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

Prognostic value of the tumor-to-liver density ratio in patients with metastatic colorectal cancer treated with bevacizumab-based chemotherapy. A post-hoc study of the STIC-AVASTIN trial

BACKGROUND

The Response Evaluation Criteria in Solid Tumors (RECIST) are often inadequate for the early assessment of the response to cancer therapy, particularly bevacizumab-based chemotherapy. In a first cohort of patients with colorectal cancer liver metastases (CRLM), we showed that variations of the tumor-to-liver density (TTLD) ratio and modified size-based criteria determined using computed tomography (CT) data at the first restaging were better prognostic criteria than the RECIST. The aims of this study were to confirm the relevance of these radiological biomarkers as early predictors of the long-term clinical outcome and to assess their correlation with contrast-enhanced ultrasound (CEUS) parameters in a new patient cohort.

METHODS

In this post-hoc study of the multicenter STIC-AVASTIN trial, we retrospectively reviewed CT data of patients with CRLM treated with bevacizumab-based regimens. We determined the size, density and TTLD ratio of target liver lesions at baseline and at the first restaging and also performed a morphologic evaluation according to the MD Anderson criteria. We assessed the correlation of these parameters with progression-free survival (PFS) and overall survival (OS) using the log-rank test and a Cox proportional hazard model. We also examined the association between TTLD ratio and quantitative CEUS parameters.

RESULTS

This analysis concerned 79 of the 137 patients included in the STIC-AVASTIN trial. PFS and OS were significantly longer in patients with tumor size reduction > 15% at first restaging, but were not correlated with TTLD ratio variations. However, PFS was longer in patients with TTLD ratio > 0.6 at baseline and first restaging than in those who did not reach this threshold. In the multivariate analysis, only baseline TTLD ratio > 0.6 was a significant survival predictor. TTLD ratio > 0.6 was associated with improved perfusion parameters.

CONCLUSIONS

Although TTLD ratio variations did not correlate with the long-term clinical outcomes, TTLD absolute values remained a good predictor of survival at baseline and first restaging, and may reflect tumor microvascular features that might influence bevacizumab-based treatment efficiency.

TRIAL REGISTRATION

NCT00489697, registration number of the STIC-AVASTIN trial.

Challenges and perspectives in use of artificial intelligence to support treatment recommendations in clinical oncology

Artificial intelligence (AI) promises to be the next revolutionary step in modern society. Yet, its role in all fields of industry and science need to be determined. One very promising field is represented by AI-based decision-making tools in clinical oncology leading to more comprehensive, personalized therapy approaches. In this review, the authors provide an overview on all relevant technical applications of AI in oncology, which are required to understand the future challenges and realistic perspectives for decision-making tools. In recent years, various applications of AI in medicine have been developed focusing on the analysis of radiological and pathological images. AI applications encompass large amounts of complex data supporting clinical decision-making and reducing errors by objectively quantifying all aspects of the data collected. In clinical oncology, almost all patients receive a treatment recommendation in a multidisciplinary cancer conference at the beginning and during their treatment periods. These highly complex decisions are based on a large amount of information (of the patients and of the various treatment options), which need to be analyzed and correctly classified in a short time. In this review, the authors describe the technical and medical requirements of AI to address these scientific challenges in a multidisciplinary manner. Major challenges in the use of AI in oncology and decision-making tools are data security, data representation, and explainability of AI-based outcome predictions, in particular for decision-making processes in multidisciplinary cancer conferences. Finally, limitations and potential solutions are described and compared for current and future research attempts.

Response Evaluation Criteria in Gastrointestinal and Abdominal Cancers: Which to Use and How to Measure

As the management of gastrointestinal malignancy has evolved, tumor response assessment has expanded from size-based assessments to those that include tumor enhancement, in addition to functional data such as those derived from PET and diffusion-weighted imaging. Accurate interpretation of tumor response therefore requires knowledge of imaging modalities used in gastrointestinal malignancy, anticancer therapies, and tumor biology. Targeted therapies such as immunotherapy pose additional considerations due to unique imaging response patterns and drug toxicity; as a consequence, immunotherapy response criteria have been developed. Some gastrointestinal malignancies require assessment with tumor-specific criteria when assessing response, often to guide clinical management (such as watchful waiting in rectal cancer or suitability for surgery in pancreatic cancer). Moreover, anatomic measurements can underestimate therapeutic response when applied to molecular-targeted therapies or locoregional therapies in hypervascular malignancies such as hepatocellular carcinoma. In these cases, responding tumors may exhibit morphologic changes including cystic degeneration, necrosis, and hemorrhage, often without significant reduction in size. Awareness of pitfalls when interpreting gastrointestinal tumor response is required to correctly interpret response assessment imaging and guide appropriate oncologic management. Data-driven image analyses such as radiomics have been investigated in a variety of gastrointestinal tumors, such as identifying those more likely to respond to therapy or recur, with the aim of delivering precision medicine. Multimedia-enhanced radiology reports can facilitate communication of gastrointestinal tumor response by automatically embedding response categories, key data, and representative images. ©RSNA, 2024 Test Your Knowledge questions for this article are available in the supplemental material.

