTS

Tumor size, Barcelona Clinic Liver Cancer (BCLC) stage, and radiomics score (Rad-score) were found to be independent risk factors for PFS after DEB-TACE treatment for unresectable HCC, with the Rad-Clinical model being the greatest predictor of PFS in these patients (hazard ratio: 2.08; 95% confidence interval: 1.56-2.78; P < 0.001) along with high 6 months, 12 months, 18 months, and 24 months area under the curves of 0.857, 0.810, 0.843, and 0.838, respectively. In addition, compared to the radiomics and clinical nomograms, the Radiological-Clinical nomogram also significantly improved the classification accuracy for PFS outcomes, based on the net reclassification improvement (45.2%, 95% CI 0.260-0.632, p < 0.05) and integrated discrimination improvement (14.9%, 95% CI 0.064-0.281, p < 0.05). Based on this model, low-risk patients had higher PFS than high-risk patients in BCLC-B and C stages (P = 0.021). Targeted sequential therapy for patients with high and low-risk HCC in BCLC-B stage exhibited significant benefits (P = 0.018, P = 0.012), but patients with high-risk HCC in BCLC-C stage did not benefit much (P = 0.052).

CONCLUSION

The Rad-Clinical model may be favorable for predicting PFS in patients with unresectable HCC treated with DEB-TACE and for identifying patients who may benefit from targeted sequential therapy.

Imaging advances in efficacy assessment of gastric cancer neoadjuvant chemotherapy

Effective neoadjuvant chemotherapy (NAC) can improve the survival of patients with locally progressive gastric cancer, but chemotherapeutics do not always exhibit good efficacy in all patients. Therefore, accurate preoperative evaluation of the effect of neoadjuvant therapy and the appropriate selection of surgery time to minimize toxicity and complications while prolonging patient survival are key issues that need to be addressed. This paper reviews the role of three imaging methods, morphological, functional, radiomics, and artificial intelligence (AI)-based imaging, in evaluating NAC pathological reactions for gastric cancer. In addition, the advantages and disadvantages of each method and the future application prospects are discussed.

Artificial intelligence applications in computed tomography in gastric cancer: a narrative review

Background and Objective

Artificial intelligence (AI) is a revolutionary technique which is deeply impacting and reshaping clinical practice in oncology. This review aims to summarize the current status of the clinical application of AI-based computed tomography (CT) for gastric cancer (GC), focusing on diagnosis, genetic status detection and risk prediction of metastasis, prognosis and treatment efficacy. The challenges and prospects for future research will also be discussed.

Methods

We searched the PubMed/MEDLINE database to identify clinical studies published between 1990 and November 2022 that investigated AI applications in CT in GC. The major findings of the verified studies were summarized.

Key Content and Findings

AI applications in CT images have attracted considerable attention in various fields such as diagnosis, prediction of metastasis risk, survival, and treatment response. These emerging techniques have shown a high potential to outperform clinicians in diagnostic accuracy and time-saving.

Conclusions

AI-powered tools showed great potential to increase diagnostic accuracy and reduce radiologists' workload. However, the goal of AI is not to replace human ability but to help oncologists make decisions in their practice. Therefore, radiologists should play a predominant role in AI applications and decide the best ways to integrate these complementary techniques within clinical practice.

Methodological quality of radiomic-based prognostic studies in gastric cancer: a cross-sectional study

Background

Machine learning radiomics models are increasingly being used to predict gastric cancer prognoses. However, the methodological quality of these models has not been evaluated. Therefore, this study aimed to evaluate the methodological quality of radiomics studies in predicting the prognosis of gastric cancer, summarize their methodological characteristics and performance.

Methods

The PubMed and Embase databases were searched for radiomics studies used to predict the prognosis of gastric cancer published in last 5 years. The characteristics of the studies and the performance of the models were extracted from the eligible full texts. The methodological quality, reporting completeness and risk of bias of the included studies were evaluated using the RQS, TRIPOD and PROBAST. The discrimination ability scores of the models were also compared.

Results

Out of 283 identified records, 22 studies met the inclusion criteria. The study endpoints included survival time, treatment response, and recurrence, with reported discriminations ranging between 0.610 and 0.878 in the validation dataset. The mean overall RQS value was 15.32 ± 3.20 (range: 9 to 21). The mean adhered items of the 35 item of TRIPOD checklist was 20.45 ± 1.83. The PROBAST showed all included studies were at high risk of bias.

Conclusion

The current methodological quality of gastric cancer radiomics studies is insufficient. Large and reasonable sample, prospective, multicenter and rigorously designed studies are required to improve the quality of radiomics models for gastric cancer prediction.

Study registration

This protocol was prospectively registered in the Open Science Framework Registry (https://osf.io/ja52b).

CT-Based Radiomic Score: A Risk Stratifier in Far-Advanced Gastric Cancer Patients

OBJECTIVES

To prolong the survival, the value of a computed tomography-based radiomic score (RS) in stratifying survival and guiding personalized chemotherapy strategies in far-advanced gastric cancer (FGC) was investigated.

MATERIALS AND METHODS

This retrospective multicenter study enrolled 283 FGC patients (cT4a/bNxM0-1) from three centers. Patients from one center were randomly divided into the training (n = 166) and internal validation (n = 83) cohorts, whereas the external validation cohort (n = 34) consisted of patients from the two other centers. The RS was calculated for each patient to predict progression-free survival (PFS). Features from the primary tumor and main metastasis (peritoneum, liver, and lymph node) were integrated in the training cohort and then validated for its ability to stratify PFS and overall survival (OS) in the validation cohort. The association between the RS and efficacy of neoadjuvant intraperitoneal and systemic (NIPS) therapy was also explored.

RESULTS

The RS demonstrated a favorable prognostic ability to predict PFS in all cohorts (training: C-index 0.83, 95% confidence interval [CI]: 0.788-0.872; internal validation: C-index 0.75, 95% CI: 0.682-0.818; external validation: C-index 0.76, 95% CI: 0.669-0.851; all p < 0.05), as well as an excellent ability to stratify the PFS and OS in both the whole population and metastatic subgroups (p < 0.05). Patients with a low score were more likely to undergo surgery after perioperative chemotherapy (p < 0.05). Furthermore, only high-scoring patients with peritoneal metastasis benefited from NIPS.

CONCLUSION

The RS may be an effective risk stratifier for the outcomes of FGC patients and may be used to select patients who can benefit from NIPS therapy.

Comparison of MRI and CT-Based Radiomics and Their Combination for Early Identification of Pathological Response to Neoadjuvant Chemotherapy in Locally Advanced Gastric Cancer

BACKGROUND

Current radiomics for treatment response assessment in gastric cancer (GC) have focused solely on Computed tomography (CT). The importance of multi-parametric magnetic resonance imaging (mp-MRI) radiomics in GC is less clear.

