Symptomatic radiation pneumonitis after stereotactic body radiotherapy for multiple pulmonary oligometastases or synchronous primary lung cancer

[Purpose] Stereotactic body radiation therapy (SBRT) can be easily used for patients with tumors in various organs and is a promising local therapy for eradicating tumors in cancer patients. There is a rising clinical need for increasing knowledge of oligometastases in the treatment of multiple pulmonary tumors. This study aimed to explore the predictive factors for symptomatic radiation pneumonitis (RP) after SBRT for multiple pulmonary oligometastases or synchronous primary lung cancer (SPLC). [Methods and Materials] A total of 38 consecutive patients who had 2 or more pulmonary oligometastases (n = 21) or SPLC (n = 17) and who were treated with SBRT were investigated. Patient characteristics, tumor characteristics, and details of radiation therapy were retrospectively collected from a clinical database. The association between RP of grade 2 or worse (grade 2+ RP) and clinical or dosimetric factors was assessed using logistic regression analyses. [Results] The tumors presented ipsilaterally in 24 patients and bilaterally in 14 patients. During the median follow-up period of 4.9 years, grade 2+ RP, grade 2 RP, and grade 3 RP were observed in 9 patients (23.7%), 7 patients (18.4%), and 2 patients (5.3%), respectively. The mean lung dose (MLD) and the volume of the normal lung receiving ≥5 Gy (lung V5Gy) were significantly associated with grade 2+ RP (P = .023 and P = .012, respectively). The logistic model showed that 20% and 50% of the predicted probability of grade 2+ RP were 6.1 Gy and 9.1 Gy for MLD and 31.6 % and 42.8% for lung V5Gy, respectively. [Conclusion] Although further investigation is required to validate the metrics and establish reliable dose constraints, the dose-volume metrics for the normal lung could be predictive of the development of grade 2+ RP after SBRT for multiple pulmonary oligometastases or SPLCs

Lack of an association between marital status and survival in patients receiving stereotactic body radiotherapy for early-stage non-small-cell lung cancer

Marital status has been proposed as a promising prognostic factor in many malignancies, including non-small-cell lung cancer (NSCLC). However, its prognostic value is still unclear for individual non-surgical treatments for stage I NSCLC. This study investigated the prognostic value of marital status in patients with early-stage NSCLC treated with stereotactic body radiotherapy (SBRT). Patients with early-stage NSCLC treated with SBRT between January 2003 and March 2014 at our institute were enrolled, and marital status at the time of SBRT was investigated. Propensity score matching (PSM) was applied to reduce potential selection bias between the married and unmarried groups. Two hundred and forty patients (median age 77 years; 152 married, 87 unmarried) were analyzed. The unmarried included higher proportions of the elderly, women, never smokers, and those with decreased pulmonary function compared to the married. PSM identified 53 matched pairs of married and unmarried patients, with no significant difference in patient background parameters. The 5-year overall survival (OS) was 52.8% and 46.9% in the married and unmarried groups, respectively (P = 0.26). There was no significant difference in NSCLC death or non-NSCLC death between the two groups (P = 0.88 and 0.30, respectively). There was no significant difference in OS between married and unmarried male patients (n = 85, 5-year OS, 52.6% vs. 46.0%; P = 0.42) and between married and unmarried female patients (n = 21, 54.5% vs. 50.0%; P = 0.44). In conclusion, marital status was not associated with OS in patients receiving SBRT for early-stage NSCLC

Appropriate margin for planning target volume for breast radiotherapy during deep inspiration breath-hold by variance component analysis

BACKGROUND: This study aimed to quantify errors by using a cine electronic portal imaging device (cine EPID) during deep inspiration breath-hold (DIBH) for left-sided breast cancer and to estimate the planning target volume (PTV) by variance component analysis. METHODS: This study included 25 consecutive left-sided breast cancer patients treated with whole-breast irradiation (WBI) using DIBH. Breath-holding was performed while monitoring abdominal anterior-posterior (AP) motion using the Real-time Position Management (RPM) system. Cine EPID was used to evaluate the chest wall displacements in patients. Cine EPID images of the patients (309, 609 frames) were analyzed to detect the edges of the chest wall using a Canny filter. The errors that occurred during DIBH included differences between the chest wall position detected by digitally reconstructed radiographs and that of all cine EPID images. The inter-patient, inter-fraction, and intra-fractional standard deviations (SDs) in the DIBH were calculated, and the PTV margin was estimated by variance component analysis. RESULTS: The median patient age was 55 (35-79) years, and the mean irradiation time was 20.4 ± 1.7 s. The abdominal AP motion was 1.36 ± 0.94 (0.14-5.28) mm. The overall mean of the errors was 0.30 mm (95% confidence interval: - 0.05-0.65). The inter-patient, inter-fraction, and intra-fractional SDs in the DIBH were 0.82 mm, 1.19 mm, and 1.63 mm, respectively, and the PTV margin was calculated as 3.59 mm. CONCLUSIONS: Errors during DIBH for breast radiotherapy were monitored using EPID images and appropriate PTV margins were estimated by variance component analysis