CT-based radiomics for predicting lymph node metastasis in esophageal cancer: a systematic review and meta-analysis

Objective

We aimed to evaluate the diagnostic effectiveness of computed tomography (CT)-based radiomics for predicting lymph node metastasis (LNM) in patients diagnosed with esophageal cancer (EC).

Methods

The present study conducted a comprehensive search by accessing the following databases: PubMed, Embase, Cochrane Library, and Web of Science, with the aim of identifying relevant studies published until July 10th, 2023. The diagnostic accuracy was summarized using the pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the curve (AUC). The researchers utilized Spearman's correlation coefficient for assessing the threshold effect, besides performing meta-regression and subgroup analysis for the exploration of possible heterogeneity sources. The quality assessment was conducted using the Quality Assessment of Diagnostic Accuracy Studies-2 and the Radiomics Quality Score (RQS).

Results

The meta-analysis included six studies conducted from 2018 to 2022, with 483 patients enrolled and LNM rates ranging from 27.2% to 59.4%. The pooled sensitivity, specificity, PLR, NLR, DOR, and AUC, along with their corresponding 95% CI, were 0.73 (0.67, 0.79), 0.76 (0.69, 0.83), 3.1 (2.3, 4.2), 0.35 (0.28, 0.44), 9 (6, 14), and 0.78 (0.74, 0.81), respectively. The results demonstrated the absence of significant heterogeneity in sensitivity, while significant heterogeneity was observed in specificity; no threshold effect was detected. The observed heterogeneity in the specificity was attributed to the sample size and CT-scan phases (P < 0.05). The included studies exhibited suboptimal quality, with RQS ranging from 14 to 16 out of 36. However, most of the enrolled studies exhibited a low-risk bias and minimal concerns relating to applicability.

Conclusion

The present meta-analysis indicated that CT-based radiomics demonstrated a favorable diagnostic performance in predicting LNM in EC. Nevertheless, additional high-quality, large-scale, and multicenter trials are warranted to corroborate these findings.

Systematic Review Registration

Open Science Framework platform at https://osf.io/5zcnd.

Deep learning models for automatic tumor segmentation and total tumor volume assessment in patients with colorectal liver metastases

BACKGROUND

We developed models for tumor segmentation to automate the assessment of total tumor volume (TTV) in patients with colorectal liver metastases (CRLM).

METHODS

In this prospective cohort study, pre- and post-systemic treatment computed tomography (CT) scans of 259 patients with initially unresectable CRLM of the CAIRO5 trial (NCT02162563) were included. In total, 595 CT scans comprising 8,959 CRLM were divided into training (73%), validation (6.5%), and test sets (21%). Deep learning models were trained with ground truth segmentations of the liver and CRLM. TTV was calculated based on the CRLM segmentations. An external validation cohort was included, comprising 72 preoperative CT scans of patients with 112 resectable CRLM. Image segmentation evaluation metrics and intraclass correlation coefficient (ICC) were calculated.

RESULTS

In the test set (122 CT scans), the autosegmentation models showed a global Dice similarity coefficient (DSC) of 0.96 (liver) and 0.86 (CRLM). The corresponding median per-case DSC was 0.96 (interquartile range [IQR] 0.95-0.96) and 0.80 (IQR 0.67-0.87). For tumor segmentation, the intersection-over-union, precision, and recall were 0.75, 0.89, and 0.84, respectively. An excellent agreement was observed between the reference and automatically computed TTV for the test set (ICC 0.98) and external validation cohort (ICC 0.98). In the external validation, the global DSC was 0.82 and the median per-case DSC was 0.60 (IQR 0.29-0.76) for tumor segmentation.

CONCLUSIONS

Deep learning autosegmentation models were able to segment the liver and CRLM automatically and accurately in patients with initially unresectable CRLM, enabling automatic TTV assessment in such patients.

RELEVANCE STATEMENT

Automatic segmentation enables the assessment of total tumor volume in patients with colorectal liver metastases, with a high potential of decreasing radiologist's workload and increasing accuracy and consistency.

KEY POINTS

• Tumor response evaluation is time-consuming, manually performed, and ignores total tumor volume. • Automatic models can accurately segment tumors in patients with colorectal liver metastases. • Total tumor volume can be accurately calculated based on automatic segmentations.