PURPOSE

To compare and combine CT and mp-MRI radiomics for pretreatment identification of pathological response to neoadjuvant chemotherapy in GC.

STUDY TYPE

Retrospective.

POPULATION

Two hundred twenty-five GC patients were recruited and split into training (157) and validation dataset (68) in the ratio of 7:3 randomly.

FIELD/SEQUENCE

T2-weighted fast spin echo (fat suppressed T2-weighted imaging [fs-T2WI]), diffusion weighted echo planar imaging (DWI), and fast gradient echo (dynamic contrast enhanced [DCE]) sequences at 3.0T.

ASSESSMENT

Apparent diffusion coefficient (ADC) maps were generated from DWI. CT, fs-T2WI, ADC, DCE, and mp-MRI Radiomics score (Radscores) were compared between responders and non-responders. A multimodal nomogram combining CT and mp-MRI Radscores was developed. Patients were followed up for 3-65 months (median 19) after surgery, the overall survival (OS) and progression free survival (PFS) were calculated.

STATISTICAL TESTS

A logistic regression classifier was applied to construct the five models. Each model's performance was evaluated using a receiver operating characteristic curve. The association of the nomogram with OS/PFS was evaluated by Kaplan-Meier survival analysis and C-index. A P value <0.05 was considered statistically significant.

RESULTS

CT Radscore, mp-MRI Radscore and nomogram were significantly associated with tumor regression grading. The nomogram achieved the highest area under the curves (AUCs) of 0.893 (0.834-0.937) and 0.871 (0.767-0.940) in training and validation datasets, respectively. The C-index was 0.589 for OS and 0.601 for PFS. The AUCs of the mp-MRI model were not significantly different to that of the CT model in training (0.831 vs. 0.770, P = 0.267) and validation dataset (0.797 vs. 0.746, P = 0.137).

DATA CONCLUSIONS

mp-MRI radiomics provides similar results to CT radiomics for early identification of pathologic response to neoadjuvant chemotherapy. The multimodal radiomics nomogram further improved the capability.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: 2.

Evaluation the benefits of additional radiotherapy for gastric cancer patients after D2 resection using CT based radiomics

OBJECTIVES

The value of adding radiotherapy (RT) is still unclear for patients with gastric cancer (GC) after D2 lymphadenectomy. The purpose of this study is to predict and compare the overall survival (OS) and disease-free survival (DFS) of GC patients treated by chemotherapy and chemoradiation based on contrast-enhanced CT (CECT) radiomics.

METHODS

A total of 154 patients treated by chemotherapy and chemoradiation in authors' hospital were retrospectively reviewed and randomly divided into the training and testing cohorts (7:3). Radiomics features were extracted from contoured tumor volumes in CECT using the pyradiomics software. Radiomics score and nomogram with integrated clinical factors were developed to predict the OS and DFS and evaluated with Harrell's Consistency Index (C-index).

RESULTS

Radiomics score achieved a C index of 0.721(95%CI: 0.681-0.761) and 0.774 (95%CI: 0.738-0.810) in the prediction of DFS and OS for GC patients treated by chemotherapy and chemoradiation, respectively. The benefits of additional RT only demonstrated in subgroup of GC patients with Lauren intestinal type and perineural invasion (PNI). Integrating clinical factors further improved the prediction ability of radiomics models with a C-index of 0.773 (95%CI: 0.736-0.810) and 0.802 (95%CI: 0.765-0.839) for DFS and OS, respectively.

CONCLUSIONS

CECT based radiomics is feasible to predict the OS and DFS for GC patients underwent chemotherapy and chemoradiation after D2 resection. The benefits of additional RT only observed in GC patients with intestinal cancer and PNI.

A Focus on the Synergy of Radiomics and RNA Sequencing in Breast Cancer

Radiological imaging is currently employed as the most effective technique for screening, diagnosis, and follow up of patients with breast cancer (BC), the most common type of tumor in women worldwide. However, the introduction of the omics sciences such as metabolomics, proteomics, and molecular genomics, have optimized the therapeutic path for patients and implementing novel information parallel to the mutational asset targetable by specific clinical treatments. Parallel to the "omics" clusters, radiological imaging has been gradually employed to generate a specific omics cluster termed "radiomics". Radiomics is a novel advanced approach to imaging, extracting quantitative, and ideally, reproducible data from radiological images using sophisticated mathematical analysis, including disease-specific patterns, that could not be detected by the human eye. Along with radiomics, radiogenomics, defined as the integration of "radiology" and "genomics", is an emerging field exploring the relationship between specific features extracted from radiological images and genetic or molecular traits of a particular disease to construct adequate predictive models. Accordingly, radiological characteristics of the tissue are supposed to mimic a defined genotype and phenotype and to better explore the heterogeneity and the dynamic evolution of the tumor over the time. Despite such improvements, we are still far from achieving approved and standardized protocols in clinical practice. Nevertheless, what can we learn by this emerging multidisciplinary clinical approach? This minireview provides a focused overview on the significance of radiomics integrated by RNA sequencing in BC. We will also discuss advances and future challenges of such radiomics-based approach.

Multiparametric MRI-based radiomics nomogram for early prediction of pathological response to neoadjuvant chemotherapy in locally advanced gastric cancer

OBJECTIVES

To build and validate a multi-parametric MRI (mpMRI)-based radiomics nomogram for early prediction of treatment response to neoadjuvant chemotherapy (NAC) in locally advanced gastric cancer.

METHODS

Baseline MRI were retrospectively enrolled from 141 patients with gastric adenocarcinoma who received NAC followed by radical gastrectomy. According to pathologic response of tumor regression grading (TRG), patients were labeled as responders (TRG = 0 + 1) and non-responders (TRG = 2 + 3) and further divided into a training (n = 85) and validation dataset (n = 56). Radiomics score (Radscore) were built from T2WI, ADC, and venous phase of dynamic-contrasted-enhanced MR imaging. Clinical information, laboratory indicators, MRI parameters, and follow-up data were also recorded. According to multivariable regression analysis, an mpMRI radiomics nomogram was built and its predictive ability was evaluated by ROC analysis. Decision curve analysis was applied to evaluate the clinical usefulness. Kaplan-Meier survival curves based on the nomogram were used to estimate the progression-free survival (PFS) and overall survival (OS) in the validation dataset.

RESULTS

Both single sequence-based Radscores and mpMRI radiomics nomogram were associated with pathologic response (p < 0.001). The nomogram achieved the highest diagnostic ability with area under ROC curve of 0.844 (95% CI, 0.749-0.914) and 0.820 (95% CI, 0.695-0.910) in the training and validation datasets. The hazard ratio of the nomogram for PFS and OS prediction was 2.597 (95% CI: 1.046-6.451, log-rank p = 0.023) and 2.570 (95% CI: 1.166-5.666, log-rank p = 0.011).

