A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort study

Pushing the boundaries of radiotherapy-immunotherapy combinations: highlights from the 7th immunorad conference

Over the last decade, the annual Immunorad Conference, held under the joint auspicies of Gustave Roussy (Villejuif, France) and the Weill Cornell Medical College (New-York, USA) has aimed at exploring the latest advancements in the fields of tumor immunology and radiotherapy-immunotherapy combinations for the treatment of cancer. Gathering medical oncologists, radiation oncologists, physicians and researchers with esteemed expertise in these fields, the Immunorad Conference bridges the gap between preclinical outcomes and clinical opportunities. Thus, it paves a promising way toward optimizing radiotherapy-immunotherapy combinations and, from a broader perspective, improving therapeutic strategies for patients with cancer. Herein, we report on the topics developed by key-opinion leaders during the 7th Immunorad Conference held in Paris-Les Cordeliers (France) from September 27th to 29th 2023, and set the stage for the 8th edition of Immunorad which will be held at Weill Cornell Medical College (New-York, USA) in October 2024.

Combination of radiotherapy and ICIs in advanced hepatocellular carcinoma: A systematic review of current evidence and future prospects (Review)

Hepatocellular carcinoma (HCC) is a global health concern because of its rising prevalence and high fatality rates. Conventional treatments for advanced HCC (aHCC) have limited success, emphasizing the need for novel treatment options. Radiotherapy (RT) treatments, such as stereotactic body radiation and proton therapy, improve local tumor management via precision targeting. Moreover, immune checkpoint inhibitors (ICIs) that target the programmed cell death protein 1(PD-1)/PD ligand 1 (PD-L1) and cytotoxic T lymphocyte associated protein 4 (CTLA-4) pathways have promise for systemic antitumor effectiveness. The combination of RT and ICIs takes advantage of their complementary mechanisms: RT kills immunogenic cells and controls the tumor microenvironment to increase antigen presentation, whereas ICIs enhance and maintain antitumor immune responses. This combination enhances tumor regression and immune response in aHCC, improving response rate and progression-free survival with manageable safety. The present review aimed to summarize the rationale for combining RT + ICIs in patients with aHCC and clinical outcomes, as well as ways to enhance this combination technique. The combination of these models is a promising technique for improving outcomes for patients with aHCC and warrants further investigation.

Renal Transplant Survival Prediction From Unsupervised Deep Learning-Based Radiomics on Early Dynamic Contrast-Enhanced MRI

RATIONALE AND OBJECTIVES

End-stage renal disease is characterized by an irreversible decline in kidney function. Despite a risk of chronic dysfunction of the transplanted kidney, renal transplantation is considered the most effective solution among available treatment options. Clinical attributes of graft survival prediction, such as allocation variables or results of pathological examinations, have been widely studied. Nevertheless, medical imaging is clinically used only to assess current transplant status. This study investigated the use of unsupervised deep learning-based algorithms to identify rich radiomic features that may be linked to graft survival from early dynamic contrast-enhanced magnetic resonance imaging data of renal transplants.

MATERIALS AND METHODS

A retrospective cohort of 108 transplanted patients (mean age 50 +/- 15, 67 men) undergoing systematic magnetic resonance imaging follow-up examinations (2013 to 2015) was used to train deep convolutional neural network models based on an unsupervised contrastive learning approach. 5-year graft survival analysis was performed from the obtained artificial intelligence radiomics features using penalized Cox models and Kaplan-Meier estimates.

RESULTS

Using a validation set of 48 patients (mean age 54 +/- 13, 30 men) having 1-month post-transplantation magnetic resonance imaging examinations, the proposed approach demonstrated promising 5-year graft survival capability with a 72.7% concordance index from the artificial intelligence radiomics features. Unsupervised clustering of these radiomics features enabled statistically significant stratification of patients (p=0.029).

CONCLUSION

This proof-of-concept study exposed the promising capability of artificial intelligence algorithms to extract relevant radiomics features that enable renal transplant survival prediction. Further studies are needed to demonstrate the robustness of this technique, and to identify appropriate procedures for integration of such an approach into multimodal and clinical settings.

Investigating tumor immunogenicity as a determinant of differential abscopal effects

This study investigated the role of tumor immunogenicity in the ionizing radiation (IR)-induced abscopal effect. The ovalbumin-expressing B16 cell line (B16-OVA) served as a relatively immunogenic tumor model compared to the B16F10 cell line. C57BL/6 mice were implanted with B16-OVA or B16F10 in the left thigh as the primary tumor and B16F10 in the right thigh as the secondary tumor to evaluate the abscopal response. IR was applied solely to the primary tumor, followed by administration of isotype or anti-programmed cell death protein-1 (PD-1) antibodies. Tumor-infiltrating immune cells were analyzed using flow cytometry. B16-OVA tumors exhibited increased T-cell infiltration and elevated granzyme B and Ki-67 expression in CD8+ T cells compared to B16F10 tumors. IR delayed secondary tumor growth in B16-OVA-irradiated mice, but not in B16F10-irradiated mice. While CD8+ T-cell numbers increased in the secondary tumors of both groups, regulatory T cells significantly increased only in B16F10-irradiated mice. IR promoted differentiation from stem-like TCF1+TIM3- to effector-like TCF1-TIM3+ CD8+ T cells, with elevated granzyme B expression. Polyfunctional T cells co-expressing IFN-γ, TNF-α and IL-2 were significantly increased only in secondary tumors of B16-OVA-irradiated mice under PD-1 blockade. The abscopal effect was abolished by FTY720 treatment and CD8+ T-cell depletion. In conclusion, the IR-induced abscopal effect was dependent on the immunogenicity of the irradiated tumor. The findings may have implication on enhancing abscopal effect in clinical settings.

MMRNet: Ensemble deep learning models for predicting mismatch repair deficiency in endometrial cancer from histopathological images

Combining molecular classification with clinicopathologic methods improves risk assessment and chooses therapies for endometrial cancer (EC). Detecting mismatch repair (MMR) deficiencies in EC is crucial for screening Lynch syndrome and identifying immunotherapy candidates. An affordable and accessible tool is urgently needed to determine MMR status in EC patients. We introduce MMRNet, a deep convolutional neural network designed to predict MMR-deficient EC from whole-slide images stained with hematoxylin and eosin. MMRNet demonstrates strong performance, achieving an average area under the receiver operating characteristic curve (AUROC) of 0.897, with a sensitivity of 0.628 and a specificity of 0.949 in internal cross-validation. External validation using three additional datasets results in AUROCs of 0.790, 0.807, and 0.863. Employing a human-machine fusion approach notably improves diagnostic accuracy. MMRNet presents an effective method for identifying EC cases for confirmatory MMR testing and may assist in selecting candidates for immunotherapy.

Computed tomography-based deep learning radiomics model for preoperative prediction of tumor immune microenvironment in colorectal cancer

BACKGROUND

Colorectal cancer (CRC) is a leading cause of cancer-related death globally, with the tumor immune microenvironment (TIME) influencing prognosis and immunotherapy response. Current TIME evaluation relies on invasive biopsies, limiting its clinical application. This study hypothesized that computed tomography (CT)-based deep learning (DL) radiomics models can non-invasively predict key TIME biomarkers: Tumor-stroma ratio (TSR), tumor-infiltrating lymphocytes (TILs), and immune score (IS).

AIM

To develop a non-invasive DL approach using preoperative CT radiomics to evaluate TIME components in CRC patients.

METHODS

In this retrospective study, preoperative CT images of 315 pathologically confirmed CRC patients (220 in training cohort and 95 in validation cohort) were analyzed. Manually delineated regions of interest were used to extract DL features. Predictive models (DenseNet-121/169) for TSR, TILs, IS, and TIME classification were constructed. Performance was evaluated via receiver operating characteristic curves, calibration curves, and decision curve analysis (DCA).

RESULTS

The DL-DenseNet-169 model achieved area under the curve (AUC) values of 0.892 [95% confidence interval (CI): 0.828-0.957] for TSR and 0.772 (95%CI: 0.674-0.870) for TIME score. The DenseNet-121 model yielded AUC values of 0.851 (95%CI: 0.768-0.933) for TILs and 0.852 (95%CI: 0.775-0.928) for IS. Calibration curves demonstrated strong prediction-observation agreement, and DCA confirmed clinical utility across threshold probabilities (P < 0.05 for all models).

CONCLUSION

CT-based DL radiomics provides a reliable non-invasive method for preoperative TIME evaluation, enabling personalized immunotherapy strategies in CRC management.

Deep Reinforcement Learning for CT-Based Non-Invasive Prediction of SOX9 Expression in Hepatocellular Carcinoma

Background: The transcription factor SOX9 plays a critical role in various diseases, including hepatocellular carcinoma (HCC), and has been implicated in resistance to sorafenib treatment. Accurate assessment of SOX9 expression is important for guiding personalized therapy in HCC patients; however, a reliable non-invasive method for evaluating SOX9 status remains lacking. This study aims to develop a deep learning (DL) model capable of preoperatively and non-invasively predicting SOX9 expression from CT images in HCC patients. Methods: We retrospectively analyzed a dataset comprising 4011 CT images from 101 HCC patients who underwent surgical resection followed by sorafenib therapy at West China Hospital, Sichuan University. A deep reinforcement learning (DRL) approach was proposed to enhance prediction accuracy by identifying and focusing on image regions highly correlated with SOX9 expression, thereby reducing the impact of background noise. Results: Our DRL-based model achieved an area under the curve (AUC) of 91.00% (95% confidence interval: 88.64-93.15%), outperforming conventional DL methods by over 10%. Furthermore, survival analysis revealed that patients with SOX9-positive tumors had significantly shorter recurrence-free survival (RFS) and overall survival (OS) compared to SOX9-negative patients, highlighting the prognostic value of SOX9 status. Conclusions: This study demonstrates that a DRL-enhanced DL model can accurately and non-invasively predict SOX9 expression in HCC patients using preoperative CT images. These findings support the clinical utility of imaging-based SOX9 assessment in informing treatment strategies and prognostic evaluation for patients with advanced HCC.