Assessment of axial spondyloarthritis activity using a magnetic resonance imaging-based multi-region-of-interest fusion model

BACKGROUND

Identifying axial spondyloarthritis (axSpA) activity early and accurately is essential for treating physicians to adjust treatment plans and guide clinical decisions promptly. The current literature is mostly focused on axSpA diagnosis, and there has been thus far, no study that reported the use of a radiomics approach for differentiating axSpA disease activity. In this study, the aim was to develop a radiomics model for differentiating active from non-active axSpA based on fat-suppressed (FS) T2-weighted (T2w) magnetic resonance imaging (MRI) of sacroiliac joints.

METHODS

This retrospective study included 109 patients diagnosed with non-active axSpA (n = 68) and active axSpA (n = 41); patients were divided into training and testing cohorts at a ratio of 8:2. Radiomics features were extracted from 3.0 T sacroiliac MRI using two different heterogeneous regions of interest (ROIs, Circle and Facet). Various methods were used to select relevant and robust features, and different classifiers were used to build Circle-based, Facet-based, and a fusion prediction model. Their performance was compared using various statistical parameters. p < 0.05 is considered statistically significant.

RESULTS

For both Circle- and Facet-based models, 2284 radiomics features were extracted. The combined fusion ROI model accurately differentiated between active and non-active axSpA, with high accuracy (0.90 vs.0.81), sensitivity (0.90 vs. 0.75), and specificity (0.90 vs. 0.85) in both training and testing cohorts.

CONCLUSION

The multi-ROI fusion radiomics model developed in this study differentiated between active and non-active axSpA using sacroiliac FS T2w-MRI. The results suggest MRI-based radiomics of the SIJ can distinguish axSpA activity, which can improve the therapeutic result and patient prognosis. To our knowledge, this is the only study in the literature that used a radiomics approach to determine axSpA activity.

[Multi-sequence relation attention network for diagnosing HLA-B27-negative axial spondyloarthritis]

OBJECTIVE

To develop a new 3D multi-sequence relation attention network for exploring the complementary and correlation information of different magnetic resonance imaging (MRI) modalities and improving the diagnostic performance of HLA-B27-negative axial spondyloarthropathy (axSpA).

METHODS

We retrospectively collected T1-weighted imaging (T1WI) and fat suppuration MRI (FS-MRI) data and clinical data of 375 HLA-B27-negative patients from the Third Affiliated Hospital of Southern Medical University (including 164 axSpA and 211 non-axSpA patients) and 49 patients from Nanhai Hospital (including 27 axSpA and 22 non-axSpA patients) between January, 2010 and August, 2021.A 3D relation attention network MSFANet based on multi-sequence MRI was used for automatic diagnosis of axSpA against non-axSpA in these patients.MSFANet consisted of a shallow shared feature learning module and a class-aware feature learning module, and latter module used a 3D multi-sequence relation attention mechanism to refine and fuse multi-sequence MRI features.A hybrid loss function was used to enhance the recognition ability of MSFANet by learning the loss weight coefficients of different branches to improve the classification performance.

RESULTS

The experimental results demonstrated that MSFANet outperformed several state-of-the-art fusion algorithms (P<0.05) with AUC, accuracy, sensitivity, and specificity of 0.840, 77.93%, 83.70%, and 70.29% in the internal validation set, and of 0.783, 74.47%, 82.43% and 70.40% in the independent external validation set, respectively.The ablation studies showed that under the same architecture, the fusion model was superior to single-sequence models, which confirmed the effectiveness and necessity of fusing multi-sequence MRI.The visualization results demonstrated that MSFANet could focus on learning information from abnormal areas on MRI during the classification.

CONCLUSION

We successfully constructed a 3D deep neural network based on multi-sequence MRI for differential diagnosis of HLA-B27-negative axSpA against nonaxSpA and verified the effectiveness of the multisequence relation attention mechanism for promoting classification performance of the network.

Combination of clinical, radiomic, and "delta" radiomic features in survival prediction of metastatic gastroesophageal adenocarcinoma

Objectives

To identify combined clinical, radiomic, and delta-radiomic features in metastatic gastroesophageal adenocarcinomas (GEAs) that may predict survival outcomes.

Methods

A total of 166 patients with metastatic GEAs on palliative chemotherapy with baseline and treatment/follow-up (8-12 weeks) contrast-enhanced CT were retrospectively identified. Demographic and clinical data were collected. Three-dimensional whole-lesional radiomic analysis was performed on the treatment/follow-up scans. "Delta" radiomic features were calculated based on the change in radiomic parameters compared to the baseline. The univariable analysis (UVA) Cox proportional hazards model was used to select clinical variables predictive of overall survival (OS) and progression-free survival (PFS) (p-value <0.05). The radiomic and "delta" features were then assessed in a multivariable analysis (MVA) Cox model in combination with clinical features identified on UVA. Features with a p-value <0.01 in the MVA models were selected to assess their pairwise correlation. Only non-highly correlated features (Pearson's correlation coefficient <0.7) were included in the final model. Leave-one-out cross-validation method was used, and the 1-year area under the receiver operating characteristic curve (AUC) was calculated for PFS and OS.