CONCLUSIONS

The mpMRI-based radiomics nomogram showed preferable performance in predicting pathologic response to NAC in LAGC.

KEY POINTS

• This study investigated the value of multi-parametric MRI-based radiomics in predicting pathologic response to neoadjuvant chemotherapy in locally advanced gastric cancer. • The nomogram incorporating T2WI Radscore, ADC Radscore, and DCE Radscore as well as Borrmann classification outperformed the single sequence-based Radscore. • The nomogram also exhibited a promising prognostic ability for patient survival and enriched radiomics studies in gastric cancer.

Contrast computed tomography-based radiomics is correlation with COG risk stratification of neuroblastoma

PURPOSE

Although a risk stratification strategy for neuroblastoma (NB) has been proposed, precise and convenient clinical risk estimation remains challenging. This study aimed to investigate the correlation of contrast computed tomography (CT)-based radiomics with NB risk stratification.

METHODS

Patients with NB (n = 289) from two centers (244 and 45 patients in the training/testing and external validation cohorts, respectively) were divided into nonhigh- and high-risk groups. A total of 1648 radiomics features were extracted from the arterial phase, and the radiomics signature was constructed using rad scores, whereas the clinical model was established based on clinical factors. Further, a combined nomogram was developed based on the clinical factors and radiomics signatures. Finally, receiver operating characteristic curve and decision curve analyses (DCA) were used to assess the performance of the established models.

RESULTS

Seventeen radiomics features were used to construct the radiomics signature. A significant difference was observed in the rad score between the two groups in the training (0.540 vs. 0.704, P < 0.001) and testing (0.563 vs. 0.969, P < 0.001) cohorts. The nomogram showed a higher area under the curve (AUC) in the training (AUC = 0.87), testing (AUC = 0.83), and external validation (AUC = 0.84) cohorts than other models. The Hosmer-Lemeshow test and calibration curves indicated that the nomogram fit perfectly. DCA demonstrated that the clinical-radiomics nomogram was more beneficial.

CONCLUSIONS

Contrast CT-based radiomics shows correlation with COG risk stratification of NB. Radiomics features combined with clinical factors showed the best performance, which may improve the management of patients with NB.

Delta computed tomography radiomics features-based nomogram predicts long-term efficacy after neoadjuvant chemotherapy in advanced gastric cancer

BACKGROUND AND OBJECTIVE

No effective preoperative tool is available for predicting the prognosis of advanced gastric cancer (AGC) treated by neoadjuvant chemotherapy (NAC). We aimed to explore the association between change values ("delta") in the radiomic signatures of computed tomography (CT) (delCT-RS) before and after NAC for AGC and overall survival(OS).

METHODS AND DESIGN

A total of 132 AGC patients with AGC were studied as a training cohort in our center, and 45 patients from another center were used as an external validation set. A radiomic signatures-clinical-nomogram(RS-CN) was established using delCT-RS and preoperative clinical variables. The prediction performance of RS-CN was evaluated using the area under the receiver operating characteristic (ROC)curve (AUC values), time-dependent ROC, decision curve analysis(DCA) and C-index.

RESULTS

Multivariable Cox regression analyses showed that delCT-RS, cT-stage, cN-stage, Lauren-type and the value of variation of carcinoma embryonic antigen (CEA) between NAC were independent risk factors for 3-year OS of AGC. In the training cohort, RS-CN had a good prediction performance for OS (C-Index 0.73) and AUC values were significantly better than those of delCT-RS, ypTNM-stage and tumor regression grade(TRG) (0.827 vs 0.704 vs 0.749 vs 0.571, p < 0.001). DCA and time-dependent ROC of RS-CN were better than those of ypTNM stage, TRG grade and delCT-RS. The prediction performance of the validation set was equivalent to that of the training set. The cut-off (177.2) of RS-CN score was obtained from X-Tile software, a score of > 177.2 was defined as high-risk group(HRG), and scores of ≤ 177.2 were defined as the low-risk group(LRG). The 3-year OS and disease free survival(DFS) of patients in the LRG were significantly better than those in the HRG. Adjuvant chemotherapy(AC) can only significantly improve the 3-year OS and DFS of the LRG. (p < 0.05).

CONCLUSIONS

Our nomogram based on delCT-RS has good prediction of prognosis before surgery and helps identify patients that are most likely to benefit from AC. It works well in precise and individualised NAC in AGC.

Deep learning model for diagnosing early gastric cancer using preoperative computed tomography images

BACKGROUND

Early gastric cancer (EGC) is defined as a lesion restricted to the mucosa or submucosa, independent of size or evidence of regional lymph node metastases. Although computed tomography (CT) is the main technique for determining the stage of gastric cancer (GC), the accuracy of CT for determining tumor invasion of EGC was still unsatisfactory by radiologists. In this research, we attempted to construct an AI model to discriminate EGC in portal venous phase CT images.

METHODS

We retrospectively collected 658 GC patients from the first affiliated hospital of Nanchang university, and divided them into training and internal validation cohorts with a ratio of 8:2. As the external validation cohort, 93 GC patients were recruited from the second affiliated hospital of Soochow university. We developed several prediction models based on various convolutional neural networks, and compared their predictive performance.

RESULTS

The deep learning model based on the ResNet101 neural network represented sufficient discrimination of EGC. In two validation cohorts, the areas under the curves (AUCs) for the receiver operating characteristic (ROC) curves were 0.993 (95% CI: 0.984-1.000) and 0.968 (95% CI: 0.935-1.000), respectively, and the accuracy was 0.946 and 0.914. Additionally, the deep learning model can also differentiate between mucosa and submucosa tumors of EGC.

CONCLUSIONS

These results suggested that deep learning classifiers have the potential to be used as a screening tool for EGC, which is crucial in the individualized treatment of EGC patients.

Development and validation of a predictive model combining clinical, radiomics, and deep transfer learning features for lymph node metastasis in early gastric cancer

Background

This study aims to develop and validate a predictive model combining deep transfer learning, radiomics, and clinical features for lymph node metastasis (LNM) in early gastric cancer (EGC).

Materials and methods

This study retrospectively collected 555 patients with EGC, and randomly divided them into two cohorts with a ratio of 7:3 (training cohort, n = 388; internal validation cohort, n = 167). A total of 79 patients with EGC collected from the Second Affiliated Hospital of Soochow University were used as external validation cohort. Pre-trained deep learning networks were used to extract deep transfer learning (DTL) features, and radiomics features were extracted based on hand-crafted features. We employed the Spearman rank correlation test and least absolute shrinkage and selection operator regression for feature selection from the combined features of clinical, radiomics, and DTL features, and then, machine learning classification models including support vector machine, K-nearest neighbor, random decision forests (RF), and XGBoost were trained, and their performance by determining the area under the curve (AUC) were compared.