Ultrasound-based deep learning radiomics for enhanced axillary lymph node metastasis assessment: a multicenter study

BACKGROUND

Accurate preoperative assessment of axillary lymph node metastasis (ALNM) in breast cancer is crucial for guiding treatment decisions. This study aimed to develop a deep-learning radiomics model for assessing ALNM and to evaluate its impact on radiologists' diagnostic accuracy.

METHODS

This multicenter study included 866 breast cancer patients from 6 hospitals. The data were categorized into training, internal test, external test, and prospective test sets. Deep learning and handcrafted radiomics features were extracted from ultrasound images of primary tumors and lymph nodes. The tumor score and LN score were calculated following feature selection, and a clinical-radiomics model was constructed based on these scores along with clinical-ultrasonic risk factors. The model's performance was validated across the 3 test sets. Additionally, the diagnostic performance of radiologists, with and without model assistance, was evaluated.

RESULTS

The clinical-radiomics model demonstrated robust discrimination with AUCs of 0.94, 0.92, 0.91, and 0.95 in the training, internal test, external test, and prospective test sets, respectively. It surpassed the clinical model and single score in all sets (P < .05). Decision curve analysis and clinical impact curves validated the clinical utility of the clinical-radiomics model. Moreover, the model significantly improved radiologists' diagnostic accuracy, with AUCs increasing from 0.71 to 0.82 for the junior radiologist and from 0.75 to 0.85 for the senior radiologist.

CONCLUSIONS

The clinical-radiomics model effectively predicts ALNM in breast cancer patients using noninvasive ultrasound features. Additionally, it enhances radiologists' diagnostic accuracy, potentially optimizing resource allocation in breast cancer management.

Radiomic markers associated with clinical benefit in patients with radiographic progression of advanced uveal melanoma on tebentafusp

STUDY AIM

Tebentafusp, a bispecific fusion protein consisting of affinity-enhanced T cell receptor fused to anti-CD3 effector, has shown overall survival (OS) benefits across all RECIST response categories, including progressive disease (PD). In a phase 2 trial (NCT02570308) for advanced uveal melanoma (mUM), 35 % of PD patients experienced ≥ 0.5 log ctDNA reduction, resulting in a median overall survival (OS) of ∼17 months, compared to ∼8 months in the non-ctDNA reduction group.

METHODS

A total of 34 of 127 2L+ mUM patients with PD were split into two groups based on absence or presence of ctDNA reduction (≥0.5 log reduction). Lesions from CT and MRI scans were analyzed using radiomics features at baseline and week eight, yielding two machine-learning-derived patient signatures (16 features). Performance of per-patient analysis (n = 32) and per-lesion analysis (n = 148) was assessed using ROC AUC (95 % confidence interval [CI]).

RESULTS

In the per-patient analysis, a volumetric signature classified patients into groups with ROC AUC of 0.71 [0.53-0.90] with 63 % specificity and 81 % sensitivity at the optimal threshold (0.57). In the per-lesion analysis, a radiomic signature reached an ROC AUC of 0.70 [0.58-0.81] with 66 % specificity and 74 % sensitivity at the optimal threshold (0.53). Group B had lower baseline tumor lesion volume (ROC AUC=0.65), distinct baseline (ROC AUC=0.66), and change by week eight (ROC AUC=0.66/0.69 on CT/MRI) in tumor heterogeneity.

CONCLUSION

Radiomic analysis accurately predicted ctDNA reduction in PD patients at both the patient and lesion level. The most influential predictor was decreased tumor heterogeneity observed on CT/MRI.

Artificial Intelligence for Drug Discovery: An Update and Future Prospects

Artificial intelligence (AI) has become a pivotal tool for medical image analysis, significantly enhancing drug discovery through improved diagnostics, staging, prognostication, and response assessment. At a high level, AI-driven image analysis enables the quantification and synthesis of previously qualitative imaging characteristics, facilitating the identification of novel disease-specific biomarkers, patient risk stratification, prognostication, and adverse event prediction. In addition, AI can assist in response assessment by capturing changes in imaging "phenotype" over time, allowing for optimized treatment plans based on real-time analysis. Integrating this emerging technology into drug discovery pipelines has the potential to accelerate the identification and development of new pharmaceuticals by assisting in target identification and patient selection, as well as reducing the incidence, and therefore cost, of failed trials through high-throughput, reproducible, and data-driven insights. Continued progress in AI applications will shape the future of medical imaging, ultimately fostering more efficient, accurate, and tailored drug discovery processes. Herein, we offer a comprehensive overview of how AI enhances medical imaging to inform drug development and therapeutic strategies.

Interpretable CT Radiomics-based Machine Learning Model for Preoperative Prediction of Ki-67 Expression in Clear Cell Renal Cell Carcinoma

RATIONALE AND OBJECTIVES

To develop and externally validate interpretable CT radiomics-based machine learning (ML) models for preoperative Ki-67 expression prediction in clear cell renal cell carcinoma (ccRCC).

METHODS

506 patients were retrospectively enrolled from three independent institutes and divided into the training (n=357) and external test (n=149) sets. Ki67 expression was determined by immunohistochemistry (IHC) and categorized into low (<15%) and high (≥15%) expression groups. Radiomics features were extracted from segmented tumor regions in the corticomedullary phase (CMP) CT images using the "PyRadiomics" package. The least absolute shrinkage and selection operator (LASSO) regression was applied to select the most relevant radiomics features for Ki-67 expression, which were then used to train five ML models. Models' performances were evaluated via the receiving operator curve analysis and compared using Delong test. Calibration and decision curve analyses assessed the models' clinical utility. Kaplan-Meier analysis and Log-rank tests were conducted to determine the prognostic value of radiomics-predicted Ki-67 expression status. The optimal model was interpreted using SHapley Additive exPlanations (SHAP).

RESULTS

Eight radiomics feature were selected to build models using Random forest (RF), eXtreme Gradient Boosting (XGBoost), Logistic regression (LR), Support vector machine (SVM), and K-nearest neighbor (KNN). The RF model exhibited the best performance, achieving the highest area under the curve (AUC) in both the training (0.910, 95% confidence interval [CI]: 0.881-0.936) and external test (0.885, 95% CI: 0.826-0.934) sets, as confirmed by Delong test (all P values＜0.05). Calibration and decision curves further demonstrated the superior clinical utility of the RF model. Both IHC-based and RF-predicted high Ki-67 expression groups were significantly associated with a higher risk of tumor recurrence in the training and external test sets (all P values＜0.05). The prediction process of the RF model was uncovered in the globe and individualized terms using the SHAP.

CONCLUSION

The interpretable CT radiomics-based RF classifier exhibited robust predictive performance in assessing Ki-67 expression levels preoperatively, offering valuable prognostic insights and aiding clinical decision-making in ccRCC patients.

Electroporation-mediated novel albumin-fused Flt3L DNA delivery promotes cDC1-associated anticancer immunity

Dendritic cells (DCs) constitute a distinct type of immune cell found within tumors, serving a central role in mediating tumor antigen-specific immunity against cancer cells. Frequently, DC functions are dysregulated by the immunosuppressive signals present within the tumor microenvironment (TME). Consequently, DC manipulation holds great potential to enhance the cytotoxic T cell response against cancer diseases. One strategy involves administering Fms-like tyrosine kinase receptor 3 ligand (Flt3L), a vitally important cytokine for DC development. In this current study, the electroporation-mediated delivery of a novel albumin-fused Flt3L DNA (alb-Flt3L DNA) demonstrated the ability to induce an anti-tumor immune response. This albumin fusion construct possesses more persistent bioactivity in targeted organs. Furthermore, TC-1-bearing-C57BL/6 mice receiving alb-Flt3L DNA treatment presented better tumor control and superior survival. Cellular analysis revealed that alb-Flt3L DNA administration promoted robust DC and cDC1 expansion. In addition, increased levels of IFN-γ-secreting CD8+ lymphocytes were found in correlation to greater cDC1 population. Moreover, the toxicity of alb-Flt3L administration is limited. Collectively, our data showcases a novel DC-based immunotherapy using electroporation to administer alb-Flt3L DNA.

Different MRI-based radiomics machine learning models to predict CD3+ tumor-infiltrating lymphocytes in rectal cancer

Objectives

This study aimed to develop and evaluate multiple machine learning models utilizing contrast-enhanced T1-weighted imaging (T1-CE) to differentiate between low-/high-infiltration of total T lymphocytes (CD3) in patients with rectal cancer.

Methods

We retrospectively selected 157 patients (103 men, 54 women) with pathologically confirmed rectal cancer diagnosed between March 2015 and October 2019. The cohort was randomly divided into a training dataset (n=109) and a test dataset (n=48) for subsequent analysis. Seven radiomic features were selected to generate three models: logistic regression (LR), random forest (RF), and support vector machine (SVM). The diagnostic performance of the three models was compared using the DeLong test. Additionally, Kaplan-Meier analysis was employed to assess disease-free survival (DFS) in patients with high and low CD3+ tumor-infiltrating lymphocyte (TIL) density.