Results

Of the 166 patients (median age of 59.8 years), 114 (69%) were male, 139 (84%) were non-Asian, and 147 (89%) had an Eastern Cooperative Oncology Group (ECOG) performance status of 0-1. The median PFS and OS on treatment were 3.6 months (95% CI 2.86, 4.63) and 9 months (95% CI 7.49, 11.04), respectively. On UVA, the number of chemotherapy cycles and number of lesions at the end of treatment were associated with both PFS and OS (p < 0.001). ECOG status was associated with OS (p = 0.0063), but not PFS (p = 0.054). Of the delta-radiomic features, delta conventional HUmin, delta gray-level zone length matrix (GLZLM) GLNU, and delta GLZLM LGZE were incorporated into the model for PFS, and delta shape compacity was incorporated in the model for OS. Of the treatment/follow-up radiomic features, shape compacity and neighborhood gray-level dependence matrix (NGLDM) contrast were used in both models. The combined 1-year AUC (Kaplan-Meier estimator) was 0.82 and 0.81 for PFS and OS, respectively.

Conclusions

A combination of clinical, radiomics, and delta-radiomic features may predict PFS and OS in GEAs with reasonable accuracy.

The Role of Radiomics in Fibrosis Crohn's Disease: A Review

Inflammatory bowel disease (IBD) is a global health concern that has been on the rise in recent years. In addition, imaging is the established method of care for detecting, diagnosing, planning treatment, and monitoring the progression of IBD. While conventional imaging techniques are limited in their ability to provide comprehensive information, cross-sectional imaging plays a crucial role in the clinical management of IBD. However, accurately characterizing, detecting, and monitoring fibrosis in Crohn's disease remains a challenging task for clinicians. Recent advances in artificial intelligence technology, machine learning, computational power, and radiomic emergence have enabled the automated evaluation of medical images to generate prognostic biomarkers and quantitative diagnostics. Radiomics analysis can be achieved via deep learning algorithms or by extracting handcrafted radiomics features. As radiomic features capture pathophysiological and biological data, these quantitative radiomic features have been shown to offer accurate and rapid non-invasive tools for IBD diagnostics, treatment response monitoring, and prognosis. For these reasons, the present review aims to provide a comprehensive review of the emerging radiomics methods in intestinal fibrosis research that are highlighted and discussed in terms of challenges and advantages.

Repeatability of radiomics studies in colorectal cancer: a systematic review

BACKGROUND

Recently, radiomics has been widely used in colorectal cancer, but many variable factors affect the repeatability of radiomics research. This review aims to analyze the repeatability of radiomics studies in colorectal cancer and to evaluate the current status of radiomics in the field of colorectal cancer.

METHODS

The included studies in this review by searching from the PubMed and Embase databases. Then each study in our review was evaluated using the Radiomics Quality Score (RQS). We analyzed the factors that may affect the repeatability in the radiomics workflow and discussed the repeatability of the included studies.

RESULTS

A total of 188 studies was included in this review, of which only two (2/188, 1.06%) studies controlled the influence of individual factors. In addition, the median score of RQS was 11 (out of 36), range-1 to 27.

CONCLUSIONS

The RQS score was moderately low, and most studies did not consider the repeatability of radiomics features, especially in terms of Intra-individual, scanners, and scanning parameters. To improve the generalization of the radiomics model, it is necessary to further control the variable factors of repeatability.

Integrating Radiomics With Clinicoradiological Scoring Can Predict High-Risk Patients Who Need Surgery in Crohn's Disease: A Pilot Study

BACKGROUND

Early identification of Crohn's disease (CD) patients at risk for complications could enable targeted surgical referral, but routine magnetic resonance enterography (MRE) has not been definitively correlated with need for surgery. Our objective was to identify computer-extracted image (radiomic) features from MRE associated with risk of surgery in CD and combine them with clinical and radiological assessments to predict time to intervention.

METHODS

This was a retrospective single-center pilot study of CD patients who had an MRE within 3 months prior to initiating medical therapy. Radiomic features were extracted from annotated terminal ileum regions on MRE and combined with clinical variables and radiological assessment (via Simplified Magnetic Resonance Index of Activity scoring for wall thickening, edema, fat stranding, ulcers) in a random forest classifier. The primary endpoint was high- and low-risk groups based on need for surgery within 1 year of MRE. The secondary endpoint was time to surgery after treatment.