Results

We constructed eight pre-trained transfer learning networks and extracted DTL features, respectively. The results showed that 1,048 DTL features extracted based on the pre-trained Resnet152 network combined in the predictive model had the best performance in discriminating the LNM status of EGC, with an AUC of 0.901 (95% CI: 0.847-0.956) and 0.915 (95% CI: 0.850-0.981) in the internal validation and external validation cohorts, respectively.

Conclusion

We first utilized comprehensive multidimensional data based on deep transfer learning, radiomics, and clinical features with a good predictive ability for discriminating the LNM status in EGC, which could provide favorable information when choosing therapy options for individuals with EGC.

CT-based radiomic nomogram for preoperative prediction of DNA mismatch repair deficiency in gastric cancer

Background

DNA mismatch repair (MMR) deficiency has attracted considerable attention as a predictor of the immunotherapy efficacy of solid tumors, including gastric cancer. We aimed to develop and validate a computed tomography (CT)-based radiomic nomogram for the preoperative prediction of MMR deficiency in gastric cancer (GC).

Methods

In this retrospective analysis, 225 and 91 GC patients from two distinct hospital cohorts were included. Cohort 1 was randomly divided into a training cohort (n = 176) and an internal validation cohort (n = 76), whereas cohort 2 was considered an external validation cohort. Based on repeatable radiomic features, a radiomic signature was constructed using the least absolute shrinkage and selection operator (LASSO) regression analysis. We employed multivariable logistic regression analysis to build a radiomics-based model based on radiomic features and preoperative clinical characteristics. Furthermore, this prediction model was presented as a radiomic nomogram, which was evaluated in the training, internal validation, and external validation cohorts.

Results

The radiomic signature composed of 15 robust features showed a significant association with MMR protein status in the training, internal validation, and external validation cohorts (both P-values &lt;0.001). A radiomic nomogram incorporating a radiomic signature and two clinical characteristics (age and CT-reported N stage) represented good discrimination in the training cohort with an AUC of 0.902 (95% CI: 0.853–0.951), in the internal validation cohort with an AUC of 0.972 (95% CI: 0.945–1.000) and in the external validation cohort with an AUC of 0.891 (95% CI: 0.825–0.958).

Conclusion

The CT-based radiomic nomogram showed good performance for preoperative prediction of MMR protein status in GC. Furthermore, this model was a noninvasive tool to predict MMR protein status and guide neoadjuvant therapy.

Efficacy and prognostic value of delta radiomics on dual-energy computed tomography for gastric cancer with neoadjuvant chemotherapy: a preliminary study

BACKGROUND

A non-invasive tool for tumor regression grade (TRG) evaluation is urgently needed for gastric cancer (GC) treated with neoadjuvant chemotherapy (NAC).

PURPOSE

To develop and validate a radiomics signature (RS) to evaluate TRG for locally advanced GC after NAC and assess its prognostic value.

MATERIAL AND METHODS

A total of 103 patients with GC treated with NAC were retrospectively recruited from April 2018 to December 2019 and were randomly allocated into a training cohort (n = 69) and a validation cohort (n = 34). Delineation was performed on both mixed and iodine-uptake images based on dual-energy computed tomography (DECT). A total of 4094 radiomics features were extracted from the pre-NAC, post-NAC, and delta feature sets. Spearman correlation and the least absolute shrinkage and selection operator were used for dimensionality reduction. Multivariable logistic regression was used for TRG evaluation and generated the optimal RS. Kaplan-Meier survival analysis with the log-rank test was implemented in an independent cohort of 40 patients to validate the prognostic value of the optimal RS.

RESULTS

Three, five, and six radiomics features were finally selected for the pre-NAC, post-NAC, and delta feature sets. The delta model demonstrated the best performance in assessing TRG in both the training and the validation cohorts (AUCs=0.91 and 0.76, respectively; P>0.1). The optimal RS from the delta model showed a significant capability to predict survival in the independent cohort (P<0.05).

CONCLUSION

Delta radiomics based on DECT images serves as a potential biomarker for TRG evaluation and shows prognostic value for patients with GC treated with NAC.

Radiomics in precision medicine for gastric cancer: opportunities and challenges

OBJECTIVES

Radiomic features derived from routine medical images show great potential for personalized medicine in gastric cancer (GC). We aimed to evaluate the current status and quality of radiomic research as well as its potential for identifying biomarkers to predict therapy response and prognosis in patients with GC.

METHODS

We performed a systematic search of the PubMed and Embase databases for articles published from inception through July 10, 2021. The phase classification criteria for image mining studies and the radiomics quality scoring (RQS) tool were applied to evaluate scientific and reporting quality.

RESULTS

Twenty-five studies consisting of 10,432 patients were included. 96% of studies extracted radiomic features from CT images. Association between radiomic signature and therapy response was evaluated in seven (28%) studies; association with survival was evaluated in 17 (68%) studies; one (4%) study analyzed both. All results of the included studies showed significant associations. Based on the phase classification criteria for image mining studies, 18 (72%) studies were classified as phase II, with two, four, and one studies as discovery science, phase 0 and phase I, respectively. The median RQS score for the radiomic studies was 44.4% (range, 0 to 55.6%). There was extensive heterogeneity in the study population, tumor stage, treatment protocol, and radiomic workflow amongst the studies.

CONCLUSIONS

Although radiomic research in GC is highly heterogeneous and of relatively low quality, it holds promise for predicting therapy response and prognosis. Efforts towards standardization and collaboration are needed to utilize radiomics for clinical application.

KEY POINTS

• Radiomics application of gastric cancer is increasingly being reported, particularly in predicting therapy response and survival. • Although radiomics research in gastric cancer is highly heterogeneous and relatively low quality, it holds promise for predicting clinical outcomes. • Standardized imaging protocols and radiomic workflow are needed to facilitate radiomics into clinical use.

A CT-based deep learning radiomics nomogram for predicting the response to neoadjuvant chemotherapy in patients with locally advanced gastric cancer: A multicenter cohort study

BACKGROUND

Accurate prediction of treatment response to neoadjuvant chemotherapy (NACT) in individual patients with locally advanced gastric cancer (LAGC) is essential for personalized medicine. We aimed to develop and validate a deep learning radiomics nomogram (DLRN) based on pretreatment contrast-enhanced computed tomography (CT) images and clinical features to predict the response to NACT in patients with LAGC.