Results

The three radiomics models performed well in predicting the infiltration of CD3+ TILS, with area under the curve (AUC) values of 0.871, 0.982, and 0.913, respectively, in the training set for the LR, RF, and SVM models. In the validation set, the corresponding AUC values were 0.869, 0.794, and 0.837, respectively. Among the radiomics models, the LR model exhibited superior diagnostic performance and robustness. The merged model, which integrated radiomics features from the SVM model and clinical features from the clinical model, outperformed the individual radiomics models, with AUCs of 0.8932 and 0.8829 in the training and test cohorts, respectively. Additionally, a lower expression level of CD3+ TILs in the cohort was independently correlated with DFS (P = 0.0041).

Conclusion

The combined model demonstrated a better discriminatory ability in assessing the abundance of CD3+ TILs in rectal cancer. Furthermore, the expression of CD3+ TILs was significantly correlated with DFS, highlighting its potential prognostic value.

Advances in knowledge

This study is the first attempt to compare the predictive TILs performance of three machine learning models, LR, RF, and SVM, based on the combination of radiomics and immunohistochemistry. The MRI-based combined model, composed of radiomics features from the SVM model and clinical features from the clinical model, exhibited better discriminatory capability for the expression of CD3+ TILs in rectal cancer.

Deep learning for malignant lymph node segmentation and detection: a review

Radiation therapy remains a cornerstone in the treatment of cancer, with the delineation of Organs at Risk (OARs), tumors, and malignant lymph nodes playing a critical role in the planning process. However, the manual segmentation of these anatomical structures is both time-consuming and costly, with inter-observer and intra-observer variability often leading to delineation errors. In recent years, deep learning-based automatic segmentation has gained increasing attention, leading to a proliferation of scholarly works on OAR and tumor segmentation algorithms utilizing deep learning techniques. Nevertheless, similar comprehensive reviews focusing solely on malignant lymph nodes are scarce. This paper provides an in-depth review of the advancements in deep learning for malignant lymph node segmentation and detection. After a brief overview of deep learning methodologies, the review examines specific models and their outcomes for malignant lymph node segmentation and detection across five clinical sites: head and neck, upper extremity, chest, abdomen, and pelvis. The discussion section extensively covers the current challenges and future trends in this field, analyzing how they might impact clinical applications. This review aims to bridge the gap in literature by providing a focused overview on deep learning applications in the context of malignant lymph node challenges, offering insights into their potential to enhance the precision and efficiency of cancer treatment planning.

Dendrimer Conjugates with PD-L1-Binding Peptides Enhance In Vivo Antitumor Immune Response

Peptides are an emerging class of biologics for cancer immunotherapy; however, their clinical translation is hindered by poor binding kinetics, bioavailability, and short plasma half-life compared to their corresponding antibodies. Nanoparticles present potential solutions but face scale-up difficulties due to complexity. Here, a translatable, modular nanoparticle scaffold is presented for peptide-based immune checkpoint inhibitors (ICIs). This platform is based on a simple structure of generation 7 (G7) poly(amidoamine) (PAMAM) dendrimers conjugated with engineered peptides (dendrimer-peptide conjugates, DPCs). DPCs functionalized with multiple copies of a programmed death-ligand 1 (PD-L1)-binding peptide exhibited significantly enhanced avidity-based binding kinetics and in vitro specificity, in addition to the substantially prolonged plasma half-life in vivo. Notably, a series of in vivo experiments revealed that DPCs displayed selective tumor accumulation and high efficacy, without apparent toxicity, when applied to a syngeneic mouse model bearing mouse oral carcinoma (MOC1) tumors. The results indicate that the DPC platform significantly improves the antagonistic effect and in vivo behaviors of the PD-L1-binding peptides, which can be potentially applied to virtually any peptide-based ICIs. The DPC platform's simplicity and modular nature will likely increase the potential of its clinical translation and ultimately enable precision/personalized cancer immunotherapy.

Machine learning based radiomic models outperform clinical biomarkers in predicting outcomes after immunotherapy for hepatocellular carcinoma

BACKGROUND

Atezolizumab plus bevacizumab (A/B) is a first-line therapy for unresectable hepatocellular carcinoma (HCC). Only a small proportion of patients respond to treatment. This study integrated radiomic and clinical data derived from routine pre-treatment imaging to predict outcomes after immunotherapy.

METHODS

152 patients from two international centres receiving A/B were retrospectively reviewed. Deep learning autosegmentation generated whole liver masks from pre-treatment CTs. Radiomic features combined with clinical variables were used to predict 12-month mortality post A/B. Radiomic and integrated radiomic-clinical models were developed using 7 machine learning models in combination with 13 feature selection techniques in the Imperial College London (ICL) cohort. K-means clustering identified high- and low-risk groups and predicted overall survival (OS), progression-free survival (PFS) and response. Model performance was assessed in the independent Assistance Publique-Hôpitaux de Paris (AP-HP) cohort.

RESULTS

The integrated radiomic-clinical model outperformed BCLC stage (AUC 0.61, p<0.001) and ALBI grade (AUC 0.48, p<0.001) in ICL (AUC 0.89, 95% CI 0.75-0.99) and AP-HP (AUC 0.75, 95% CI 0.64-0.85) cohorts. Integrated model-stratified high-risk patients had significantly shorter median OS (ICL: 5.6 months vs. 28.2 months; p<0.001; AP-HP: 5.8 months vs. 15.7 months; p<0.001) and PFS (ICL: 2.4 months vs. 14.6 months; p<0.001; AP-HP: 2.1 months vs. 6.1 months; p=0.046). Low-risk patients had significantly higher ICI response rates compared to high-risk patients (35.6% vs. 21.4%; p=0.038). In multivariable analysis, radiomic group was the strongest predictor of OS (HR 3.22, 95% CI 1.99-5.20; p<0.001) and PFS (HR 1.82, 95% CI 1.18-2.80; p=0.010).

CONCLUSION

Radiomic-based models predict survival outcomes and response to immunotherapy in patients with advanced HCC. Deep learning in combination with machine learning can stratify patients and allows for precision treatment strategies.

IMPACT AND IMPLICATIONS

There is a lack of prognostic markers predicting survival and response after immunotherapy in hepatocellular carcinoma. This study used deep learning and machine learning to develop and validate an integrated radiomic-clinical model which can predict survival and response to atezolizumab plus bevacizumab from pre-treatment imaging. Radiomic-based machine learning models can risk-stratify advanced HCC patients receiving atezolizumab plus bevacizumab.

Turning the kaleidoscope: Innovations shaping the future of clinical trial design

Current clinical trials are based on rigid designs and drug-centric approaches that can stifle flexibility and innovation. With advances in molecular biology and technology, there is an urgent call to revitalize trial designs to meet these evolving demands. We propose a reshaped, prismatic vision of clinical trials combining different knowledge layers, synergized with modern computational approaches. This paradigm based on iterative learning will enable a more adaptive and precise framework for oncology drug development.

Multiparametric MRI-based radiomics and clinical nomogram predicts the recurrence of hepatocellular carcinoma after postoperative adjuvant transarterial chemoembolization

BACKGROUND

This study was undertaken to develop and validate a radiomics model based on multiparametric magnetic resonance imaging (MRI) for predicting recurrence in patients with hepatocellular carcinoma (HCC) following postoperative adjuvant transarterial chemoembolization (PA-TACE).

METHODS

In this retrospective study, 149 HCC patients (81 for training, 36 for internal validation, 32 for external validation) treated with PA-TACE were included in two medical centers. Multiparametric radiomics features were extracted from three MRI sequences. Least absolute shrinkage and selection operator (LASSO)-COX regression was utilized to select radiomics features. Optimal clinical characteristics selected by multivariate Cox analysis were integrated with Rad-score to develop a recurrence-free survival (RFS) prediction model. The model performance was evaluated by time-dependent receiver operating characteristic (ROC) curves, Harrell's concordance index (C-index), and calibration curve.

RESULTS

Fifteen optimal radiomic features were selected and the median Rad-score value was 0.434. Multivariate Cox analysis indicated that neutrophil-to-lymphocyte ratio (NLR) (hazard ratio (HR) = 1.49, 95% confidence interval (CI): 1.1-2.1, P = 0.022) and tumor size (HR = 1.28, 95% CI: 1.1-1.5, P = 0.001) were the independent predictors of RFS after PA-TACE. A combined model was established by integrating Rad-score, NLR, and tumor size in the training cohort (C-index 0.822; 95% CI 0.805-0.861), internal validation cohort (0.823; 95% CI 0.771-0.876) and external validation cohort (0.846; 95% CI 0.768-0.924). The calibration curve exhibited a satisfactory correspondence.

CONCLUSION

A multiparametric MRI-based radiomics model can predict RFS of HCC patients receiving PA-TACE and a nomogram can be served as an individualized tool for prognosis.

Interplay between MRI radiomics and immune gene expression signatures in oral squamous cell carcinoma

With the advances in immunotherapy and the challenge of poor responsiveness in oral squamous cell carcinoma (OSCC) patients, understanding the tumor microenvironment is crucial. Radiogenomics offers the potential to provide pre-operative, non-invasive image-derived immune biomarkers. To this aim, the present study explores the capability of MRI-based radiomics to describe patients' immune state in OSCC. Seven MRI-based radiomic, 29 immune-related gene expression signatures were computed and deconvolution analysis was performed for a subset of OSCC from the BD2Decide database. A correlation-driven analysis identified key associations between radiomic and immune-related signatures and cell populations. Radiomic classifiers of the gene expression signatures were then developed to evaluate their capability to stratify patients based on immune status. MRI-based radiomic models showed promising results in predicting a gene expression signature associated with significant prognostic value for HNSCC patients who underwent radiotherapy (AUC = 0.92), suggesting these models' potential in distinguishing radioresistant from radiosensitive patients, aiding treatment decisions. Additionally, radiomic signatures reflected immune infiltrating cells in our cohort (M1, CD8 + T, B cells). MRI-radiomic signatures and associated models could become non-invasive methods to evaluate the prognosis and treatment choice in OSCC patients. Based on our promising results, and upon external validation, MRI-radiomics could enhance personalized medicine approaches.

