Incorporating the inflammation-related parameters enhances the performance of the nomogram for predicting local control in lung cancer patients treated with stereotactic body radiation therapy.

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.

Developing a clinical-radiomic prediction model for 3-year cancer-specific survival in lung cancer patients treated with stereotactic body radiation therapy.

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.

Developing a Prediction Model for Radiation Pneumonitis in Lung Cancer Patients Treated With Stereotactic Body Radiation Therapy Combined With Clinical, Dosimetric Factors, and Laboratory Biomarkers.

BACKGROUND: The study aims to identify the risk factors and develop a model for predicting grade ≥2 radiation pneumonitis (RP) for lung cancer patients treated with stereotactic body radiation therapy (SBRT). MATERIALS AND METHODS: Clinical data, dosimetric data, and laboratory biomarkers from 186 patients treated with lung SBRT were collected. Univariate and multivariate logistic regression were performed to determine the predictive factors for grade ≥2 RP. Three models were developed by using the clinical, dosimetric, and combined factors, respectively. RESULTS: With a median follow-up of 36 months, grade ≥2 RP was recorded in 13.4% of patients. On univariate logistic regression analysis, clinical factors of age and lung volume, dosimetric factors of treatment durations, fractional dose and V10, and laboratory biomarkers of neutrophil, PLT, PLR, and Hb levels were significantly associated with grade ≥2 RP. However, on multivariate analysis, only age, lung volume, fractional dose, V10, and Hb levels were independent factors. AUC values for the clinical, dosimetric, and combined models were 0.730 (95% CI, 0.660-0.793), 0.711 (95% CI, 0.641-0.775) and 0.830 (95% CI, 0.768-0.881), respectively. The combined model provided superior discriminative ability than the clinical and dosimetric models (P < .05). CONCLUSION: Age, lung volume, fractional dose, V10, and Hb levels were demonstrated to be significant factors associated with grade ≥2 RP for lung cancer patients after SBRT. A novel model combining clinical, dosimetric factors, and laboratory biomarkers improved predictive performance compared with the clinical and dosimetric model alone.

The Clinical Outcomes, Prognostic Factors and Nomogram Models for Primary Lung Cancer Patients Treated With Stereotactic Body Radiation Therapy.

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.

Comparison of Three Radiobiological Models in Stereotactic Body Radiotherapy for Non-Small Cell Lung Cancer.

Objective: The applicability of the linear quadratic (LQ) model to local control (LC) modeling after hypofractionated radiotherapy to treat lung cancer is highly debated. To date, the differences in predicted outcomes between the LQ model and other radiobiological models, which are characterized by additional dose modification beyond a certain transitional dose (dT), have not been well established. This study aims to compare the outcomes predicted by the LQ model with those predicted by two other radiobiological models in stereotactic body radiotherapy (SBRT) for non-small cell lung cancer (NSCLC). Methods: Computer tomography (CT) simulation data sets for 20 patients diagnosed with stage Ⅰ primary NSCLC were included in this study. Three radiobiological models, including the LQ, the universal survival curve (USC) and the modified linear quadratic and linear (mLQL) model were employed to predict the tumor control probability (TCP) data. First, the dT values for the USC and mLQL models were determined. Then, the biologically effective dose (BED) and the predicted TCP values from the LQ model were compared with those calculated from the USC and mLQL models. Results: The dT values from the USC model were 29.6 Gy, 33.8 Gy and 44.5 Gy, whereas the values were 90.2 Gy, 84.0 Gy and 57.3 Gy for the mLQL model for 1-year, 2-year and 3-year TCP prediction. The remarkable higher dT values obtained from the mLQL model revealed the same dose-response relationship as the LQ model in the low- and high-dose ranges. We also found that TCP prediction from the LQ and USC models differed by less than 3%, although the BED values for the two models were significantly different. Conclusion: Radiobiological analysis reveals small differences between the models and suggested that the LQ model is applicable for modeling LC using SBRT to treat lung cancer, even when an extremely high fractional dose is used.

Calculating the individualized fraction regime in stereotactic body radiotherapy for non-small cell lung cancer based on uncomplicated tumor control probability function.