METHODS

719 patients with LAGC were retrospectively recruited from four Chinese hospitals between Dec 1st, 2014 and Nov 30th, 2020. The training cohort and internal validation cohort (IVC), comprising 243 and 103 patients, respectively, were randomly selected from center I; the external validation cohort1 (EVC1) comprised 207 patients from center II; and EVC2 comprised 166 patients from another two hospitals. Two imaging signatures, reflecting the phenotypes of the deep learning and handcrafted radiomics features, were constructed from the pretreatment portal venous-phase CT images. A four-step procedure, including reproducibility evaluation, the univariable analysis, the LASSO method, and the multivariable logistic regression analysis, was applied for feature selection and signature building. The integrated DLRN was then developed for the added value of the imaging signatures to independent clinicopathological factors for predicting the response to NACT. The prediction performance was assessed with respect to discrimination, calibration, and clinical usefulness. Kaplan-Meier survival curves based on the DLRN were used to estimate the disease-free survival (DFS) in the follow-up cohort (n = 300).

FINDINGS

The DLRN showed satisfactory discrimination of good response to NACT and yielded the areas under the receiver operating curve (AUCs) of 0.829 (95% CI, 0.739-0.920), 0.804 (95% CI, 0.732-0.877), and 0.827 (95% CI, 0.755-0.900) in the internal and two external validation cohorts, respectively, with good calibration in all cohorts (p > 0.05). Furthermore, the DLRN performed significantly better than the clinical model (p < 0.001). Decision curve analysis confirmed that the DLRN was clinically useful. Besides, DLRN was significantly associated with the DFS of patients with LAGC (p < 0.05).

INTERPRETATION

A deep learning-based radiomics nomogram exhibited a promising performance for predicting therapeutic response and clinical outcomes in patients with LAGC, which could provide valuable information for individualized treatment.

Can Gastric Cancer Patients with High Mandard Score Benefit from Neoadjuvant Chemotherapy?

A high Mandard score may indicate the tumor is insensitive to chemotherapy. We analyzed tumor regression and lymph node response under different Mandard scores to assess the impact of Mandard score on prognosis. Methods. Mandard scores and ypN stage of postoperative pathological reports were recorded. The results were reviewed by a professional pathologist. The radiologist compared the tumor regression before and after chemotherapy by computed tomography (CT). The survival of all patients was obtained by telephone follow-up. Multivariate Cox regression was used to assess the relationship between overall risk of death and Mandard score, imaging evaluation, and ypN stage. Results. In the Mandard score (4-5) group, the median survival time for PR and ypN0 patients was 68.5 and 76.7 months. While in the Mandard score (1-2) group, the median survival time for PD and ypN3a patients was 15.6 and 14.5 months. Imaging evaluation of tumor regression (PR 68.5 months, SD 27.8 months, and PD 10.2 months) and lymph node remission (ypN0 76.7 months, ypN1 61.6 months, ypN2 18.0 months, ypN3a 18.7 months, and ypN3b 18.3 months) showed improved survival. Mandard score, imaging evaluation, and ypN stage are important prognostic factors affecting prognosis. Conclusion. A high Mandard score does not mean neoadjuvant chemotherapy is ineffective in gastric cancer. Patients with imaging evaluation of tumor regression and ypN stage reduction may benefit from neoadjuvant chemotherapy.

CT-Based Radiomics Showing Generalization to Predict Tumor Regression Grade for Advanced Gastric Cancer Treated With Neoadjuvant Chemotherapy

Objective

The aim of this study was to develop and validate a radiomics model to predict treatment response in patients with advanced gastric cancer (AGC) sensitive to neoadjuvant therapies and verify its generalization among different regimens, including neoadjuvant chemotherapy (NAC) and molecular targeted therapy.

Materials and Methods

A total of 373 patients with AGC receiving neoadjuvant therapies were enrolled from five cohorts. Four cohorts of patients received different regimens of NAC, including three retrospective cohorts (training cohort and internal and external validation cohorts) and a prospective Dragon III cohort (NCT03636893). Another prospective SOXA (apatinib in combination with S-1 and oxaliplatin) cohort received neoadjuvant molecular targeted therapy (ChiCTR-OPC-16010061). All patients underwent computed tomography before treatment, and thereafter, tumor regression grade (TRG) was assessed. The primary tumor was delineated, and 2,452 radiomics features were extracted for each patient. Mutual information and random forest were used for dimensionality reduction and modeling. The performance of the radiomics model to predict TRG under different neoadjuvant therapies was evaluated.

Results

There were 28 radiomics features selected. The radiomics model showed generalization to predict TRG for AGC patients across different NAC regimens, with areas under the curve (AUCs) (95% interval confidence) of 0.82 (0.76~0.90), 0.77 (0.63~0.91), 0.78 (0.66~0.89), and 0.72 (0.66~0.89) in the four cohorts, with no statistical difference observed (all p > 0.05). However, the radiomics model showed poor predictive value on the SOXA cohort [AUC, 0.50 (0.27~0.73)], which was significantly worse than that in the training cohort (p = 0.010).

Conclusion

Radiomics is generalizable to predict TRG for AGC patients receiving NAC treatments, which is beneficial to transform appropriate treatment, especially for those insensitive to NAC.

Pretreatment Contrast-Enhanced Computed Tomography Radiomics for Prediction of Pathological Regression Following Neoadjuvant Chemotherapy in Locally Advanced Gastric Cancer: A Preliminary Multicenter Study

Background

Sensitivity to neoadjuvant chemotherapy in locally advanced gastric cancer patients varies; however, an effective predictive marker is currently lacking. We aimed to propose and validate a practical treatment efficacy prediction method based on contrast-enhanced computed tomography (CECT) radiomics.

Method

Data of l24 locally advanced gastric carcinoma patients who underwent neoadjuvant chemotherapy were acquired retrospectively between December 2012 and August 2020 from three different cancer centers. In total, 1216 radiomics features were initially extracted from each lesion’s pretreatment portal venous phase computed tomography image. Subsequently, a radiomics predictive model was constructed using machine learning software. Clinicopathological data and radiological parameters of the enrolled patients were collected and analyzed retrospectively. Univariate and multivariate logistic regression analyses were performed to screen for independent predictive indices. Finally, we developed an integrated model combining clinicopathological predictive parameters and radiomics features.

Result

In the training set, 10 (14.9%) patients achieved a good response (GR) after preoperative neoadjuvant chemotherapy (n = 77), whereas in the testing set, seven (17.5%) patients achieved a GR (n = 47). The radiomics predictive model showed competitive prediction efficacy in both the training and independent external validation sets. The areas under the curve (AUC) values were 0.827 (95% confidence interval [CI]: 0.609–1.000) and 0.854 (95% CI: 0.610–1.000), respectively. Similarly, when only the single hospital data were included as an independent external validation set (testing set 2), AUC values of the models were 0.827 (95% CI: 0.650–0.952) and 0.889 (95% CI: 0.663–1.000) in the training set and testing set 2, respectively.