Current applications of radiomics in the assessment of tumor microenvironment of hepatocellular carcinoma

The tumor microenvironment (TME) of hepatocellular carcinoma (HCC) has garnered significant attention, especially with the rise of immunotherapy as a treatment strategy. Radiomics, an innovative technique, offers valuable insights into the intricate structure of the TME. This review highlights recent advancements in radiomics for analyzing the HCC TME, identifies key areas that warrant further research, and explores comprehensive multi-omics approaches that extend the potential of radiomics to new frontiers.

Using Cancer-Associated Fibroblasts as a Shear-Wave Elastography Imaging Biomarker to Predict Anti-PD-1 Efficacy of Triple-Negative Breast Cancer

In the clinical setting, the efficacy of single-agent immune checkpoint inhibitors (ICIs) in triple-negative breast cancer (TNBC) remains suboptimal. Therefore, there is a pressing need to develop predictive biomarkers to identify non-responders. Considering that cancer-associated fibroblasts (CAFs) represent an integral component of the tumor microenvironment that affects the stiffness of solid tumors on shear-wave elastography (SWE) imaging, wound healing CAFs (WH CAFs) were identified in highly heterogeneous TNBC. This subtype highly expressed vitronectin (VTN) and constituted the majority of CAFs. Moreover, WH CAFs were negatively correlated with CD8+ T cell infiltration levels and influenced tumor proliferation in the Eo771 mouse model. Furthermore, multi-omics analysis validated its role in immunosuppression. In order to non-invasively classify patients as responders or non-responders to ICI monotherapy, a deep learning model was constructed to classify the level of WH CAFs based on SWE imaging. As anticipated, this model effectively distinguished the level of WH CAFs in tumors. Based on the classification of the level of WH CAFs, while tumors with a high level of WH CAFs were found to exhibit a poor response to anti programmed cell death protein 1 (PD-1) monotherapy, they were responsive to the combination of anti-PD-1 and erdafitinib, a selective fibroblast growth factor receptor (FGFR) inhibitor. Overall, these findings establish a reference for a novel non-invasive method for predicting ICI efficacy to guide the selection of TNBC patients for precision treatment in clinical settings.

Habitat radiomics analysis for progression free survival and immune-related adverse reaction prediction in non-small cell lung cancer treated by immunotherapy

BACKGROUND

Non-small cell lung cancer (NSCLC) is highly heterogeneous, leading to varied treatment responses and immune-related adverse reactions (irAEs) among patients. Habitat radiomics allows non-invasive quantitative assessment of intratumor heterogeneity (ITH). Therefore, our objective is to employ habitat radiomics techniques to develop a robust approach for predicting the efficacy of Immune checkpoint inhibitors (ICIs) and the likelihood of irAEs in advanced NSCLC patients.

METHODS

In this retrospective two center study, two independent cohorts of patients with NSCLC were used to develop (n = 248) and validate signatures (n = 95). After applying four kinds of machine learning algorithms to select the key preoperative CT radiomic features, we used clinical, radiomics and habitat radiomic features to develop the clinical signature, radiomics signature and habitat radiomic signature for ICIs prognostics and irAEs prediction. By combining habitat radiomic features with corresponding clinicopathologic information, the nomogram signature was constructed in the training cohort. Next, the internal validation cohort (n = 75) of patients, and the external validation cohort (n = 20) of patients treated with ICIs were included to evaluate the predictive value of the four signatures, and their predictive performance was assessed by the area under operating characteristic curve (AUC).

RESULTS

Our study introduces a radiomic nomogram model that integrates clinical and habitat radiomic features to identify patients who may benefit from ICIs or experience irAEs. The Radiomics Nomogram model exhibited superior predictive performance in the training, validation, and external validation sets, with AUCs of 0.923, 0.817, and 0.899, respectively. This model outperformed both the Whole-tumor Radiomics Signature model (AUCs of 0.870, 0.736, and 0.626) and the Habitat Signature model (AUCs of 0.900, 0.804, and 0.808). The radiomics model focusing on tumor sub-regional habitat showed better predictive performance than the model derived from the entire tumor. Decision Curve Analysis (DCA) and calibration curves confirmed the nomogram's effectiveness.

CONCLUSION

By leveraging machine learning to predict the outcomes of ICIs, we can move closer to achieving tailored ICIs for lung cancer. This advancement will assist physicians in selecting and managing subsequent treatment strategies, thereby facilitating clinical decision-making.

Natural Killer Cell-Associated Radiogenomics Model for Hepatocellular Carcinoma: Integrating CD2 and Enhanced CT-Derived Radiomics Signatures

RATIONALE AND OBJECTIVES

Hepatocellular carcinoma (HCC) is a leading cause of cancer mortality. Natural Killer (NK) cells play a crucial role in immune defense against HCC, but their activity is often impaired by the tumor microenvironment (TME). This study aims to integrate radiomics and transcriptomics to develop a prognostic model linking NK cell characteristics to clinical outcomes in HCC.

METHODS

Transcriptomic data from five cohorts (734 HCC patients) from the Gene Expression Omnibus and The Cancer Genome Atlas databases were analyzed using the Microenvironment Cell Populations-counter algorithm. NK cell-related prognostic biomarkers were identified via weighted gene co-expression network analysis and LASSO-Cox regression. Radiomics models were established using CT imaging features from 239 patients in three datasets from The Cancer Imaging Archive and Shanghai East Hospital. HCC radiogenomic subtypes were proposed by integrating genetic biomarkers and radiomics models.

RESULTS

CD2 expression was identified as an independent NK cell-related prognostic biomarker, with a positive impact on prognosis and a strong correlation with NK cell-associated biological processes in HCC. A robust radiomics model was constructed, and the integration of CD2 expression with radioscore identified potential radiogenomic subtypes of HCC.

CONCLUSION

Radiomics has potential to link TME immune phenotypes with HCC prognosis. CD2 is a key biomarker connecting NK cells with radiomic features, offering a new classification of HCC into radiogenomic subtypes. This approach supports the use of radiogenomics in personalized HCC treatment.

Role of Radiomics-based Multiomics Panel in the Microenvironment and Prognosis of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the most prevalent form of liver tumor, characterized by restricted therapeutic options and typically low long-term survival rates. Recently, immunotherapy has revolutionized HCC treatment, making the tumor microenvironment (TME) a research focus. Radiomics is increasingly crucial in HCC clinical decisions, offering advanced tools for TME characterization and prognosis assessment. Meanwhile, advancements in pathomics provide new insights into HCC's comprehensive traits and details. Advancements in genomics and transcriptomics enable the integration of radiomics and pathomics with genetic data to better understand HCC heterogeneity and its microenvironment, aiding prognostic assessments. This review provides a comprehensive overview of pivotal radiomics studies focused on TME prediction, underscoring the synergistic effects of integrating multiomics approaches for TME analysis and HCC outcome prediction. It critically examines the challenges and opportunities inherent in multiomics research, emphasizing its substantial significance in both research and clinical contexts.

Predictive Model of Objective Response to Nivolumab Monotherapy for Advanced Renal Cell Carcinoma by Machine Learning Using Genetic and Clinical Data: The SNiP-RCC Study

PURPOSE

Anti-PD-1 antibodies are widely used for cancer treatment, including in advanced renal cell carcinoma (RCC). However, the therapeutic response varies among patients. This study aimed to predict tumor response to nivolumab anti-PD-1 antibody treatment for advanced RCC by integrating genetic and clinical data using machine learning (ML).

METHODS

Clinical and single-nucleotide polymorphism (SNP) data obtained in the SNPs in nivolumab PD-1 inhibitor for RCC study, which enrolled Japanese patients treated with nivolumab monotherapy for advanced clear cell RCC, were used. A point-wise linear (PWL) algorithm, logistic regression with elastic-net regularization, and eXtreme Gradient Boosting were used in this study. AUC values for objective response and C-indices for progression-free survival (PFS) were calculated to evaluate the utility of the models.

RESULTS

Among the three ML algorithms, the AUC values to predict objective response were highest for the PWL algorithm among all the data sets. Three predictive models (clinical model, small SNP model, and large SNP model) were created by the PWL algorithm using the clinical data alone and using eight and 49 SNPs in addition to the clinical data. C-indices for PFS by the clinical model, small SNP model, and large SNP model were 0.522, 0.600, and 0.635, respectively.

CONCLUSION

The results demonstrated that the SNP models created by ML produced excellent predictions of tumor response to nivolumab monotherapy for advanced clear cell RCC and will be helpful in treatment decisions.

Multiparametric MRI-based Machine Learning Radiomics for Predicting Treatment Response to Transarterial Chemoembolization Combined with Targeted and Immunotherapy in Unresectable Hepatocellular Carcinoma: A Multicenter Study

RATIONALE AND OBJECTIVES

To develop and validate multiple machine learning predictive models incorporating clinical features and pretreatment multiparametric magnetic resonance imaging (MRI) radiomic features for predicting treatment response to transarterial chemoembolization combined with molecular targeted therapy plus immunotherapy in unresectable hepatocellular carcinoma (HCC).