BACKGROUND: To calculate the individualized fraction regime (IFR) in stereotactic body radiotherapy (SBRT) for non-small cell lung cancer (NSCLC) patients using the uncomplicated tumor control probability (UTCP, P+) function. METHODS: Thirty-three patients with peripheral lung cancer or lung metastases who had undergone SBRT were analyzed. Treatment planning was performed using the dose regime of 48 Gy in 4 fractions. Dose volume histogram (DVH) data for the gross tumor volume (GTV), lung, chest wall (CW) and rib were exported and the dose bin was multiplied by a certain percentage of the dose in that bin which ranged from 1 to 200% in steps of 1%. For each dose fraction, P+ values were calculated by considering the tumor control probability (TCP), radiation-induced pneumonitis (RIP), chest wall pain (CWP) and radiation-induced rib fracture (RIRF). UTCP values as a function of physical dose were plotted and the maximum P+ values corresponded to the optimal therapeutic gain. The IFR in 3 fractions was also calculated with the same method by converting the dose using the linear quadratic (LQ) model. RESULTS: Thirty-three patients attained an IFR using the introduced methods. All the patients achieved a TCP value higher than 92.0%. The IFR ranged from 3 × 10.8 Gy to 3 × 12.5 Gy for 3 fraction regimes and from 4 × 9.2 Gy to 4 × 10.7 Gy for 4 fraction regimes. Four patients with typical tumor characteristics demonstrated that the IFR was patient-specific and could maximize the therapeutic gain. Patients with a large tumor had a lower TCP and UTCP and a smaller fractional dose than patients with a small tumor. Patients with a tumor adjacent to the organ at risk (OAR) or at a high risk of RIP had a lower UTCP and a smaller fractional dose compared with patients with a tumor located distant from the OAR. CONCLUSIONS: The proposed method is capable of predicting the IFR for NSCLC patients undergoing SBRT. Further validation in clinical samples is required.

Comparative Analysis of Local Control Prediction Using Different Biophysical Models for Non-Small Cell Lung Cancer Patients Undergoing Stereotactic Body Radiotherapy.

PURPOSE: The consistency for predicting local control (LC) data using biophysical models for stereotactic body radiotherapy (SBRT) treatment of lung cancer is unclear. This study aims to compare the results calculated from different models using the treatment planning data. MATERIALS AND METHODS: Treatment plans were designed for 17 patients diagnosed with primary non-small cell lung cancer (NSCLC) using 5 different fraction schemes. The Martel model, Ohri model, and the Tai model were used to predict the 2-year LC value. The Gucken model, Santiago model, and the Tai model were employed to estimate the 3-year LC data. RESULTS: We found that the employed models resulted in completely different LC prediction except for the Gucken and the Santiago models which exhibited quite similar 3-year LC data. The predicted 2-year and 3-year LC values in different models were not only associated with the dose normalization but also associated with the employed fraction schemes. The greatest difference predicted by different models was up to 15.0%. CONCLUSIONS: Our results show that different biophysical models influence the LC prediction and the difference is not only correlated to the dose normalization but also correlated to the employed fraction schemes.

Radiobiological evaluation of simultaneously dose-escalated versus non-escalated intensity-modulated radiation therapy for patients with upper thoracic esophageal cancer.

OBJECTIVE: To compare the radiobiological response between simultaneously dose-escalated and non-escalated intensity-modulated radiation therapy (DE-IMRT and NE-IMRT) for patients with upper thoracic esophageal cancer (UTEC) using radiobiological evaluation. METHODS: Computed tomography simulation data sets for 25 patients pathologically diagnosed with primary UTEC were used in this study. DE-IMRT plan with an escalated dose of 64.8 Gy/28 fractions to the gross tumor volume (GTV) and involved lymph nodes from 25 patients pathologically diagnosed with primary UTEC, was compared to an NE-IMRT plan of 50.4 Gy/28 fractions. Dose-volume metrics, tumor control probability (TCP), and normal tissue complication probability for the lung and spinal cord were compared. In addition, the risk of acute esophageal toxicity (AET) and late esophageal toxicity (LET) were also analyzed. RESULTS: Compared with NE-IMRT plan, we found the DE-IMRT plan resulted in a 14.6 Gy dose escalation to the GTV. The tumor control was predicted to increase by 31.8%, 39.1%, and 40.9% for three independent TCP models. The predicted incidence of radiation pneumonitis was similar (3.9% versus 3.6%), and the estimated risk of radiation-induced spinal cord injury was extremely low (<0.13%) in both groups. Regarding the esophageal toxicities, the estimated grade ≥2 and grade ≥3 AET predicted by the Kwint model were increased by 2.5% and 3.8%. Grade ≥2 AET predicted using the Wijsman model was increased by 14.9%. The predicted incidence of LET was low (<0.51%) in both groups. CONCLUSION: Radiobiological evaluation reveals that the DE-IMRT dosing strategy is feasible for patients with UTEC, with significant gains in tumor control and minor or clinically acceptable increases in radiation-induced toxicities.

Different definitions of esophagus influence esophageal toxicity prediction for esophageal cancer patients administered simultaneous integrated boost versus standard-dose radiation therapy.

