A combined predictive model based on radiomics features and clinical factors for disease progression in early-stage non-small cell lung cancer treated with stereotactic ablative radiotherapy

A nomogram predicts the new high-grade patterns of pulmonary invasive non-mucinous adenocarcinoma based on the radiomics and clinical features

OBJECTIVES

To develop a nomogram for the prediction of the new high-grade patterns of invasive non-mucinous adenocarcinoma (INMA) based on the radiomics and clinical features to provide accurate individualized treatment for patients.

METHODS

We collected patients pathologically diagnosed with INMA at our hospital. The study's endpoint, defined as 'new high-grade', was characterized by the presence of micropapillary patterns at ≥5% or high-grade patterns (including solid, micropapillary, and complex glandular) at ≥20%. Patients were randomly divided into training and validation cohorts in a ratio of 8:2. The region of interest (ROI) of chest plain scan images was sketched using 3D slicer software. The image and clinical features were analyzed by Least Absolute Shrinkage and Selection Operator (LASSO), univariate, and multivariate regression to construct the radiomics signature and nomogram model. The nomogram model was validated using the validation cohort.

RESULTS

A total of 226 patients were divided into training (n = 180) and validation (n = 46) cohorts. From the ROI of these patients, 107 image features were extracted. LASSO regression analysis identified 16 image features that were used to construct the radiomics signature. The area under the curve values for the radiomics signature in the training and validation cohorts were 0.803 and 0.772, respectively. The Harrell's concordance index for the model, with 95% confidence intervals (CI), was 0.815 (CI: 0.806-0.824) for the training cohort and 0.802 (CI: 0.761-0.843) for the validation cohort.

CONCLUSIONS

The radiomics prediction model demonstrates strong predictive capabilities and could serve as a valuable tool for guiding personalized surgical treatment strategies for patients with INMA.

Long-term survival and failure patterns in inoperable early-stage non-small cell lung cancer following stereotactic body radiotherapy: a single-institution retrospective study

This study is to analyse the failure patterns and long-term survival after stereotactic body radiotherapy (SBRT) in patients with T1-3N0M0 inoperable non-small cell lung cancer (NSCLC). Early-stage NSCLC patitents who received SBRT at Zhejiang Cancer Hospital from January 2012 to September 2018 were retrospectively analyzed. The primary endpoint were the patterns of disease progression, which were divided into local recurrence, regional failure, and distant metastasis. Kaplan-Meier method survival analysis was used to calculate overall survival (OS), progression-free survival (PFS). Cox model was used for univariate analysis and multivariate analysis. A total of 215 patients with 224 lesions were enrolled. After the median follow-up time of 50.8 months (1.0-117.9 months), 76 (35.3%) patients progressed, with regional progression occurring in 4 cases (1.8%), local and local-regional progression in 17 cases (7.9%), various distant metastases developing in 55 cases (25.6%). The OS rates at 1, 3, and 5 years were 97.1%, 80.9%, and 63.8%, respectively, with a median OS of 92.2 months (95%CI, 61.5-122.9 months). The PFS rates at 1, 3, and 5 years were 87.5%, 65.9%, and 50.8%, respectively, with a median PFS of 62.2 months (95% CI, 45.0-59.4 months). There was no significant difference in OS (P = 0.832) and PFS (P = 0.672) between the two groups with or without pathology. Multivariate analysis showed that BED and patient age were independent prognostic factors affecting early-stage lung cancer survival (all P < 0.05). Distant metastasis was the main failure pattern of inoperable early-stage NSCLC after SBRT, and the high-risk population should be selected for further systemic treatment.

Can Patient Characteristics and Pre-Treatment MRI Features Predict Survival After Stereotactic Ablative Radiotherapy (SABR) Treatment in Hepatocellular Carcinoma (HCC): Preliminary Assessment

BACKGROUND

The study purpose was to develop a machine learning (ML)-based predictive model for event-free survival (EFS) in patients with hepatocellular carcinoma (HCC) undergoing stereotactic ablative radiotherapy (SABR).