MATERIALS AND METHODS

This retrospective study involved 276 patients with unresectable HCC who received combination therapy from 4 medical centers. Patients were divided into one training cohort and two independent external validation cohorts. 16 radiomic features from six multiparametric MRI sequences and 2 clinical features were used to build six machine learning models. The models were evaluated using the area under the curve (AUC), decision curve analysis, and incremental predictive value.

RESULTS

Alpha-fetoprotein and neutrophil-to-lymphocyte ratio are clinical independent predictors of treatment response. In the training cohort and two external validation cohorts, the AUCs and 95% confidence intervals for predicting treatment response were respectively 0.782 (0.698-0.857) 0.695 (0.566-0.823), and 0.679 (0.542-0.810) for the clinical model; 0.942 (0.903-0.974), 0.869 (0.761-0.949), and 0.868 (0.769-0.942) for the radiomics model; and 0.956 (0.920-0.984), 0.895 (0.810-0.967), and 0.892 (0.804-0.957) for the combined clinical-radiomics model. In the three cohorts, the incremental predictive value of the radiomics model over the clinical model was 49.2% (P < 0.001), 28.8% (P < 0.001), and 31.5% (P < 0.001).

CONCLUSION

The combined clinical-radiomics model may provide a reliable and non-invasive tool to predict individual treatment responses and guide and improve clinical decision-making in combination therapy of HCC patients.

["Optimizing Imaging and Dose-Response in Radiotherapies" XVIth workshop organised by the Cancéropôle Grand-Ouest's "Vectorisation, Imagerie, Radiothérapies" network - 4-7 October 2023, Erquy, France]

The sixteenth edition of the international workshop organized by "Tumour Targeting & Radiotherapies" network of the Cancéropôle Grand-Ouest focused on the problem of optimizing the dose-effect relationships of internal and external radiotherapy, using a variety of innovations from different disciplines, such as technological and imaging advances, vectorization, artificial intelligence, modeling and combined therapies.

Intratumoral and peritumoral PET/CT-based radiomics for non-invasively and dynamically predicting immunotherapy response in NSCLC

BACKGROUND

We aimed to develop a machine learning model based on intratumoral and peritumoral 18F-FDG PET/CT radiomics to non-invasively and dynamically predict the response to immunotherapy in non-small cell lung cancer (NSCLC).

METHODS

This retrospective study included 296 NSCLC patients, including a training cohort (N = 183), a testing cohort (N = 78), and a TCIA radiogenomic cohort (N = 35). The extreme gradient boosting algorithm was employed to develop the radiomic models.

RESULTS

The COMB-Radscore, which was developed by combining radiomic features from PET, CT, and PET/CT images, had the most satisfactory predictive performance with AUC (ROC) 0.894 and 0.819 in the training and testing cohorts, respectively. Survival analysis has demonstrated that COMB-Radscore is an independent prognostic factor for progression-free survival and overall survival. Moreover, COMB-Radscore demonstrates excellent dynamic predictive performance, with an AUC (ROC) of 0.857, enabling the earlier detection of potential disease progression in patients compared to radiological evaluation solely relying on tumor size. Further radiogenomic analysis showed that the COMB-Radscore was associated with infiltration abundance and functional status of CD8 + T cells.

CONCLUSIONS

The radiomic model holds promise as a precise, personalized, and dynamic decision support tool for the treatment of NSCLC patients.

Multi-cohort study in gastric cancer to develop CT-based radiomic models to predict pathological response to neoadjuvant immunotherapy

BACKGROUND

Neoadjuvant immunotherapy has been shown to improve survival in patients with gastric cancer. This study sought to develop and validate a radiomics-based machine learning (ML) model for patients with locally advanced gastric cancer (LAGC), specifically to predict whether patients will achieve a major pathological response (MPR) following neoadjuvant immunotherapy. With its predictive capabilities, this tool shows promise for enhancing clinical decision-making processes in the future.

METHODS

This study utilized a multicenter cohort design, retrospectively gathering clinical data and computed tomography (CT) images from 268 patients diagnosed with advanced gastric cancer who underwent neoadjuvant immunotherapy between January 2019 and December 2023 from two medical centers. Radiomic features were extracted from CT images, and a multi-step feature selection procedure was applied to identify the top 20 representative features. Nine ML algorithms were implemented to build prediction models, with the optimal algorithm selected for the final prediction model. The hyperparameters of the chosen model were fine-tuned using Bayesian optimization and grid search. The performance of the model was evaluated using several metrics, including the area under the curve (AUC), accuracy, and Cohen's kappa coefficient.

RESULTS

Three cohorts were included in this study: the development cohort (DC, n = 86), the internal validation cohort (IVC, n = 59), and the external validation cohort (EVC, n = 52). Nine ML models were developed using DC cases. Among these, an optimized Bayesian-LightGBM model, demonstrated robust predictive performance for MPR following neoadjuvant immunotherapy in LAGC patients across all cohorts. Specifically, within DC, the LightGBM model attained an AUC of 0.828, an overall accuracy of 0.791, a Cohen's kappa coefficient of 0.552, a sensitivity of 0.742, a specificity of 0.818, a positive predictive value (PPV) of 0.586, a negative predictive value (NPV) of 0.867, a Matthews correlation coefficient (MCC) of 0.473, and a balanced accuracy of 0.780. Comparable performance metrics were validated in both the IVC and the EVC, with AUC values of 0.777 and 0.714, and overall accuracies of 0.729 and 0.654, respectively. These results suggested good fitness and generalization of the Bayesian-LightGBM model. Shapley Additive Explanations (SHAP) analysis identified significant radiomic features contributing to the model's predictive capability. The SHAP values of the features wavelet.LLH_gldm_SmallDependenceLowGrayLevelEmphasis, wavelet.HHL_glrlm_RunVariance, and wavelet.LLH_glszm_LargeAreaHighGrayLevelEmphasis were ranked among the top three, highlighting their significant contribution to the model's predictive performance. In contrast to existing radiomic models that exclusively focus on neoadjuvant chemotherapy, our model integrates both neoadjuvant immunotherapy and chemotherapy, thereby offering more precise predictive capabilities.

CONCLUSION

The radiomics-based ML model demonstrated significant efficacy in predicting the pathological response to neoadjuvant immunotherapy in LAGC patients, thereby providing a foundation for personalized treatment strategies.

The artificial intelligence revolution in gastric cancer management: clinical applications

Nowadays, gastric cancer has become a significant issue in the global cancer burden, and its impact cannot be ignored. The rapid development of artificial intelligence technology is attempting to address this situation, aiming to change the clinical management landscape of gastric cancer fundamentally. In this transformative change, machine learning and deep learning, as two core technologies, play a pivotal role, bringing unprecedented innovations and breakthroughs in the diagnosis, treatment, and prognosis evaluation of gastric cancer. This article comprehensively reviews the latest research status and application of artificial intelligence algorithms in gastric cancer, covering multiple dimensions such as image recognition, pathological analysis, personalized treatment, and prognosis risk assessment. These applications not only significantly improve the sensitivity of gastric cancer risk monitoring, the accuracy of diagnosis, and the precision of survival prognosis but also provide robust data support and a scientific basis for clinical decision-making. The integration of artificial intelligence, from optimizing the diagnosis process and enhancing diagnostic efficiency to promoting the practice of precision medicine, demonstrates its promising prospects for reshaping the treatment model of gastric cancer. Although most of the current AI-based models have not been widely used in clinical practice, with the continuous deepening and expansion of precision medicine, we have reason to believe that a new era of AI-driven gastric cancer care is approaching.

A CT-based interpretable deep learning signature for predicting PD-L1 expression in bladder cancer: a two-center study

BACKGROUND

To construct and assess a deep learning (DL) signature that employs computed tomography imaging to predict the expression status of programmed cell death ligand 1 in patients with bladder cancer (BCa).

METHODS

This retrospective study included 190 patients from two hospitals who underwent surgical removal of BCa (training set/external validation set, 127/63). We used convolutional neural network and radiomics machine learning technology to generate prediction models. We then compared the performance of the DL signature with the radiomics machine learning signature and selected the optimal signature to build a nomogram with the clinical model. Finally, the internal forecasting process of the DL signature was explained using Shapley additive explanation technology.

RESULTS

On the external validation set, the DL signature had an area under the curve of 0.857 (95% confidence interval: 0.745-0.932), and demonstrated superior prediction performance in comparison with the other models. SHAP expression images revealed that the prediction of PD-L1 expression status is mainly influenced by the tumor edge region, particularly the area close to the bladder wall.

CONCLUSIONS

The DL signature performed well in comparison with other models and proved to be a valuable, dependable, and interpretable tool for predicting programmed cell death ligand 1 expression status in patients with BCa.