RESULTS

Eight radiomic features capturing localized texture heterogeneity within the terminal ileum were significantly associated with risk of surgery within 1 year of treatment (P < .05); yielding a discovery cohort area under the receiver-operating characteristic curve of 0.67 (n = 50) and validation cohort area under the receiver-operating characteristic curve of 0.74 (n = 23). Kaplan-Meier analysis of radiomic features together with clinical variables and Simplified Magnetic Resonance Index of Activity scores yielded the best hazard ratio of 4.13 (P = (7.6 × 10-6) and concordance index of 0.71 in predicting time to surgery after MRE.

CONCLUSIONS

Radiomic features on MRE may be associated with risk of surgery in CD, and in combination with clinicoradiological scoring can yield an accurate prognostic model for time to surgery.

Systematic review of the radiomics quality score applications: an EuSoMII Radiomics Auditing Group Initiative

OBJECTIVE

The main aim of the present systematic review was a comprehensive overview of the Radiomics Quality Score (RQS)-based systematic reviews to highlight common issues and challenges of radiomics research application and evaluate the relationship between RQS and review features.

METHODS

The literature search was performed on multiple medical literature archives according to PRISMA guidelines for systematic reviews that reported radiomic quality assessment through the RQS. Reported scores were converted to a 0-100% scale. The Mann-Whitney and Kruskal-Wallis tests were used to compare RQS scores and review features.

RESULTS

The literature research yielded 345 articles, from which 44 systematic reviews were finally included in the analysis. Overall, the median of RQS was 21.00% (IQR = 11.50). No significant differences of RQS were observed in subgroup analyses according to targets (oncological/not oncological target, neuroradiology/body imaging focus and one imaging technique/more than one imaging technique, characterization/prognosis/detection/other).

CONCLUSIONS

Our review did not reveal a significant difference of quality of radiomic articles reported in systematic reviews, divided in different subgroups. Furthermore, low overall methodological quality of radiomics research was found independent of specific application domains. While the RQS can serve as a reference tool to improve future study designs, future research should also be aimed at improving its reliability and developing new tools to meet an ever-evolving research space.

KEY POINTS

• Radiomics is a promising high-throughput method that may generate novel imaging biomarkers to improve clinical decision-making process, but it is an inherently complex analysis and often lacks reproducibility and generalizability. • The Radiomics Quality Score serves a necessary role as the de facto reference tool for assessing radiomics studies. • External auditing of radiomics studies, in addition to the standard peer-review process, is valuable to highlight common limitations and provide insights to improve future study designs and practical applicability of the radiomics models.

Artificial Intelligence in Medicine and Dentistry

INTRODUCTION

Artificial intelligence has been applied in various fields throughout history, but its integration into daily life is more recent. The first applications of AI were primarily in academia and government research institutions, but as technology has advanced, AI has also been applied in industry, commerce, medicine and dentistry.

OBJECTIVE

Considering that the possibilities of applying artificial intelligence are developing rapidly and that this field is one of the areas with the greatest increase in the number of newly published articles, the aim of this paper was to provide an overview of the literature and to give an insight into the possibilities of applying artificial intelligence in medicine and dentistry. In addition, the aim was to discuss its advantages and disadvantages.

CONCLUSION

The possibilities of applying artificial intelligence to medicine and dentistry are just being discovered. Artificial intelligence will greatly contribute to developments in medicine and dentistry, as it is a tool that enables development and progress, especially in terms of personalized healthcare that will lead to much better treatment outcomes.

Role of artificial intelligence in risk prediction, prognostication, and therapy response assessment in colorectal cancer: current state and future directions

Artificial Intelligence (AI) is a branch of computer science that utilizes optimization, probabilistic and statistical approaches to analyze and make predictions based on a vast amount of data. In recent years, AI has revolutionized the field of oncology and spearheaded novel approaches in the management of various cancers, including colorectal cancer (CRC). Notably, the applications of AI to diagnose, prognosticate, and predict response to therapy in CRC, is gaining traction and proving to be promising. There have also been several advancements in AI technologies to help predict metastases in CRC and in Computer-Aided Detection (CAD) Systems to improve miss rates for colorectal neoplasia. This article provides a comprehensive review of the role of AI in predicting risk, prognosis, and response to therapies among patients with CRC.

The combination of radiomics features and VASARI standard to predict glioma grade

Background and Purpose

Radiomics features and The Visually AcceSAble Rembrandt Images (VASARI) standard appear to be quantitative and qualitative evaluations utilized to determine glioma grade. This study developed a preoperative model to predict glioma grade and improve the efficacy of clinical strategies by combining these two assessment methods.

Materials and Methods

Patients diagnosed with glioma between March 2017 and September 2018 who underwent surgery and histopathology were enrolled in this study. A total of 3840 radiomic features were calculated; however, using the least absolute shrinkage and selection operator (LASSO) method, only 16 features were chosen to generate a radiomic signature. Three predictive models were developed using radiomic features and VASARI standard. The performance and validity of models were evaluated using decision curve analysis and 10-fold nested cross-validation.