Conclusion

Our study is the first to discover that CECT radiomics could provide powerful and consistent predictions of therapeutic sensitivity to neoadjuvant chemotherapy among gastric cancer patients across different hospitals.

A radiomics model predicts the response of patients with advanced gastric cancer to PD-1 inhibitor treatment

Programmed cell death 1 (PD1) inhibitors have shown promising treatment effects in advanced gastric cancer, the beneficiary population not definite. This study aimed to construct an individualized radiomics model to predict the treatment benefits of PD-1 inhibitors in gastric cancer. Patients with advanced gastric cancer treated with PD-1 inhibitors were randomly divided into a training set (n = 58) and a validation set (n = 29). CT imaging data were extracted from medical records, and an individual radiomics nomogram was generated based on the imaging features and clinicopathological risk factors. Discrimination performance was evaluated by Harrell’s c-index and receiver operator characteristic (ROC) curve analyses. The areas under the ROC curves (AUCs) were analyzed to predict anti-PD-1 efficacy and survival. We found that the radiomics nomogram could predict the response of gastric cancer to anti-PD-1 treatment. The AUC was 0.865 with a 95% CI of 0.812-0.828 in the training set, while the AUC was 0.778 with a 95% CI of 0.732–0.776 in the validation set. The diagnostic performance of the radiomics was significantly higher than that of the clinical factors (p < 0.01). Patients with a low risk of disease progression discriminated by the radiomics nomogram had longer progression-free survival than those with a high risk (6.5 vs. 3.2 months, HR 1.99, 95% CI: 1.19-3.31, p = 0.009). The radiomics nomogram based on CT imaging features and clinical risk factors could predict the treatment benefits of PD-1 inhibitors in advanced gastric cancer, enabling it to guide decision-making regarding clinical treatment.

Predictive Value of Multiparametric MRI for Response to Single-Cycle Induction Chemo-Immunotherapy in Locally Advanced Head and Neck Squamous Cell Carcinoma

Objectives

To assess the predictive value of multiparametric MRI for treatment response evaluation of induction chemo-immunotherapy in locally advanced head and neck squamous cell carcinoma.

Methods

Twenty-two patients with locally advanced, histologically confirmed head and neck squamous cell carcinoma who were enrolled in the prospective multicenter phase II CheckRad-CD8 trial were included in the current analysis. In this unplanned secondary single-center analysis, all patients who received contrast-enhanced MRI at baseline and in week 4 after single-cycle induction therapy with cisplatin/docetaxel combined with the immune checkpoint inhibitors tremelimumab and durvalumab were included. In week 4, endoscopy with representative re-biopsy was performed to assess tumor response. All lesions were segmented in the baseline and restaging multiparametric MRI, including the primary tumor and lymph node metastases. The volume of interest of the respective lesions was volumetrically measured, and time-resolved mean intensities of the golden-angle radial sparse parallel-volume-interpolated gradient-echo perfusion (GRASP-VIBE) sequence were extracted. Additional quantitative parameters including the T1 ratio, short-TI inversion recovery ratio, apparent diffusion coefficient, and dynamic contrast-enhanced (DCE) values were measured. A model based on parallel random forests incorporating the MRI parameters from the baseline MRI was used to predict tumor response to therapy. Receiver operating characteristic (ROC) curves were used to evaluate the prognostic performance.

Results

Fifteen patients (68.2%) showed pathologic complete response in the re-biopsy, while seven patients had a residual tumor (31.8%). In all patients, the MRI-based primary tumor volume was significantly lower after treatment. The baseline DCE parameters of time to peak and wash-out were significantly different between the pathologic complete response group and the residual tumor group (p < 0.05). The developed model, based on parallel random forests and DCE parameters, was able to predict therapy response with a sensitivity of 78.7% (95% CI 71.24–84.93) and a specificity of 78.6% (95% CI 67.13–87.48). The model had an area under the ROC curve of 0.866 (95% CI 0.819–0.914).

Conclusions

DCE parameters indicated treatment response at follow-up, and a random forest machine learning algorithm based on DCE parameters was able to predict treatment response to induction chemo-immunotherapy.

Segmentation of Gastric Computerized Tomography Images under Intelligent Algorithms in Evaluation of Efficacy of Decitabine Combined with Paclitaxel in Treatment of Gastric Cancer

To analyze the evaluation of artificial intelligence algorithm combined with gastric computed tomography (CT) image in clinical chemotherapy for advanced gastric cancer, 112 patients with advanced gastric cancer were selected as the research object. Among which, 56 patients in the experimental group received paclitaxel (PTX) combined with decitabine sequential decitabine maintenance therapy. Fifty-six patients in the control group received first-line treatment with decitabine combined with cisplatin. The image segmentation algorithm based on fast interactive dictionary selection was used to process gastric CT images. Complete response (CR), partial response (PR), stable disease (SD), progressive disease (PD), response rate (RR), disease control rate (DCR), and overall survival (OS) after treatment were recorded. The true-positive rate (TPR) and coincidence ratio (CR) of the proposed algorithm for image segmentation were significantly higher than those of the mean shift algorithm and the iCoseg algorithm. The mean edge distance (MED) and edge distance variance (EDV) were significantly lower than the mean shift algorithm and the iCoseg algorithm, and the differences were considerable (P < 0.05). The number of CR (5 cases), PR (13 cases), RR (18 cases), and DCR (44 cases) in the experimental group was significantly higher than that in the control group, while the number of PD (12 cases) was significantly lower than that in the control group (P < 0.05). The number of patients complicated with hematological toxicity, leucopenia, thrombocytopenia, and digestive tract reaction in the experimental group was less than that in the control group (P < 0.05). From the comparison of long-term efficacy, the survival rate of patients in both groups showed a decreasing trend within 24 months, but the decreasing trend of survival rate of patients in the experimental group was better than that in the control group. In short, the proposed algorithm had better segmentation performance than traditional algorithms. Compared with first-line treatment with decitabine and cisplatin, PTX in combination with decitabine sequential citabine maintenance regimens had better disease control rates, lower toxicity, and more effective improvements in patient quality of life and longer survival in patients with advanced gastric cancer.

Predicting Response to Systemic Chemotherapy for Advanced Gastric Cancer Using Pre-Treatment Dual-Energy CT Radiomics: A Pilot Study

Objective

To build and assess a pre-treatment dual-energy CT-based clinical-radiomics nomogram for the individualized prediction of clinical response to systemic chemotherapy in advanced gastric cancer (AGC).