Society for Immunotherapy of Cancer (SITC) consensus statement on essential biomarkers for immunotherapy clinical protocols

Immunotherapy of cancer is now an essential pillar of treatment for patients with many individual tumor types. Novel immune targets and technical advances are driving a rapid exploration of new treatment strategies incorporating immune agents in cancer clinical practice. Immunotherapies perturb a complex system of interactions among genomically unstable tumor cells, diverse cells within the tumor microenvironment including the systemic adaptive and innate immune cells. The drive to develop increasingly effective immunotherapy regimens is tempered by the risk of immune-related adverse events. Evidence-based biomarkers that measure the potential for therapeutic response and/or toxicity are critical to guide optimal patient care and contextualize the results of immunotherapy clinical trials. Responding to the lack of guidance on biomarker testing in early-phase immunotherapy clinical trials, we propose a definition and listing of essential biomarkers recommended for inclusion in all such protocols. These recommendations are based on consensus provided by the Society for Immunotherapy of Cancer (SITC) Clinical Immuno-Oncology Network (SCION) faculty with input from the SITC Pathology and Biomarker Committees and the Journal for ImmunoTherapy of Cancer readership. A consensus-based selection of essential biomarkers was conducted using a Delphi survey of SCION faculty. Regular updates to these recommendations are planned. The inaugural list of essential biomarkers includes complete blood count with differential to generate a neutrophil-to-lymphocyte ratio or systemic immune-inflammation index, serum lactate dehydrogenase and albumin, programmed death-ligand 1 immunohistochemistry, microsatellite stability assessment, and tumor mutational burden. Inclusion of these biomarkers across early-phase immunotherapy clinical trials will capture variation among trials, provide deeper insight into the novel and established therapies, and support improved patient selection and stratification for later-phase clinical trials.

Role of AI in empowering and redefining the oncology care landscape: perspective from a developing nation

Early diagnosis and accurate prognosis play a pivotal role in the clinical management of cancer and in preventing cancer-related mortalities. The burgeoning population of Asia in general and South Asian countries like India in particular pose significant challenges to the healthcare system. Regrettably, the demand for healthcare services in India far exceeds the available resources, resulting in overcrowded hospitals, prolonged wait times, and inadequate facilities. The scarcity of trained manpower in rural settings, lack of awareness and low penetrance of screening programs further compounded the problem. Artificial Intelligence (AI), driven by advancements in machine learning, deep learning, and natural language processing, can profoundly transform the underlying shortcomings in the healthcare industry, more for populous nations like India. With about 1.4 million cancer cases reported annually and 0.9 million deaths, India has a significant cancer burden that surpassed several nations. Further, India's diverse and large ethnic population is a data goldmine for healthcare research. Under these circumstances, AI-assisted technology, coupled with digital health solutions, could support effective oncology care and reduce the economic burden of GDP loss in terms of years of potential productive life lost (YPPLL) due to India's stupendous cancer burden. This review explores different aspects of cancer management, such as prevention, diagnosis, precision treatment, prognosis, and drug discovery, where AI has demonstrated promising clinical results. By harnessing the capabilities of AI in oncology research, healthcare professionals can enhance their ability to diagnose cancers at earlier stages, leading to more effective treatments and improved patient outcomes. With continued research and development, AI and digital health can play a transformative role in mitigating the challenges posed by the growing population and advancing the fight against cancer in India. Moreover, AI-driven technologies can assist in tailoring personalized treatment plans, optimizing therapeutic strategies, and supporting oncologists in making well-informed decisions. However, it is essential to ensure responsible implementation and address potential ethical and privacy concerns associated with using AI in healthcare.

Prediction of postoperative disease-free survival in colorectal cancer patients using CT radiomics nomogram: a multicenter study

BackgroundRadiomics analysis is widely used to assess tumor prognosis.PurposeTo explore the value of computed tomography (CT) radiomics nomogram in predicting disease-free survival (DFS) of patients with colorectal cancer (CRC) after operation.Material and MethodsA total of 522 CRC patients from three centers were retrospectively included. Radiomics features were extracted from CT images, and the least absolute shrinkage and selection operator Cox regression algorithm was employed to select radiomics features. Clinical risk factors associated with DFS were selected through univariate and multivariate Cox regression analysis to build the clinical model. A predictive nomogram was developed by amalgamating pertinent clinical risk factors and radiomics features. The predictive performance of the nomogram was evaluated using the C-index, calibration curve, and decision curve. DFS probabilities were estimated using the Kaplan-Meier method.ResultsIntegrating the retained eight radiomics features and three clinical risk factors (pathological N stage, microsatellite instability, perineural invasion), a nomogram was constructed. The C-index for the nomogram were 0.819 (95% CI=0.794-0.844), 0.782 (95% CI=0.740-0.824), 0.786 (95% CI=0.753-0.819), and 0.803 (95% CI=0.765-0.841) in the training set, internal validation set, external validation set 1, and external validation set 2, respectively. The calibration curves demonstrated a favorable congruence between the predicted and observed values as depicted by the nomogram. The decision curve analysis underscored that the nomogram yielded a heightened clinical net benefit.ConclusionThe constructed radiomics nomogram, amalgamating the radiomics features and clinical risk factors, exhibited commendable performance in the individualized prediction of postoperative DFS in CRC patients.

Radiomics Biomarkers to Predict Checkpoint Inhibitor Pneumonitis in Non-small Cell Lung Cancer

RATIONALE AND OBJECTIVES

Immune checkpoint inhibitors (ICIs) have revolutionized the treatment of non-small cell lung cancer (NSCLC). However, immune-related adverse events still occur, of which checkpoint inhibitor pneumonitis (CIP) is the most common. We aimed to construct and validate a contrast-enhanced computed tomography-based radiomic nomogram to predict the probability of CIP before ICIs treatment in NSCLC.

MATERIALS AND METHODS

We retrospectively analyzed 685 patients with NSCLC who were initially treated with ICIs. A total of 186 patients were included in our study, and an additional 52 patients from another hospital were considered for external validation. After radiomics feature extraction and selection, we applied a support vector machine classification model to distinguish CIP and used the probability as a radiomics signature. A radiomics-clinical logistic regression model was built using the filtered clinical parameters and a radiomic signature. Receiver operating characteristic, area under the curve (AUC), calibration curve, and decision curve analysis was used for inter-model comparison.

RESULTS

The combined radiomics-clinical model constructed using age, interstitial lung disease, emphysema at baseline, and radiomics signature showed an AUC of 0.935, 0.905, and 0.923 for the training, validation, and external validation cohorts, respectively. Compared with the clinical-only (AUC of 0.829, 0.826, and 0.809) and radiomics-only models (0.865, 0.847, and 0.841), the radiomics-clinical displayed better predictive power.

CONCLUSION

This combined radiomics-clinical model predicted the probability of CIP during ICIs treatment in patients with NSCLC with favorable accuracy and could therefore be used as an effective tool to guide clinical ICIs decisions.

The Role of [18F]FDG PET/CT in Monitoring of Therapy Response in Lung Cancer

Lung cancer remains a leading cause of cancer deaths worldwide, with an all stage 5-year relative survival rate of less than 30%. Multiple treatment strategies are available and continue to evolve, with therapy primarily tailored to the type and stage of the disease. Accurate monitoring of therapy response is crucial for optimizing treatment outcomes. PET/CT imaging with [18F]FDG has become the standard of care across various phases of lung cancer management due to its ability to assess metabolic activity. This review underscores the pivotal role of [18F]FDG PET/CT in evaluating therapy response in lung cancer, particularly in non-small cell lung cancer (NSCLC). It examines conventional response criteria and their adaptations in the era of immunotherapy, highlighting the value of integrating metabolic imaging with established criteria to improve treatment assessment and guide clinical decisions. The potential of non-[18F]FDG PET tracers targeting diverse biological pathways to provide deeper insights into tumor biology, therapy response and predictive outcomes is also explored. Additionally, the emerging role of radiomics in enhancing treatment efficacy assessment and improving patient management is briefly highlighted. Despite the challenges in the routine clinical application of various metabolic response criteria, [18F]FDG PET/CT remains a crucial tool in monitoring therapy response in lung cancer. Ongoing advancements in therapeutic strategies, radiopharmaceuticals, and imaging techniques continue to drive progress in lung cancer management, promising improved patient outcomes.

Exploring radiomic features of lateral cerebral ventricles in postmortem CT for postmortem interval estimation

The aim of this study is to investigate the potential of radiomic features extracted from postmortem computed tomography (PMCT) scans of the lateral cerebral ventricles (LCVs) to provide information on the time since death, or postmortem interval (PMI), a critical aspect of forensic medicine. Periodic PMCT scans, referred to as "sequential scans", were obtained from twelve corpses with known times of death, ranging from 5.5 to 273 h postmortem. Radiomics features were then extracted from the LCVs, and a mixed-effect model, specifically designed for sequential data, was employed to assess the association between feature values and PMI. Four model variants were fitted to the data to identify the best functional form to explain the relationship between the variables. Significant associations were observed for features, the most significant being the median Hounsfield Units (HU) within the LCVs (p < 9.47 × 10⁻⁹), LCVs surface area (p < 4.69 × 10⁻⁶), L-major axis (p < 2.17 × 10⁻⁵), L-minor axis (p < 1.30 × 10⁻⁴), and HU entropy (p < 4.16 × 10⁻⁴). Our findings align with previous studies, supporting a logarithmic model for PMI-related changes in LCV volume and mean HU intensity value. This study highlights the potential of PMCT-based radiomics as source of complementary information that could be integrated into existing methods for PMI estimation. Our results support the application of a quantitative imaging approach in forensic investigations.

Application of Intravoxel Incoherent Motion in the Prediction of Intra-Tumoral Tertiary Lymphoid Structures in Hepatocellular Carcinoma

Objective

To explore the value of intravoxel incoherent motion (IVIM) sequences in predicting intra-tumoral tertiary lymphoid structures (TLSs).