Results

Our study included 102 patients: 35 with low-grade glioma (LGG) and 67 with high-grade glioma (HGG). Model 1 utilized both radiomics and the VASARI standard, which included radiomic signatures, proportion of edema, and deep white matter invasion. Models 2 and 3 were constructed with radiomics or VASARI, respectively, with an area under the receiver operating characteristic curve (AUC) of 0.937 and 0.831, respectively, which was less than that of Model 1, with an AUC of 0.966.

Conclusion

The combination of radiomics features and the VASARI standard is a robust model for predicting glioma grades.

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.

Preoperative computed tomography-based tumoral radiomic features prediction for overall survival in resectable non-small cell lung cancer

Objectives

The purpose of this study was to evaluate whether preoperative radiomics features could meliorate risk stratification for the overall survival (OS) of non-small cell lung cancer (NSCLC) patients.

Methods

After rigorous screening, the 208 NSCLC patients without any pre-operative adjuvant therapy were eventually enrolled. We segmented the 3D volume of interest (VOI) based on malignant lesion of computed tomography (CT) imaging and extracted 1542 radiomics features. Interclass correlation coefficients (ICC) and LASSO Cox regression analysis were utilized to perform feature selection and radiomics model building. In the model evaluation phase, we carried out stratified analysis, receiver operating characteristic (ROC) curve, concordance index (C-index), and decision curve analysis (DCA). In addition, integrating the clinicopathological trait and radiomics score, we developed a nomogram to predict the OS at 1 year, 2 years, and 3 years, respectively.

Results

Six radiomics features, including gradient_glcm_InverseVariance, logarithm_firstorder_Median, logarithm_firstorder_RobustMeanAbsoluteDeviation, square_gldm_LargeDependenceEmphasis, wavelet_HLL_firstorder_Kurtosis, and wavelet_LLL_firstorder_Maximum, were selected to construct the radiomics signature, whose areas under the curve (AUCs) for 3-year prediction reached 0.857 in the training set (n=146) and 0.871 in the testing set (n=62). The results of multivariate analysis revealed that the radiomics score, radiological sign, and N stage were independent prognostic factors in NSCLC. Moreover, compared with clinical factors and the separate radiomics model, the established nomogram exhibited a better performance in predicting 3-year OS.

Conclusions

Our radiomics model may provide a promising non-invasive approach for preoperative risk stratification and personalized postoperative surveillance for resectable NSCLC patients.

Preliminary Evaluation of Artificial Intelligence-Based Anti-Hepatocellular Carcinoma Molecular Target Study in Hepatocellular Carcinoma Diagnosis Research

In this paper, in-depth research analysis of anti-hepatocellular carcinoma molecular targets for hepatocellular carcinoma diagnosis was conducted using artificial intelligence. Because BRD4 plays an important role in gene transcription for cell cycle regulation and apoptosis, tumor-targeted therapy by inhibiting the expression or function of BRD4 has received increasing attention in the field of antitumor research. Study subjects in small samples were used as the validation set for validating each diagnostic model constructed based on the training set. The diagnostic effect of each model in the validation set is evaluated by calculating the sensitivity, specificity, and compliance rate, and the model with the best and most stable diagnostic value is selected by combining the results of model construction, validation, and evaluation. The total sample was divided into a training set and test set by using a stratified sampling method in the ratio of 7 : 3. Logistic regression, weighted k-nearest neighbor, decision tree, and BP artificial neural network were used in the training set to construct diagnostic models for early-stage liver cancer, respectively, and the optimal parameters of the corresponding models were obtained, and then, the constructed models were validated in the test set. To evaluate the diagnostic efficacy, stability, and generalization ability of the four classification methods more robustly, a 10-fold crossover test was performed for each classification method. BRD4 is an epigenetic regulator that is associated with the upregulation of expression of various oncogenic drivers in tumors. Targeting BRD4 with pharmacological inhibitors has emerged as a novel approach for tumor treatment. However, before we implemented this topic, there were no detailed studies on whether BRD4 could be used for the treatment of HCC, the role of BRD4 in HCC cell proliferation and apoptosis, and the ability of small molecule BRD4 inhibitors to induce apoptosis in hepatocellular carcinoma cells.

Pre-Treatment Computed Tomography Radiomics for Predicting the Response to Neoadjuvant Chemoradiation in Locally Advanced Rectal Cancer: A Retrospective Study

Background and Purpose

Computerized tomography (CT) scans are commonly performed to assist in diagnosis and treatment of locally advanced rectal cancer (LARC). This study assessed the usefulness of pretreatment CT-based radiomics for predicting pathological complete response (pCR) of LARC to neoadjuvant chemoradiotherapy (nCRT).

