Bao-Tian Huang

Accurate prediction of local recurrence (LR) after stereotactic body radiation therapy (SBRT) for lung cancer remains challenging, and conventional radiobiological models have shown limited predictive performance. This study aimed to develop a clinical-radiomic model for LR prediction and compare its performance with existing radiobiological models. A total of 153 SBRT-treated lung cancer patients were retrospectively analyzed. Radiobiological models were used to estimate 2-year and 3-year local control (LC) probabilities, which were subsequently converted to LR probabilities using the relationship P(LR) = 1 − P(LC) for comparison with other models. Radiomic feature selection and model construction were performed using LASSO regression with tenfold cross-validation. A clinical-radiomic model was built by integrating clinical factors with the radiomic score (Radscore), followed by nomogram development. Model performance was evaluated using ROC analysis and calibration plots, with internal validation performed through bootstrap resampling. Clinical utility was evaluated using bootstrapped ROC, calibration, and decision curve analysis (DCA). The clinical-radiomic model showed higher AUC values than the radiobiological and radiomic models alone for both 2-year and 3-year LR predictions (all P < 0.05). Nomogram-derived risk scores differed significantly between the recurrence and non-recurrence groups. Bootstrap resampling suggested stable discriminatory performance, acceptable calibration, and potential decision-analytic performance. The clinical-radiomic model may show potential for individualized LR risk stratification in lung cancer patients receiving SBRT. However, further external validation in prospective multicenter studies is required before routine clinical application.

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Predicting local recurrence after stereotactic body radiation therapy (SBRT) for lung cancer remains challenging. This study aims to develop a machine lear

The incidence of local recurrence remains noteworthy among lung cancer patients treated with stereotactic body radiation therapy (SBRT). The aim of the study is to identify the risk factors and develop a nomogram for local control (LC) prediction. One hundred fifty-eight primary or metastatic lung cancer patients treated with SBRT were retrospectively analyzed. The clinical, dosimetric and inflammation-related parameters were collected. The Cox regression analysis was performed to determine the independent prognostic factors. A nomogram based on the prognostic factors was established and internally validated using a bootstrap resampling method. The median follow-up time for the whole cohort was 40 months (95% CI: 34–46) and 35.4% of the patients (56/158) experienced local recurrence. The 1-year, 3-year and 5-year LC rates were 97.4%, 85.8% and 76.1%. Multivariate Cox regression analysis revealed that six independent factors were associated with LC, including age, clinical stage, planning target volume (PTV) volume, BED of the prescription dose (BEDPD), lymphocyte count, and neutrocyte count. The bootstrap-corrected C-index of the developed nomogram was 0.745 (95% CI, 0.663–0.793). The time-dependent AUC indicated the nomogram exhibited strong discriminatory capability. Calibration curves demonstrated a good concordance between the predicted and the observed probabilities. The results of decision curve analysis highlighted the clinical utility of the model. Additionally, the high- and low-risk patients were stratified based on the cut-off point from the nomogram (P < 0.0001). A nomogram based on the clinical, dosimetric, and inflammation-related predictors is developed for LC prediction in lung cancer patients treated with SBRT. External validation is required for further confirm its validity.

Background: The debate regarding the accuracy of radiobiological models for local control (LC) prediction in lung cancer patients undergoing stereotactic body radiation therapy (SBRT) remains unresolved. The study seeks to externally validate the predictive efficacy of radiobiological models using single-institutional SBRT database.Methods: The cohort comprised 153 patients diagnosed with primary or metastatic lung cancer who underwent SBRT. The study employed three radiobiological models to estimate the probability of 2-year LC, including the Liu model, Klement model, and Ohri model. Furthermore, the likelihood of 3-year LC was predicted using the Liu model, Klement model, Gucken model, and Santiago model. The performance of the prediction models was assessed through the AUC values of the receiver operating characteristic (ROC) curve and the calibration plots.Results: Local recurrence was observed in 38.6% of patients (59/153) within two years, and in 43.1% (66/153) within three years after the radiotherapy. The ROC curves indicated discriminative power for all the 2-year and 3-year models, with the exception of the Klement model. The Ohri model showed a significantly improved discriminative ability than the Klement model for 2-year prediction, while it was not statistically significant when compared to the Liu model. However, no significant differences were found among the four models in terms of 3-year LC prediction. The calibration plots, using the Hosmer-Lemeshow goodness-of-fit test, confirmed that the predicted probabilities of the models were in agreement with the actual observation with P>0.05, except for the 2-year LC prediction using the Klement model.Conclusion: Considering the balance between prediction accuracy and model simplicity, it is recommended to utilize the Ohri model for 2-year LC prediction and either the Gucken model or Santiago model for 3-year LC prediction.