METHODS

Patients receiving SABR for HCC at a single institution, between 2017 and 2020, were included in the study. They were split into training and test (85%:15%) cohorts. Events of interest were HCC recurrence or death. Three ML models were trained, the features were selected, and the hyperparameters were tuned. The performance was measured using Harrell's C index with the best-performing model being tested on the unseen cohort.

RESULTS

Overall, 41 patients were included (training = 34, test = 7) and 64 lesions were analysed (training = 50, test = 14), resulting in 30 events (60% rate) in the training set (death = 6, recurrence = 24) and 8 events (57% rate) in the test set (death = 5, recurrence = 3). A Cox regression model, using age at treatment, albumin, and intra-lesional fat identified through MRI as variables, had the best performance with a mean training score of 0.78 (standard deviation (SD) 0.02), a mean validation of 0.78 (SD 0.18), and a test score of 0.94.

CONCLUSIONS

Predicting the outcomes in patients with HCC, following SABR, using a novel model is feasible and warrants further evaluation.

Radiogenomics models for predicting prognosis in locally advanced non-small cell lung cancer patients undergoing definitive chemoradiotherapy

Background

Definitive chemoradiotherapy (dCRT) is the cornerstone for locally advanced non-small cell lung cancer (LA-NSCLC). The study aimed to construct a multi-omics model integrating baseline clinical data, computed tomography (CT) images and genetic information to predict the prognosis of dCRT in LA-NSCLC patients.

Methods

The study retrospectively enrolled 105 stage III LA-NSCLC patients who had undergone dCRT. The pre-treatment CT images were collected, and the primary tumor was delineated as a region of interest (ROI) on the image using 3D-Slicer, and the radiomics features were extracted. The least absolute shrinkage and selection operator (LASSO) was employed for dimensionality reduction and selection of features. Genomic information was obtained from the baseline tumor tissue samples. We then constructed a multi-omics model by combining baseline clinical data, radiomics and genomics features. The predictive performance of the model was evaluated by the area under the curve (AUC) of the receiver operating characteristic (ROC) and the concordance index (C-index).

Results

The median follow-up time was 30.1 months, and the median progression-free survival (PFS) was 10.60 months. Four features were applied to construct the radiomics model. Multivariable analysis demonstrated the Rad-score, KEAP1 and MET mutations were independent prognostic factors for PFS. The C-index of radiomics model, genomics model and radiogenomics model all performed well in the training group (0.590 vs. 0.606 vs. 0.663) and the validation group (0.599 vs. 0.594 vs. 0.650).

Conclusions

The radiomics model, genomics model and radiogenomics model can all predict the prognosis of dCRT for LA-NSCLC, and the radiogenomics model is superior to the single type model.

2.5D peritumoural radiomics predicts postoperative recurrence in stage I lung adenocarcinoma

Objective

Radiomics can non-invasively predict the prognosis of a tumour by applying advanced imaging feature algorithms.The aim of this study was to predict the chance of postoperative recurrence by modelling tumour radiomics and peritumour radiomics and clinical features in patients with stage I lung adenocarcinoma (LUAD).

Materials and methods

Retrospective analysis of 190 patients with postoperative pathologically confirmed stage I LUAD from centre 1, who were divided into training cohort and internal validation cohort, with centre 2 added as external validation cohort. To develop a combined radiation-clinical omics model nomogram incorporating clinical features based on images from low-dose lung cancer screening CT plain for predicting postoperative recurrence and to evaluate the performance of the nomogram in the training cohort, internal validation cohort and external validation cohort.

Results

A total of 190 patients were included in the model in centre 1 and randomised into a training cohort of 133 and an internal validation cohort of 57 in a ratio of 7:3, and 39 were included in centre 2 as an external validation cohort. In the training cohort (AUC=0.865, 95% CI 0.824-0.906), internal validation cohort (AUC=0.902, 95% CI 0.851-0.953) and external validation cohort (AUC=0.830,95% CI 0.751-0.908), the combined radiation-clinical omics model had a good predictive ability. The combined model performed significantly better than the conventional single-modality models (clinical model, radiomic model), and the calibration curve and decision curve analysis (DCA) showed high accuracy and clinical utility of the nomogram.