Methods

A total of 69 pathologically confirmed AGC patients who underwent dual-energy CT before systemic chemotherapy were enrolled from two centers in this retrospective study. Treatment response was determined with follow-up CT according to the RECIST standard. Quantitative radiomics metrics of the primary lesion were extracted from three sets of monochromatic images (40, 70, and 100 keV) at venous phase. Univariate analysis and least absolute shrinkage and selection operator (LASSO) were used to select the most relevant radiomics features. Multivariable logistic regression was performed to establish a clinical model, three monochromatic radiomics models, and a combined multi-energy model. ROC analysis and DeLong test were used to evaluate and compare the predictive performance among models. A clinical-radiomics nomogram was developed; moreover, its discrimination, calibration, and clinical usefulness were assessed.

Result

Among the included patients, 24 responded to the systemic chemotherapy. Clinical stage and the iodine concentration (IC) of the tumor were significant clinical predictors of chemotherapy response (all p < 0.05). The multi-energy radiomics model showed a higher predictive capability (AUC = 0.914) than two monochromatic radiomics models and the clinical model (AUC: 40 keV = 0.747, 70 keV = 0.793, clinical = 0.775); however, the predictive accuracy of the 100-keV model (AUC: 0.881) was not statistically different (p = 0.221). The clinical-radiomics nomogram integrating the multi-energy radiomics signature with IC value and clinical stage showed good calibration and discrimination with an AUC of 0.934. Decision curve analysis proved the clinical usefulness of the nomogram and multi-energy radiomics model.

Conclusion

The pre-treatment DECT-based clinical-radiomics nomogram showed good performance in predicting clinical response to systemic chemotherapy in AGC, which may contribute to clinical decision-making and improving patient survival.

Perioperative Chemotherapy with FLOT Scheme in Resectable Gastric Adenocarcinoma: A Preliminary Correlation between TRG and Radiomics

Perioperative chemotherapy (p-ChT) with a fluorouracil plus leucovorin, oxaliplatin, and docetaxel (FLOT) scheme is the gold standard of care for locally advanced gastric cancer. We aimed to test CT radiomics performance in early response prediction for p-ChT. Patients with advanced gastric cancer who underwent contrast enhanced CT prior to and post p-ChT were retrospectively enrolled. Histologic evaluation of resected specimens was used as the reference standard, and patients were divided into responders (TRG 1a-1b) and non-responders (TRG 2-3) according to their Becker tumor regression grade (TRG). A volumetric region of interest including the whole tumor tissue was drawn from a CT portal-venous phase before and after p-ChT; 120 radiomic features, both first and second order, were extracted. CT radiomics performances were derived from baseline CT radiomics alone and ΔRadiomics to predict response to p-ChT according to the TRG and tested using a receiver operating characteristic (ROC) curve. The final population comprised 15 patients, 6 (40%) responders and 9 (60%) non-responders. Among pre-treatment CT radiomics parameters, Shape, GLCM, First order, and NGTDM features showed a significant ability to discriminate between responders and non-responders (p < 0.011), with Cluster Shade and Autocorrelation (GLCM features) having AUC = 0.907. ΔRadiomics showed significant differences for Shape, GLRLM, GLSZM, and NGTDM features (p < 0.007). MeshVolume (Shape feature) and LongRunEmphasis (GLRLM feature) had AUC = 0.889. In conclusion, CT radiomics may represent an important supportive approach for the radiologic evaluation of advanced gastric cancer patients.

Radiomics as a New Frontier of Imaging for Cancer Prognosis: A Narrative Review

The evaluation of the efficacy of different therapies is of paramount importance for the patients and the clinicians in oncology, and it is usually possible by performing imaging investigations that are interpreted, taking in consideration different response evaluation criteria. In the last decade, texture analysis (TA) has been developed in order to help the radiologist to quantify and identify parameters related to tumor heterogeneity, which cannot be appreciated by the naked eye, that can be correlated with different endpoints, including cancer prognosis. The aim of this work is to analyze the impact of texture in the prediction of response and in prognosis stratification in oncology, taking into consideration different pathologies (lung cancer, breast cancer, gastric cancer, hepatic cancer, rectal cancer). Key references were derived from a PubMed query. Hand searching and clinicaltrials.gov were also used. This paper contains a narrative report and a critical discussion of radiomics approaches related to cancer prognosis in different fields of diseases.

Artificial Intelligence in the Imaging of Gastric Cancer: Current Applications and Future Direction

Gastric cancer (GC) is one of the most common cancers and one of the leading causes of cancer-related death worldwide. Precise diagnosis and evaluation of GC, especially using noninvasive methods, are fundamental to optimal therapeutic decision-making. Despite the recent rapid advancements in technology, pretreatment diagnostic accuracy varies between modalities, and correlations between imaging and histological features are far from perfect. Artificial intelligence (AI) techniques, particularly hand-crafted radiomics and deep learning, have offered hope in addressing these issues. AI has been used widely in GC research, because of its ability to convert medical images into minable data and to detect invisible textures. In this article, we systematically reviewed the methodological processes (data acquisition, lesion segmentation, feature extraction, feature selection, and model construction) involved in AI. We also summarized the current clinical applications of AI in GC research, which include characterization, differential diagnosis, treatment response monitoring, and prognosis prediction. Challenges and opportunities in AI-based GC research are highlighted for consideration in future studies.

Evaluation of dual-energy CT derived radiomics signatures in predicting outcomes in patients with advanced gastric cancer after neoadjuvant chemotherapy

BACKGROUND

To investigate the prognostic value of dual-energy CT (DECT) based radiomics to predict disease-free survival (DFS) and overall survival (OS) for patients with advanced gastric cancer (AGC) after neoadjuvant chemotherapy (NAC).

METHODS

From January 2014 to December 2018, a total of 156 AGC patients were enrolled and randomly allocated into a training cohort and a testing cohort at a ratio of 2:1. Volume of interest of primary tumor was delineated on eight image series. Four feature sets derived from pre-NAC and delta radiomics were generated for each survival arm. Random survival forest was used for generating the optimal radiomics signature (RS). Statistical metrics for model evaluation included Harrell's concordance index (C-index) and the average cumulative/dynamic AUC throughout follow-up. A clinical model and a combined Rad-clinical model were built for comparison.

RESULTS

The pre-IU (derived from iodine uptake images before NAC) RS performed best for DFS and OS in the testing cohort (C-indices, 0.784 and 0.698; the average cumulative/dynamic AUCs, 0.80 and 0.77). When compared with the clinical model, the radiomics model had significantly higher C-index to predict DFS in the testing cohort (0.784 vs. 0.635, p < 0.001), but no statistical difference was found for OS (0.698 vs. 0.680, p = 0.473). The combined Rad-clinical models showed improved performance in the testing cohort, with C-indices of 0.810 and 0.710 for DFS and OS, respectively.