We aim to evaluate whether different definitions of esophagus (DEs) impact on the esophageal toxicity prediction for esophageal cancer (EC) patients administered intensity-modulated radiation therapy with simultaneous integrated boost (SIB-IMRT) vs. standard-dose IMRT (SD-IMRT). The esophagus for 21 patients diagnosed with primary EC were defined in the following four ways: the whole esophagus, including the tumor (ESOwhole); ESOwhole within the treatment field (ESOinfield); ESOinfield, excluding the tumor (ESOinfield-tumor) and ESOwhole, excluding the tumor (ESOwhole-tumor). The difference in the dose variation, acute esophageal toxicity (AET) and late esophageal toxicity (LET) of four DEs were compared. We found that the mean esophageal dose for ESOwhole, ESOinfield, ESOinfield-tumor and ESOwhole-tumor were increased by 7.2 Gy, 10.9 Gy, 4.6 Gy and 2.0 Gy, respectively, in the SIB-IMRT plans. Radiobiological models indicated that a grade ≥ 2 AET was 2.9%, 3.1%, 2.2% and 1.6% higher on average with the Kwint model and 14.6%, 13.2%, 7.2% and 3.4% higher with the Wijsman model for the four DEs. A grade ≥ 3 AET increased by 4.3%, 7.2%, 4.2% and 1.2%, respectively. Additionally, the predicted LET increased by 0.15%, 0.39%, 1.2 × 10-2% and 1.5 × 10-3%. Our study demonstrates that different DEs influence the esophageal toxicity prediction for EC patients administered SIB-IMRT vs. SD-IMRT treatment.

Radiobiological modeling of two stereotactic body radiotherapy schedules in patients with stage I peripheral non-small cell lung cancer.

This study aims to compare the radiobiological response of two stereotactic body radiotherapy (SBRT) schedules for patients with stage I peripheral non-small cell lung cancer (NSCLC) using radiobiological modeling methods. Volumetric modulated arc therapy (VMAT)-based SBRT plans were designed using two dose schedules of 1 × 34 Gy (34 Gy in 1 fraction) and 4 × 12 Gy (48 Gy in 4 fractions) for 19 patients diagnosed with primary stage I NSCLC. Dose to the gross target volume (GTV), planning target volume (PTV), lung and chest wall (CW) were converted to biologically equivalent dose in 2 Gy fraction (EQD2) for comparison. Five different radiobiological models were employed to predict the tumor control probability (TCP) value. Three additional models were utilized to estimate the normal tissue complication probability (NTCP) value for the lung and the modified equivalent uniform dose (mEUD) value to the CW. Our result indicates that the 1 × 34 Gy dose schedule provided a higher EQD2 dose to the tumor, lung and CW. Radiobiological modeling revealed that the TCP value for the tumor, NTCP value for the lung and mEUD value for the CW were 7.4% (in absolute value), 7.2% (in absolute value) and 71.8% (in relative value) higher on average, respectively, using the 1 × 34 Gy dose schedule.

Dosimetric evaluation of a simple planning method for improving intensity-modulated radiotherapy for stage III lung cancer.

This study aimed to evaluate the dosimetric outcomes of a base-dose-plan-compensation (BDPC) planning method for improving intensity-modulated radiotherapy (IMRT) for stage III lung cancer. For each of the thirteen included patients, three types of planning methods were applied to obtain clinically acceptable plans: (1) the conventional optimization method (CO); (2) a split-target optimization method (STO), in which the optimization objectives were set higher dose for the target with lung density; (3) the BDPC method, which compensated for the optimization-convergence error by further optimization based on the CO plan. The CO, STO and BDPC methods were then compared regarding conformity index (CI), homogeneity index (HI) of the target, organs at risk (OARs) sparing and monitor units (MUs). The BDPC method provided better HI/CI by 54%/7% on average compared to the CO method and by 38%/3% compared to the STO method. The BDPC method also spared most of the OARs by up to 9%. The average MUs of the CO, STO and BDPC plans were 890, 937 and 1023, respectively. Our results indicated that the BDPC method can effectively improve the dose distribution in IMRT for stage III lung cancer, at the expense of more MUs.

Radiobiological modeling analysis of the optimal fraction scheme in patients with peripheral non-small cell lung cancer undergoing stereotactic body radiotherapy.