Materials and Methods

This prospective study pre-operatively enrolled hepatocellular carcinoma (HCC) patients who underwent magnetic resonance imaging including IVIM sequences, between January 2019 and April 2021. Intra-tumoral TLSs presence was assessed on pathological slide images. Clinical and radiological characteristics were collected. IVIM quantitative parameters and radiomics features were obtained based on the whole delineated tumor volume. By using feature selection techniques, 22 radiomics features, clinical-radiological features (lymphocyte count and satellite nodules), and IVIM parameters (apparent diffusion coefficient (ADC_90Percentile), perfusion fraction (f_Maximum)) were selected. The logistic regression algorithm was used to construct the prediction model based on the combination of these features. The diagnostic performance was assessed using the area under the receiver operating characteristic (AUC). The recurrence-free survival (RFS) was evaluated with Kaplan-Meier curves.

Results

A total of 168 patients were divided into training (n=128) and testing (n=40) cohorts (mean age: 56.83±14.43 years; 149 [88.69%] males; 130 TLSs+). In testing cohort, the model combining multimodal features demonstrated a good performance (AUC: 0.86) and significantly outperformed models based on single-modality features. The model based on radiomics features (AUC: 0.80) had better performance than other features, including IVIM parameter maps (ADC_90Percentile and f_Maximum, AUC: 0.72) and clinical-radiological characteristics (satellite nodules and lymphocyte counts, AUC: 0.59). TLSs+ patients had higher RFS than TSLs- patients (all p <0.05).

Conclusion

The nomogram based on the proposed model can be used as a pre-operative predictive biomarker of TLSs.

Critical Relevance Statement

The nomogram incorporating IVIM sequences may serve as a pre-operative predictive biomarker of intra-tumoral tertiary lymphoid structure (TLS) status.

Radiomics model building from multiparametric MRI to predict Ki-67 expression in patients with primary central nervous system lymphomas: a multicenter study

OBJECTIVES

To examine the correlation of apparent diffusion coefficient (ADC), diffusion weighted imaging (DWI), and T1 contrast enhanced (T1-CE) with Ki-67 in primary central nervous system lymphomas (PCNSL). And to assess the diagnostic performance of MRI radiomics-based machine-learning algorithms in differentiating the high proliferation and low proliferation groups of PCNSL.

METHODS

83 patients with PCNSL were included in this retrospective study. ADC, DWI and T1-CE sequences were collected and their correlation with Ki-67 was examined using Spearman's correlation analysis. The Kaplan-Meier method and log-rank test were used to compare the survival rates of the high proliferation and low proliferation groups. The radiomics features were extracted respectively, and the features were screened by machine learning algorithm and statistical method. Radiomics models of seven different sequence permutations were constructed. The area under the receiver operating characteristic curve (ROC AUC) was used to evaluate the predictive performance of all models. DeLong test was utilized to compare the differences of models.

RESULTS

Relative mean apparent diffusion coefficient (rADCmean) (ρ=-0.354, p = 0.019), relative mean diffusion weighted imaging (rDWImean) (b = 1000) (ρ = 0.273, p = 0.013) and relative mean T1 contrast enhancement (rT1-CEmean) (ρ = 0.385, p = 0.001) was significantly correlated with Ki-67. Interobserver agreements between the two radiologists were almost perfect for all parameters (rADCmean ICC = 0.978, 95%CI 0.966-0.986; rDWImean (b = 1000) ICC = 0.931, 95% CI 0.895-0.955; rT1-CEmean ICC = 0.969, 95% CI 0.953-0.980). The differences in PFS (p = 0.016) and OS (p = 0.014) between the low and high proliferation groups were statistically significant. The best prediction model in our study used a combination of ADC, DWI, and T1-CE achieving the highest AUC of 0.869, while the second ranked model used ADC and DWI, achieving an AUC of 0.828.

CONCLUSION

rDWImean, rADCmean and rT1-CEmean were correlated with Ki-67. The radiomics model based on MRI sequences combined is promising to distinguish low proliferation PCNSL from high proliferation PCNSL.

Metastatic Lung Adenocarcinomas: Development and Evaluation of Radiomic-Based Methods to Measure Baseline Intra-Patient Inter-Tumor Lesion Heterogeneity

Radiomics has traditionally focused on individual tumors, often neglecting the integration of metastatic disease, particularly in patients with non-small cell lung cancer. This study sought to examine intra-patient inter-tumor lesion heterogeneity indices using radiomics, exploring their relevance in metastatic lung adenocarcinoma. Consecutive adults newly diagnosed with metastatic lung adenocarcinoma underwent contrast-enhanced CT scans for lesion segmentation and radiomic feature extraction. Three methods were devised to measure distances between tumor lesion profiles within the same patient in radiomic space: centroid to lesion, lesion to lesion, and primitive to lesion, with subsequent calculation of mean, range, and standard deviation of these distances. Associations between HIs, disease control rate, objective response rate to first-line treatment, and overall survival were explored. The study included 167 patients (median age 62.3 years) between 2016 and 2019, divided randomly into experimental (N = 117,546 lesions) and validation (N = 50,232 tumor lesions) cohorts. Patients without disease control/objective response and with poorer survival consistently systematically exhibited values of all heterogeneity indices. Multivariable analyses revealed that the range of primitive-to-lesion distances was associated with disease control in both cohorts and with objective response in the validation cohort. This metrics showed univariable associations with overall survival in the experimental. In conclusion, we proposed original methods to estimate the intra-patient inter-tumor lesion heterogeneity using radiomics that demonstrated correlations with patient outcomes, shedding light on the clinical implications of inter-metastases heterogeneity. This underscores the potential of radiomics in understanding and potentially predicting treatment response and prognosis in metastatic lung adenocarcinoma patients.

Radiomics-based biomarker for PD-1 status and prognosis analysis in patients with HCC

Purpose

To investigate the impact of preoperative contrast-enhanced CT-based radiomics model on PD-1 prediction in hepatocellular carcinoma (HCC) patients.

Methods

The study included 105 HCC patients (training cohort: 72; validation cohort: 33) who underwent preoperative contrast-enhanced CT and received systemic sorafenib treatment after surgery. Radiomics score was built for each patient and was integrated with independent clinic radiologic predictors into the radiomics model using multivariable logistic regression analysis.

Results

Seventeen radiomics features were finally selected to construct the radiomics score. In multivariate analysis, serum creatine and peritumoral enhancement were significant independent factors for PD-1 prediction. The radiomics model integrated radiomics signature with serum creatine and peritumoral enhancement showed good discriminative performance (AUC of 0.897 and 0.794 in the training and validation cohort). Overall survival (OS) was significantly different between the radiomics-predicted PD-1-positive and PD-1-negative groups (OS: 29.66 months, CI:16.03-44.40 vs. 31.04 months, CI: 17.10-44.07, P<0.001). Radiomics-predicted PD-1 was an independent predictor of OS of patients treated with sorafenib after surgery. (Hazard ratio [HR]: 1.61 [1.23-2.1], P<0.001).

Conclusion

The proposed model based on radiomic signature helps to evaluate PD-1 status of HCC patients and may be used for evaluating patients most likely to benefit from sorafenib as a potentially combination therapy regimen with immune checkpoint therapies.

Radiomic profiling of high-risk aneurysms with blebs: an exploratory study

BACKGROUND

Blebs significantly increase rupture risk of intracranial aneurysms. Radiomic analysis offers a robust characterization of the aneurysm wall. However, the unique radiomic profile of various compartments, including blebs, remains unexplored. Likewise, the correlation between these imaging markers and fluid/mechanical metrics is yet to be investigated. To address this, we analyzed the radiomic features (RFs) of bleb-containing aneurysms and their relationship with wall tension and shear stress metrics, aiming to enhance risk assessment.

METHODS

Aneurysms were imaged using high-resolution magnetic resonance imaging (MRI). A T1 and a T1 after contrast (T1+Gd) sequences were acquired. 3D models of aneurysm bodies and blebs were generated, and RFs were extracted. Aneurysms with and without blebs were matched based on location and size for analysis. Univariate regression models and Spearman's correlations were used to establish associations between bleb-dependent RFs and mechanical/fluid dynamics metrics.

RESULTS

Eighteen aneurysms with blebs were identified. Fifty-five RFs were significantly different between blebs and body within the same aneurysms. Of these RFs, 9% (5/55) were first-order, and 91% (50/55) were second-order features. After aneurysms with and without blebs were matched for location and size, five RFs 5% (5/93) were significantly different. Forty-one out of the 55 RFs different between bleb and body sac of the primary aneurysm were moderately and strongly correlated with mechanical and fluid dynamics metrics.

CONCLUSION

Aneurysm blebs exhibit distinct radiomic profiles compared with the main body of the aneurysm sac. The variability in bleb wall characteristics may arise from differing mechanical stresses and localized hemodynamics. Leveraging radiomic profiling could help identify regions with a heightened risk of rupture.

Prediction of immunotherapy response in nasopharyngeal carcinoma: a comparative study using MRI-based radiomics signature and programmed cell death ligand 1 expression score

OBJECTIVES

To compare an MRI-based radiomics signature with the programmed cell death ligand 1 (PD-L1) expression score for predicting immunotherapy response in nasopharyngeal carcinoma (NPC).

METHODS

Consecutive patients with NPC who received immunotherapy between January 2019 and June 2022 were divided into training (n = 111) and validation (n = 66) sets. Tumor radiomics features were extracted from pretreatment MR images. PD-L1 combined positive score (CPS) was calculated using immunohistochemistry. The least absolute shrinkage and selection operator (LASSO) algorithm was used for feature selection and radiomics signature construction. Receiver operating characteristic (ROC) curve analysis was performed to assess prediction performance.