Conclusion

The combined preoperative radiation-clinical omics model provides good predictive value for postoperative recurrence in stage ILUAD and combines the model's superiority in both internal and external validation cohorts, demonstrating its potential to aid in postoperative treatment strategies.

Artificial Intelligence-suggested Predictive Model of Survival in Patients Treated With Stereotactic Radiotherapy for Early Lung Cancer

BACKGROUND/AIM

Overall survival (OS)-predictive models to clinically stratify patients with stage I Non-Small Cell Lung Cancer (NSCLC) undergoing stereotactic body radiation therapy (SBRT) are still unavailable. The aim of this work was to build a predictive model of OS in this setting.

PATIENTS AND METHODS

Clinical variables of patients treated in three Institutions with SBRT for stage I NSCLC were retrospectively collected into a reference cohort A (107 patients) and 2 comparative cohorts B1 (32 patients) and B2 (38 patients). A predictive model was built using Cox regression (CR) and artificial neural networks (ANN) on reference cohort A and then tested on comparative cohorts.

RESULTS

Cohort B1 patients were older and with worse chronic obstructive pulmonary disease (COPD) than cohort A. Cohort B2 patients were heavier smokers but had lower Charlson Comorbidity Index (CCI). At CR analysis for cohort A, only ECOG Performance Status 0-1 and absence of previous neoplasms correlated with better OS. The model was enhanced combining ANN and CR findings. The reference cohort was divided into prognostic Group 1 (0-2 score) and Group 2 (3-9 score) to assess model's predictions on OS: grouping was close to statistical significance (p=0.081). One and 2-year OS resulted higher for Group 1, lower for Group 2. In comparative cohorts, the model successfully predicted two groups of patients with divergent OS trends: higher for Group 1 and lower for Group 2.

CONCLUSION

The produced model is a relevant tool to clinically stratify SBRT candidates into prognostic groups, even when applied to different cohorts. ANN are a valuable resource, providing useful data to build a prognostic model that deserves to be validated prospectively.

Prognostic Value of 18F-FDG PET/CT Radiomics in Extranodal Nasal-Type NK/T Cell Lymphoma

OBJECTIVE

To investigate the prognostic utility of radiomics features extracted from 18F-fluorodeoxyglucose (FDG) PET/CT combined with clinical factors and metabolic parameters in predicting progression-free survival (PFS) and overall survival (OS) in individuals diagnosed with extranodal nasal-type NK/T cell lymphoma (ENKTCL).

MATERIALS AND METHODS

A total of 126 adults with ENKTCL who underwent 18F-FDG PET/CT examination before treatment were retrospectively included and randomly divided into training (n = 88) and validation cohorts (n = 38) at a ratio of 7:3. Least absolute shrinkage and selection operation Cox regression analysis was used to select the best radiomics features and calculate each patient's radiomics scores (RadPFS and RadOS). Kaplan-Meier curve and Log-rank test were used to compare survival between patient groups risk-stratified by the radiomics scores. Various models to predict PFS and OS were constructed, including clinical, metabolic, clinical + metabolic, and clinical + metabolic + radiomics models. The discriminative ability of each model was evaluated using Harrell's C index. The performance of each model in predicting PFS and OS for 1-, 3-, and 5-years was evaluated using the time-dependent receiver operating characteristic (ROC) curve.

RESULTS

Kaplan-Meier curve analysis demonstrated that the radiomics scores effectively identified high- and low-risk patients (all P < 0.05). Multivariable Cox analysis showed that the Ann Arbor stage, maximum standardized uptake value (SUVmax), and RadPFS were independent risk factors associated with PFS. Further, β2-microglobulin, Eastern Cooperative Oncology Group performance status score, SUVmax, and RadOS were independent risk factors for OS. The clinical + metabolic + radiomics model exhibited the greatest discriminative ability for both PFS (Harrell's C-index: 0.805 in the validation cohort) and OS (Harrell's C-index: 0.833 in the validation cohort). The time-dependent ROC analysis indicated that the clinical + metabolic + radiomics model had the best predictive performance.

CONCLUSION

The PET/CT-based clinical + metabolic + radiomics model can enhance prognostication among patients with ENKTCL and may be a non-invasive and efficient risk stratification tool for clinical practice.