This study aimed to determine the optimal fraction scheme (FS) in patients with small peripheral non-small cell lung cancer (NSCLC) undergoing stereotactic body radiotherapy (SBRT) with the 4 × 12 Gy scheme as the reference. CT simulation data for sixteen patients diagnosed with primary NSCLC or metastatic tumor with a single peripheral lesion ≤3 cm were used in this study. Volumetric modulated arc therapy (VMAT) plans were designed based on ten different FS of 1 × 25 Gy, 1 × 30 Gy, 1 × 34 Gy, 3 × 15 Gy, 3 × 18 Gy, 3 × 20 Gy, 4 × 12 Gy, 5 × 12 Gy, 6 × 10 Gy and 10 × 7 Gy. Five different radiobiological models were employed to predict the tumor control probability (TCP) value. Three other models were utilized to estimate the normal tissue complication probability (NTCP) value to the lung and the modified equivalent uniform dose (mEUD) value to the chest wall (CW). The 1 × 30 Gy regimen is recommended to achieve 4.2% higher TCP and slightly higher NTCP and mEUD values to the lung and CW compared with the 4 × 12 Gy schedule, respectively. This regimen also greatly shortens the treatment duration. However, the 3 × 15 Gy schedule is suggested in patients where the lung-to-tumor volume ratio is small or where the tumor is adjacent to the CW.

Monitor unit optimization in stereotactic body radiotherapy for small peripheral non-small cell lung cancer patients.

The increasingly attractive stereotactic body radiotherapy (SBRT) treatment for stage I lung cancer is concomitant with a large amount of monitor units (MU), leading to excessive out-of-field dose and prolonged beam-on time. The study aims to reduce the MU number and shorten the beam-on time by optimizing the planning parameters. Clinically acceptable treatment plans from fourteen patients suffered from peripheral stage I non-small cell lung cancer (NSCLC) were created in the study. Priority for the upper objective of the target (PUOT), strength and Max MU setting in the MU objective function (MUOF) were adjusted respectively to investigate their effect on MU number, organs at risk (OARs) sparing and beam-on time. We found that the planning parameters influenced the MU number in a PUOT, strength and Max MU dependent manner. Combined with high priority for the UOT (HPUOT) and MUOF, the MU number was reduced from 443 ± 25 to 228 ± 22 MU/Gy without compromising the target coverage and OARs sparing. We also found beam-on time was proportional to MU number and it could be shortened from 7.9 ± 0.5 to 4.1 ± 0.4 minutes.

Comparison of Two RapidArc Delivery Strategies in Stereotactic Body Radiotherapy of Peripheral Lung Cancer with Flattening Filter Free Beams.

PURPOSE: To investigate the performance of using partial arc (PA) and full arc with avoidance sectors (FAAS) in stereotactic body radiotherapy (SBRT) of peripheral lung cancer with flattening filter free (FFF) beams. METHODS: Eighteen patients with primary (T1 or T2) non-small-cell lung cancer (NSCLC) or lung metastatic were selected for this study. Nine patients with a gross tumor volume (GTV) <= 10 cc were designated as the small tumor group. The other nine patients with a GTV between 10 cc and 44 cc were assigned to the large tumor group. The treatment plans were generated in eighteen patients using PA and FAAS techniques, respectively, and delivered with a Varian TrueBeam Linac. Dosimetry of the target and organs at risk (OARs), monitor unit (MU), out-of-field dose, and delivery time were statistically analyzed. Delta4 and portal dosimetry were employed to evaluate the delivery accuracy. RESULTS: For the small tumor group, compared with the PA plans, the FAAS plans significantly achieved a lower MU/fraction, out-of-field dose and a shorter treatment time (p<0.05), but the target dose was slightly higher than that delivered by PA plans (p<0.05). For the large tumor group, the PA plans significantly attained a shorter treatment time (p<0.05), whereas MU/fraction, out-of-field dose and dose to OARs were comparable between the two plans (p>0.05). Furthermore, all plans generated from the eighteen patients achieved a high pass rate in patient-specific quality assurance, with all the gamma indices greater than 97% at the Γ3mm, 3% threshold. CONCLUSION: This study suggests that the FAAS technique is more beneficial for the small tumor patients undergoing lung SBRT with FFF beams because of its higher treatment efficiency and MU reduction. However, for the large tumor patients, the PA technique is recommended due to its higher treatment efficiency.

[Application of Excel programs of Fisher exact probability test for medical data].

OBJECTIVE: To accomplish the computation of Fisher exact probability test for fourfold table data in Excel. METHOD: The computing program of exact probability method for medical data in fourfold table design was edited by employing the IF statement and the relevant functions, such as SUM, FACT, DSUM, etc in Excel. The computational results are compared and evaluated according to the case studies. RESULTS: The output of Fisher Exact Probability was generated and presented correctly following the input of four numerical values into the computation program in the setting of Excel. The parametric outcomes are in agreement with those produced by SAS and SPSS, in the combination tables containing the P value, two-tailed cumulative P value, left-tailed P-value, right-tailed P-value, Chi2 values and P values both for direct Chi-squared test and corrected Chi-squared test. CONCLUSIONS: Direct Chi-squared test, corrected Chi-squared test combined with Fisher Exact Probability test for fourfold table data can be conveniently, rapidly, and accurately accomplished in Excel.