Materials and Methods

Patients with LARC who underwent nCRT followed by total mesorectal excision surgery from July 2010 to December 2018 were enrolled in this retrospective study. A total of 340 radiomic features were extracted from pretreatment contrast-enhanced CT images. The most relevant features to pCR were selected using the least absolute shrinkage and selection operator (LASSO) method and a radiomic signature was generated. Predictive models were built with radiomic features and clinico-pathological variables. Model performance was assessed with decision curve analysis and was validated in an independent cohort.

Results

The pCR was achieved in 44 of the 216 consecutive patients (20.4%) in this study. The model with the best performance used both radiomics and clinical variables including radiomic signatures, distance to anal verge, lymphocyte-to-monocyte ratio, and carcinoembryonic antigen. This combined model discriminated between patients with and without pCR with an area under the curve of 0.926 and 0.872 in the training and the validation cohorts, respectively. The combined model also showed better performance than models built with radiomic or clinical variables alone.

Conclusion

Our combined predictive model was robust in differentiating patients with and without response to nCRT.

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

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

Comparative study of radiomics, tumor morphology, and clinicopathological factors in predicting overall survival of patients with rectal cancer before surgery

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Radiomics analysis of pretreatment MR images could predict overall survival (OS) in patients.

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Clinical, pathological and MRI imaging indexes were included and models were established. Tumor morphological model, clinicopathological model, radiomics model and comprehensive model were used to evaluate the prognosis of patients with rectal cancer. It can explore the influence of the above factors on the prognosis of rectal cancer from multi-level and multi-angle.

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The proposed radiomics nomogram showed better prognostic performance than the clinicopathological and imaging model in risk stratification and can classify patients into high- and low-risk groups with significant differences in OS.

We compared the ability of a radiomics model, morphological imaging model, and clinicopathological risk model to predict 3-year overall survival (OS) in 206 patients with rectal cancer who underwent radical surgery and had magnetic resonance imaging, clinicopathological, and OS data available. The patients were randomized to a training cohort (n = 146) and a verification cohort (n = 60). Radiomics features were extracted from preoperative T2-weighted images, and a radiomics score model was constructed. Factors that were significant in the Cox multivariate analysis were used to construct the final morphological tumor model and clinicopathological model. A comprehensive model in the form of a line chart was established by combining the three models. Ten radiomics features significantly related to OS were selected to construct the radiomics feature model and calculate the radiomics score. In the morphological model, mesorectal extension depth and distance between the lower tumor margin and the anal margin were significant prognostic factors. N stage was the only significant clinicopathological factor. The comprehensive model combined with the above factors had the best prediction performance for OS. The C-index had a predictive performance of 0.872 (95% confidence interval [CI]: 0.832–0.912) in the training cohort and 0.944 (95% CI: 0.890–0.990) in the verification cohort, which was better than for any single model. The comprehensive model was divided into high-risk and low-risk groups. Kaplan-Meier curve analysis showed that all factors were significantly correlated with poor OS in the high-risk group. A comprehensive nomogram based on multi-model radiomics features can predict 3-year OS after rectal cancer surgery.

Imaging-based Machine-learning Models to Predict Clinical Outcomes and Identify Biomarkers in Pancreatic Cancer: A Scoping Review

OBJECTIVE

To perform a scoping review of imaging-based machine-learning models to predict clinical outcomes and identify biomarkers in patients with PDAC.

SUMMARY OF BACKGROUND DATA

Patients with PDAC could benefit from better selection for systemic and surgical therapy. Imaging-based machine-learning models may improve treatment selection.

METHODS

A scoping review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses-scoping review guidelines in the PubMed and Embase databases (inception-October 2020). The review protocol was prospectively registered (open science framework registration: m4cyx). Included were studies on imaging-based machine-learning models for predicting clinical outcomes and identifying biomarkers for PDAC. The primary outcome was model performance. An area under the curve (AUC) of ≥0.75, or a P-value of ≤0.05, was considered adequate model performance. Methodological study quality was assessed using the modified radiomics quality score.

RESULTS

After screening 1619 studies, 25 studies with 2305 patients fulfilled the eligibility criteria. All but 1 study was published in 2019 and 2020. Overall, 23/25 studies created models using radiomics features, 1 study quantified vascular invasion on computed tomography, and one used histopathological data. Nine models predicted clinical outcomes with AUC measures of 0.78-0.95, and C-indices of 0.65-0.76. Seventeen models identified biomarkers with AUC measures of 0.68-0.95. Adequate model performance was reported in 23/25 studies. The methodological quality of the included studies was suboptimal, with a median modified radiomics quality score score of 7/36.

CONCLUSIONS

The use of imaging-based machine-learning models to predict clinical outcomes and identify biomarkers in patients with PDAC is increasingly rapidly. Although these models mostly have good performance scores, their methodological quality should be improved.