RESULTS

A total of eleven radiomics features with the greatest discrimination capability were identified by the LASSO algorithm to construct the radiomics signature. In predicting patients with objective response to immunotherapy, radiomics score (Rd-score) yielded a significantly higher area under the ROC curve than that of CPS in both the training (0.790 vs. 0.645, p = 0.025) and the validation (0.735 vs. 0.608, p = 0.038) sets. Multivariate analysis identified the Rd-score as an independent influencing factor in predicting immunotherapy response (odds ratio = 19.963, p < 0.001). Kaplan-Meier analysis indicated that patients with Rd-score ≥ 0.5 showed longer progression-free survival than patients with Rd-score < 0.5 (log-rank p < 0.01).

CONCLUSION

An MRI-based radiomics signature demonstrated greater efficacy than the PD-L1 expression score in predicting immunotherapy response in patients with NPC.

KEY POINTS

Question How does an MRI-based radiomics signature compare with the programmed cell death ligand 1 expression score for predicting immunotherapy response in nasopharyngeal carcinoma? Findings The MRI-based radiomics signature demonstrated superior predictive value compared with programmed cell death ligand 1 expression score in identifying immunotherapy responders. Clinical relevance MRI-based radiomics are a promising novel noninvasive tool for predicting immunotherapy outcomes in nasopharyngeal carcinoma.

Deep Learning Radiomics Nomogram Based on MRI for Differentiating between Borderline Ovarian Tumors and Stage I Ovarian Cancer: A Multicenter Study

RATIONALE AND OBJECTIVES

To develop and validate a deep learning radiomics nomogram (DLRN) based on T2-weighted MRI to distinguish between borderline ovarian tumors (BOTs) and stage I epithelial ovarian cancer (EOC) preoperatively.

MATERIALS AND METHODS

This retrospective multicenter study enrolled 279 patients from three centers, divided into a training set (n = 207) and an external test set (n = 72). The intra- and peritumoral radiomics analysis was employed to develop a combined radiomics model. A deep learning model was constructed based on the largest orthogonal slices of the tumor volume, and a clinical model was constructed using independent clinical predictors. The DLRN was then constructed by integrating deep learning, intra- and peritumoral radiomics, and clinical predictors. For comparison, an original radiomics model based solely on tumor volume (excluding the peritumoral area) was also constructed. All models were validated through 10-fold cross-validation and external testing, and their predictive performance was evaluated by the area under the receiver operating characteristic curve (AUC).

RESULTS

The DLRN demonstrated superior performance across the 10-fold cross-validation, with the highest AUC of 0.825±0.082. On the external test set, the DLRN significantly outperformed the clinical model and the original radiomics model (AUC = 0.819 vs. 0.708 and 0.670, P = 0.047 and 0.015, respectively). Furthermore, the combined radiomics model performed significantly better than the original radiomics model (AUC = 0.778 vs. 0.670, P = 0.043).

CONCLUSION

The DLRN exhibited promising performance in distinguishing BOTs from stage I EOC preoperatively, thus potentially assisting clinical decision-making.

From Images to Genes: Radiogenomics Based on Artificial Intelligence to Achieve Non-Invasive Precision Medicine in Cancer Patients

With the increasing demand for precision medicine in cancer patients, radiogenomics emerges as a promising frontier. Radiogenomics is originally defined as a methodology for associating gene expression information from high-throughput technologies with imaging phenotypes. However, with advancements in medical imaging, high-throughput omics technologies, and artificial intelligence, both the concept and application of radiogenomics have significantly broadened. In this review, the history of radiogenomics is enumerated, related omics technologies, the five basic workflows and their applications across tumors, the role of AI in radiogenomics, the opportunities and challenges from tumor heterogeneity, and the applications of radiogenomics in tumor immune microenvironment. The application of radiogenomics in positron emission tomography and the role of radiogenomics in multi-omics studies is also discussed. Finally, the challenges faced by clinical transformation, along with future trends in this field is discussed.

Multimodal Imaging Approach for Tumor Treatment Response Evaluation in the Era of Immunotherapy

ABSTRACT

Immunotherapy is likely the most remarkable advancement in lung cancer treatment during the past decade. Although immunotherapy provides substantial benefits, their therapeutic responses differ from those of conventional chemotherapy and targeted therapy, and some patients present unique immunotherapy response patterns that cannot be judged under the current measurement standards. Therefore, the response monitoring of immunotherapy can be challenging, such as the differentiation between real response and pseudo-response. This review outlines the various tumor response patterns to immunotherapy and discusses methods for quantifying computed tomography (CT) and 18 F-fluorodeoxyglucose positron emission tomography (PET) in the field of lung cancer. Emerging technologies in magnetic resonance imaging (MRI) and non-FDG PET tracers are also explored. With immunotherapy responses, the role for imaging is essential in both anatomical radiological responses (CT/MRI) and molecular changes (PET imaging). Multiple aspects must be considered when assessing treatment responses using CT and PET. Finally, we introduce multimodal approaches that integrate imaging and nonimaging data, and we discuss future directions for the assessment and prediction of lung cancer responses to immunotherapy.

Correlation of noninvasive imaging of tumour-infiltrating lymphocytes with survival and BCG immunotherapy response in patients with bladder cancer: a multicentre cohort study

BACKGROUND

Tumour-infiltrating lymphocytes (TILs) are strongly correlated with the prognosis and immunotherapy response in bladder cancer. The TIL status is typically assessed through microscopy as part of tissue pathology. Here, the authors developed Rad-TIL model, a novel radiomics model, to predict TIL status in patients with bladder cancer.

MATERIAL AND METHODS

The authors enrolled 1089 patients with bladder cancer and developed the Rad-TIL model by using a machine-learning method based on computed tomography (CT) images. The authors applied a radiogenomics cohort to reveal the key pathways underlying the Rad-TIL model. Finally, the authors used an independent treatment cohort to evaluate the predictive efficacy of the Rad-TIL model for Bacillus Calmette-Guérin (BCG) immunotherapy.

RESULTS

The authors developed the Rad-TIL model by integrating tumoral and peritumoral features on CT images and obtained areas under the receiver operating characteristic curves of 0.844 and 0.816 in the internal and external validation cohorts, respectively. Patients were stratified into two groups based on the predicted radiomics score of TILs (RSTIL). RSTIL exhibited prognostic significance for both overall and cancer-specific survival in each cohort (hazard ratios: 2.27-3.15, all P<0.05). Radiogenomics analysis revealed a significant association of RSTIL with immunoregulatory pathways and immune checkpoint molecules (all P<0.05). Notably, BCG immunotherapy response rates were significantly higher in high-RSTIL patients than in low-RSTIL patients (P=0.007).

CONCLUSION

The Rad-TIL model, a noninvasive method for assessing TIL status, can predict clinical outcomes and BCG immunotherapy response in patients with bladder cancer.

Evaluation of radiomics as a predictor of efficacy and the tumor immune microenvironment in anti-PD-1 mAb treated recurrent/metastatic squamous cell carcinoma of the head and neck patients

BACKGROUND

We retrospectively evaluated radiomics as a predictor of the tumor microenvironment (TME) and efficacy with anti-PD-1 mAb (IO) in R/M HNSCC.

METHODS

Radiomic feature extraction was performed on pre-treatment CT scans segmented using 3D slicer v4.10.2 and key features were selected using LASSO regularization method to build classification models with XGBoost algorithm by incorporating cross-validation techniques to calculate accuracy, sensitivity, and specificity. Outcome measures evaluated were disease control rate (DCR) by RECIST 1.1, PFS, and OS and hypoxia and CD8 T cells in the TME.

RESULTS

Radiomics features predicted DCR with accuracy, sensitivity, and specificity of 76%, 73%, and 83%, for OS 77%, 86%, 70%, PFS 82%, 75%, 89%, and in the TME, for high hypoxia 80%, 88%, and 72% and high CD8 T cells 91%, 83%, and 100%, respectively.

CONCLUSION

Radiomics accurately predicted the efficacy of IO and features of the TME in R/M HNSCC. Further study in a larger patient population is warranted.

Prognostic value of enhanced cine cardiac MRI-based radiomics in dilated cardiomyopathy

BACKGROUND

Early precise identification of high-risk dilated cardiomyopathy (DCM) phenotype is essential for clinical decision-making and patient surveillance. The aim of the study was to assess the prognostic value of enhanced cine cardiac magnetic resonance (CMR)-based radiomics in DCM.

METHODS

We prospectively enrolled 401 (training set: 281; test set: 120) DCM patients. Radiomic features were extracted from enhanced cine images of entire left ventricular wall and selected by the least absolute shrinkage and selection operator. Different predictive models were built using logistic regression classifier to predict all-cause mortality and heart transplantation. Model performances were compared with the area under the receiver operating characteristic curves (AUCs). Kaplan-Meier curves, log-rank test, and Cox regression were used for survival analysis.

RESULTS

Endpoint events occurred in 65 patients over a median follow-up period of 25.4 months. 13 radiomic features were finally selected. The Rad_Combined model integrating clinical characteristics, CMR parameters and radiomics features achieved the best performance with an AUC of 0.836 and 0.835 in the training and test sets, respectively. High-risk groups with endpoint events defined by the Rad_Combined model had significantly shorter survival time than low-risk group in both the training [Hazard Ratio (HR) = 7.74, P < 0.001] and test sets (HR = 4.84, P < 0.001).

CONCLUSION

The Rad_Combined model might serve as an effective tool to help risk stratification and clinical decision-making for patients with DCM.

TRIAL REGISTRATION

Chinese Clinical Trial Registry, ChiCTR1800017058 by the ethics committee of West China hospital,Sichuan University.