Purpose: The study aims to investigate whether including the inflammation-related parameters would enhance the accuracy of a nomogram for local control (LC) prediction in lung cancer patients undergoing stereotactic body radiation therapy (SBRT). Methods: 158 primary or metastatic lung cancer patients treated with SBRT were retrospectively analyzed. The clinical, dosimetric and inflammation-related parameters were collected for the Cox regression analysis. The ACPB model was constructed by employing the clinical and dosimetric factors. And the ACPBLN model was established by adding the inflammation-related factors to the ACPB model. The two models were compared in terms of ROC, Akaike Information Criterion (AIC), C-index, time-dependent AUC, continuous net reclassification index (NRI), integrated discrimination improvement (IDI), calibration plots and decision curve analysis (DCA). Results: Multivariate Cox regression analysis revealed that six prognostic factors were independently associated with LC, including age, clinical stage, planning target volume (PTV) volume, BED of the prescribed dose (BEDPD), the lymphocyte count and neutrocyte count. The ACPBLN model performed better in AIC, bootstrap-corrected C-index, time-dependent AUC, NRI and IDI than the ACPB model. The calibration plots showed good consistency between the probabilities and observed values in the two models. The DCA curves showed that the ACPBLN nomogram had higher overall net benefit than the ACPB model across a majority of threshold probabilities. Conclusion: The inflammation-related parameters were associated with LC for lung cancer patients treated with SBRT. The inclusion of the inflammation-related parameters improved the predictive performance of the nomogram for LC prediction.

Purpose: The study aims to develop and validate a combined model for predicting 3-year cancer-specific survival (CSS) in lung cancer patients treated with stereotactic body radiation therapy (SBRT) by integrating clinical and radiomic parameters. Methods: Clinical data and pre-treatment CT images were collected from 102 patients treated with lung SBRT. Multivariate logistic regression and the least absolute shrinkage and selection operator were used to determine the clinical and radiomic factors associated with 3-year CSS. Three prediction models were developed using clinical factors, radiomic factors, and a combination of both. The performance of the models was assessed using receiver operating characteristic curve and calibration curve. A nomogram was also created to visualize the 3-year CSS prediction. Results: With a 36-month follow-up, 40 patients (39.2%) died of lung cancer and 62 patients (60.8%) survived. Three clinical factors, including gender, clinical stage, and lymphocyte ratio, along with three radiomic features, were found to be independent factors correlated with 3-year CSS. The area under the curve values for the clinical, radiomic, and combined model were 0.839 (95% CI 0.735–0.914), 0.886 (95% CI 0.790–0.948), and 0.914 (95% CI 0.825–0.966) in the training cohort, and 0.757 (95% CI 0.580–0.887), 0.818 (95% CI 0.648–0.929), and 0.843 (95% CI 0.677–0.944) in the validation cohort, respectively. Additionally, the calibration curve demonstrated good calibration performance and the nomogram created from the combined model showed potential for clinical utility. Conclusion: A clinical-radiomic model was developed to predict the 3-year CSS for lung cancer patients treated with SBRT.

Purpose: Stereotactic body radiation therapy (SBRT) is a standard treatment for early primary lung cancer patients. However, there are few simple models for predicting the clinical outcomes of these patients. Our study analyzed the clinical outcomes, identified the prognostic factors, and developed prediction nomogram models for these patients.Materials and Methods: We retrospectively analyzed 114 patients with primary lung cancer treated with SBRT from 2012 to 2020 at our institutions and assessed patient’s clinical outcomes and levels of toxicity. Kaplan–Meier analysis with a log-rank test was used to generate the survival curve. The cut-off values of continuous factors were calculated with the X-tile tool. Potential independent prognostic factors for clinical outcomes were explored using cox regression analysis. Nomograms for clinical outcomes prediction were established with identified factors and assessed by calibration curves.Results: The median overall survival (OS) was 40.6 months, with 3-year OS, local recurrence free survival (LRFS), distant disease-free survival (DDFS) and progression free survival (PFS) of 56.3%, 61.3%, 72.9% and 35.8%, respectively, with grade 3 or higher toxicity rate of 7%. The cox regression analysis revealed that the clinical stage, immobilization device, and the prescription dose covering 95% of the target area (D95) were independent prognostic factors associated with OS. Moreover, the clinical stage, and immobilization device were independent prognostic factors of LRFS and PFS. The smoking status, hemoglobin (Hb) and immobilization device were significant prognostic factors for DDFS. The nomograms and calibration curves incorporating the above factors indicated good predictive accuracy.Conclusions: SBRT is effective and safe for primary lung cancer. The prognostic factors associated with OS, LRFS, DDFS and PFS are proposed, and the nomograms we proposed are suitable for clinical outcomes prediction.

Purpose: Stereotactic body radiotherapy (SBRT) is an important treatment modality for lung cancer patients, however, tumor local recurrence rate remains some challenge and there is no reliable prediction tool. This study aims to develop a prediction model of local control for lung cancer patients undergoing SBRT based on radiomics signature combining with clinical and dosimetric parameters.Methods: The radiomics model, clinical model and combined model were developed by radiomics features, incorporating clinical and dosimetric parameters and radiomics signatures plus clinical and dosimetric parameters, respectively. Three models were established by logistic regression (LR), decision tree (DT) or support vector machine (SVM). The performance of models was assessed by receiver operating characteristic curve (ROC) and DeLong test. Furthermore, a nomogram was built and was assessed by calibration curve, Hosmer-Lemeshow and decision curve.Results: The LR method was selected for model establishment. The radiomics model, clinical model and combined model showed favorite performance and calibration (Area under the ROC curve (AUC) 0.811, 0.845 and 0.911 in the training group, 0.702, 0.786 and 0.818 in the validation group, respectively). The performance of combined model was significantly superior than the other two models. In addition, Calibration curve and Hosmer-Lemeshow (training group: P = 0.898, validation group: P = 0.891) showed good calibration of combined nomogram and decision curve proved its clinical utility.Conclusions: The combined model based on radiomics features plus clinical and dosimetric parameters can improve the prediction of 1-year local control for lung cancer patients undergoing SBRT.