Rectal MRI radiomics for predicting pathological complete response: Where we are

Radiomics using rectal MRI radiomics has emerged as a promising approach in predicting pathological complete response. In this study, we present a typical pipeline of a radiomics analysis and review recent studies, exploring applications, development of radiomics methodologies and model construction in pCR prediction. Finally, we will offer our opinion about the future and discuss the next steps of rectal MRI radiomics for predicting pCR.

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.

Vertebral MRI-based radiomics model to differentiate multiple myeloma from metastases: influence of features number on logistic regression model performance

OBJECTIVES

This study aimed to use the most frequent features to establish a vertebral MRI-based radiomics model that could differentiate multiple myeloma (MM) from metastases and compare the model performance with different features number.

METHODS

We retrospectively analyzed conventional MRI (T1WI and fat-suppression T2WI) of 103 MM patients and 138 patients with metastases. The feature selection process included four steps. The first three steps defined as conventional feature selection (CFS), carried out 50 times (ten times with 5-fold cross-validation), included variance threshold, SelectKBest, and least absolute shrinkage and selection operator. The most frequent fixed features were selected for modeling during the last step. The number of events per independent variable (EPV) is the number of patients in a smaller subgroup divided by the number of radiomics features considered in developing the prediction model. The EPV values considered were 5, 10, 15, and 20. Therefore, we constructed four models using the top 16, 8, 6, and 4 most frequent features, respectively. The models constructed with features selected by CFS were also compared.

RESULTS

The AUCs of 20EPV-Model, 15EPV-Model, and CSF-Model (AUC = 0.71, 0.81, and 0.78) were poor than 10EPV-Model (AUC = 0.84, p < 0.001). The AUC of 10EPV-Model was comparable with 5EPV-Model (AUC = 0.85, p = 0.480).

CONCLUSIONS

The radiomics model constructed with an appropriate small number of the most frequent features could well distinguish metastases from MM based on conventional vertebral MRI. Based on our results, we recommend following the 10 EPV as the rule of thumb for feature selection.

KEY POINTS

• The developed radiomics model could distinguish metastases from multiple myeloma based on conventional vertebral MRI. • An accurate model based on just a handful of the most frequent features could be constructed by utilizing multiple feature reduction techniques. • An event per independent variable value of 10 is recommended as a rule of thumb for modeling feature selection.

A predictive clinical-radiomics nomogram for diagnosing of axial spondyloarthritis using MRI and clinical risk factors

OBJECTIVES

Construct and validate a nomogram model integrating the radiomics features and the clinical risk factors to differentiating axial spondyloarthritis (axSpA) in low back pain patients undergone sacroiliac joint (SIJ)- magnetic resonance imaging (MRI).

METHODS

638 patients confirmed as axSpA (n= 424) or non-axSpA (n = 214) who were randomly divided into training (n = 447) and validation cohorts (n = 191). Optimal radiomics signatures were constructed from the 3.0T SIJ-MRI using maximum relevance-minimum redundancy (mRMR) and the least absolute shrinkage and selection operator (LASSO) algorithm in the training cohort. We also included six clinical risk predictors to build clinical model. Incorporating the independent clinical factors and Rad-score, a nomogram model was constructed by multivariable logistic regression analysis. The performance of the clinical, Rad-score, and nomogram model were evaluated by ROC analysis, calibration curve and decision curve analysis (DCA).

RESULTS

1316 features were extracted and reduced to 15 features to build the Rad-score. The Rad-score allowed a good discrimination in the training (AUC, 0.82; 95% CI, 0.77-0.86) and the validation cohort (AUC, 0.82; 95% CI, 0.76-0.88). The clinical-radiomics nomogram model also showed favorable discrimination in the training (AUC, 0.90; 95% CI, 0.86-0.93) and the validation cohort (AUC, 0.90; 95% CI, 0.85-0.94). Calibration curves (p > 0.05) and DCA demonstrated the nomogram was useful for axSpA diagnosis in the clinical environment.

CONCLUSION

The study proposed a radiomics model was able to separate axSpA and non-axSpA. The clinical-radiomics nomogram can increase the efficacy for differentiating axSpA, which might facilitate clinical decision-making process.

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.

Radiomics and Prostate MRI: Current Role and Future Applications

Multiparametric prostate magnetic resonance imaging (mpMRI) is widely used as a triage test for men at a risk of prostate cancer. However, the traditional role of mpMRI was confined to prostate cancer staging. Radiomics is the quantitative extraction and analysis of minable data from medical images; it is emerging as a promising tool to detect and categorize prostate lesions. In this paper we review the role of radiomics applied to prostate mpMRI in detection and localization of prostate cancer, prediction of Gleason score and PI-RADS classification, prediction of extracapsular extension and of biochemical recurrence. We also provide a future perspective of artificial intelligence (machine learning and deep learning) applied to the field of prostate cancer.