CONCLUSION

DECT-derived radiomics serves as a promising non-invasive biomarker to predict survival for AGC patients after NAC, providing an opportunity for transforming proper treatment.

A novel CT-based radiomic nomogram for predicting the recurrence and metastasis of gastric stromal tumors

Our study aimed to explore the value of applying the CT-based radiomic nomogram for predicting recurrence and/or metastasis (RM) of gastric stromal tumors (GSTs). During the past ten years, a total of 236 patients with GST were analyzed retrospectively. According to the postoperative follow-up classification, the patients were divided into two groups, namely non-recurrence/metastasis group (non-RM) and RM group. All the cases were randomly divided into primary cohort and validation cohort according to the ratio of 7:3. Standardized CT images were segmented by radiologists using ITK-SNAP software manually. Texture features were extracted from all segmented lesions, then radiomic features were selected and the radiomic nomogram was built using least absolute shrinkage and selection operator (LASSO) method. The clinical features with the greatest correlation with RM of GST were selected by univariate analysis, and used as parameters to build the clinical feature model. Eventually, model of radiomic and clinical features were fitted to construct the clinical + radiomic feature model. The performance of each model was evaluated by the area under receiver operating characteristic (ROC) curve (AUC). A total of 1223 features were extracted from all the segmentation regions of each case, and features were selected via the least absolute shrinkage and LASSO binary logistic regression model. After deletion of redundant features, four key features were obtained, which were used as the parameters to build a radiomic signature. The AUCs of radiomic nomogram in primary cohort and validation cohort were 0.816 and 0.946, respectively. The AUCs of clinical + radiomic feature model in primary cohort and validation cohort were 0.833 and 0.937, respectively. Using DeLong test, the differences of AUC values between radiomic nomogram and clinical + radiomic feature model in primary cohort (P = 0.840) and validation cohort (P = 0.857) were not statistically significant. To sum up, CT-based radiomic nomogram is of great potential in predicting the RM of GST non-invasively before operation.

Radiomics in Oncology, Part 1: Technical Principles and Gastrointestinal Application in CT and MRI

Simple Summary

Part I is an overview aimed to investigate some technical principles and the main fields of radiomic application in gastrointestinal oncologic imaging (CT and MRI) with a focus on diagnosis, prediction prognosis, and assessment of response to therapy in gastrointestinal cancers, describing mostly the results for each pre-eminent tumor. In particular, this paper provides a general description of the main radiomic drawbacks and future challenges, which limit radiomic application in clinical setting as routine. Further investigations need to standardize and validate the Radiomics as a helpful tool in management of oncologic patients. In that context, Radiomics has been playing a relevant role and could be considered as a future imaging landscape.

Abstract

Radiomics has been playing a pivotal role in oncological translational imaging, particularly in cancer diagnosis, prediction prognosis, and therapy response assessment. Recently, promising results were achieved in management of cancer patients by extracting mineable high-dimensional data from medical images, supporting clinicians in decision-making process in the new era of target therapy and personalized medicine. Radiomics could provide quantitative data, extracted from medical images, that could reflect microenvironmental tumor heterogeneity, which might be a useful information for treatment tailoring. Thus, it could be helpful to overcome the main limitations of traditional tumor biopsy, often affected by bias in tumor sampling, lack of repeatability and possible procedure complications. This quantitative approach has been widely investigated as a non-invasive and an objective imaging biomarker in cancer patients; however, it is not applied as a clinical routine due to several limitations related to lack of standardization and validation of images acquisition protocols, features segmentation, extraction, processing, and data analysis. This field is in continuous evolution in each type of cancer, and results support the idea that in the future Radiomics might be a reliable application in oncologic imaging. The first part of this review aimed to describe some radiomic technical principles and clinical applications to gastrointestinal oncologic imaging (CT and MRI) with a focus on diagnosis, prediction prognosis, and assessment of response to therapy.

Radiomics in immuno-oncology

With the ongoing advances in imaging techniques, increasing volumes of anatomical and functional data are being generated as part of the routine clinical workflow. This surge of available imaging data coincides with increasing research in quantitative imaging, particularly in the domain of imaging features. An important and novel approach is radiomics, where high-dimensional image properties are extracted from routine medical images. The fundamental principle of radiomics is the hypothesis that biomedical images contain predictive information, not discernible to the human eye, that can be mined through quantitative image analysis. In this review, a general outline of radiomics and artificial intelligence (AI) will be provided, along with prominent use cases in immunotherapy (e.g. response and adverse event prediction) and targeted therapy (i.e. radiogenomics). While the increased use and development of radiomics and AI in immuno-oncology is highly promising, the technology is still in its early stages, and different challenges still need to be overcome. Nevertheless, novel AI algorithms are being constructed with an ever-increasing scope of applications.

Predicting Chemotherapeutic Response for Far-advanced Gastric Cancer by Radiomics with Deep Learning Semi-automatic Segmentation

Purpose: To build a dual-energy computed tomography (DECT) delta radiomics model to predict chemotherapeutic response for far-advanced gastric cancer (GC) patients. A semi-automatic segmentation method based on deep learning was designed, and its performance was compared with that of manual segmentation. Methods: This retrospective study included 86 patients with far-advanced GC treated with chemotherapy from September 2016 to December 2017 (66 and 20 in the training and testing cohorts, respectively). Delta radiomics features between the baseline and first follow-up DECT were modeled by random forest to predict the chemotherapeutic response evaluated by the second follow-up DECT. Nine feature subsets from confounding factors and delta radiomics features were used to choose the best model with 10-fold cross-validation in the training cohort. A semi-automatic segmentation method based on deep learning was developed to predict the chemotherapeutic response and compared with manual segmentation in the testing cohort, which was further validated in an independent validation cohort of 30 patients. Results: The best model, constructed by confounding factors and texture features, reached an average AUC of 0.752 in the training cohort. Our proposed semi-automatic segmentation method was more time-effective than manual segmentation, with average saving-time of 11.2333 ± 6.3989 minutes and 9.9889 ±5.5086 minutes in the testing cohort and the independent validation cohort, respectively (both p < 0.05). The predictive ability of the semi-automatic segmentation was also better than that of the manual segmentation both in the testing cohort and the independent validation cohort (AUC: 0.728 vs. 0.687 and 0.828 vs. 0.749, respectively). Conclusion: DECT delta radiomics serves as a promising biomarker for predicting chemotherapeutic response for far-advanced GC. Semi-automatic segmentation based on deep learning shows the potential for clinical use with increased reproducibility and decreased labor costs compared to the manual version.