Development and validation of a prognostic model for non-lung cancer death in elderly patients treated with stereotactic body radiotherapy for non-small cell lung cancer

This study sought to develop and validate a prognostic model for non-lung cancer death (NLCD) in elderly patients with non-small cell lung cancer (NSCLC) treated with stereotactic body radiotherapy (SBRT). Patients aged ≥65 diagnosed with NSCLC (Tis-4N0M0), tumor diameter ≤5 cm and SBRT between 1998 and 2015 were retrospectively registered from two independent institutions. One institution was used for model development (arm D, 353 patients) and the other for validation (arm V, 401 patients). To identify risk factors for NLCD, multiple regression analysis on age, sex, performance status (PS), body mass index (BMI), Charlson comorbidity index (CCI), tumor diameter, histology and T-stage was performed on arm D. A score calculated using the regression coefficient was assigned to each factor and three risk groups were defined based on total score. Scores of 1.0 (BMI ≤18.4), 1.5 (age ≥ 5), 1.5 (PS ≥2), 2.5 (CCI 1 or 2) and 3 (CCI ≥3) were assigned, and risk groups were designated as low (total ≤ 3), intermediate (3.5 or 4) and high (≥4.5). The cumulative incidences of NLCD at 5 years in the low, intermediate and high-risk groups were 6.8, 23 and 40% in arm D, and 23, 19 and 44% in arm V, respectively. The AUC index at 5 years was 0.705 (arm D) and 0.632 (arm V). The proposed scoring system showed usefulness in predicting a high risk of NLCD in elderly patients treated with SBRT for NSCLC

Multi-institutional phase II study on the safety and efficacy of dynamic tumor tracking-stereotactic body radiotherapy for lung tumors

Background and purpose: This study aimed to evaluate the safety and efficacy of dynamic tumor tracking-stereotactic body radiotherapy (DTT-SBRT) for lung tumors. Materials and methods: Patients with cStage I primary lung cancer or metastatic lung cancer with an expected range of respiratory motion of ≥10 mm were eligible for the study. The prescribed dose was 50 Gy in four fractions. A gimbal-mounted linac was used for DTT-SBRT delivery. The primary endpoint was local control at 2 years. Results: Forty-eight patients from four institutions were enrolled in this study. Forty-two patients had primary non-small-cell lung cancer, and six had metastatic lung tumors. DTT-SBRT was delivered for 47 lesions in 47 patients with a median treatment time of 28 min per fraction. The median respiratory motion during the treatment was 13.7 mm (range: 4.5–28.1 mm). The motion-encompassing method was applied for the one remaining patient due to the poor correlation between the abdominal wall and tumor movement. The median follow-up period was 32.3 months, and the local control at 2 years was 95.2% (lower limit of the one-sided 85% confidence interval [CI]: 90.3%). The overall survival and progression-free survival at 2 years were 79.2% (95% CI: 64.7%–88.2%) and 75.0% (95% CI: 60.2%–85.0%), respectively. Grade 3 toxicity was observed in one patient (2.1%) with radiation pneumonitis. Grade 4 or 5 toxicity was not observed. Conclusion: DTT-SBRT achieved excellent local control with low incidences of severe toxicities in lung tumors with respiratory motion

Development of AI-driven prediction models to realize real-time tumor tracking during radiotherapy

[Background] In infrared reflective (IR) marker-based hybrid real-time tumor tracking (RTTT), the internal target position is predicted with the positions of IR markers attached on the patient’s body surface using a prediction model. In this work, we developed two artificial intelligence (AI)-driven prediction models to improve RTTT radiotherapy, namely, a convolutional neural network (CNN) and an adaptive neuro-fuzzy inference system (ANFIS) model. The models aim to improve the accuracy in predicting three-dimensional tumor motion. [Methods] From patients whose respiration-induced motion of the tumor, indicated by the fiducial markers, exceeded 8 mm, 1079 logfiles of IR marker-based hybrid RTTT (IR Tracking) with the gimbal-head radiotherapy system were acquired and randomly divided into two datasets. All the included patients were breathing freely with more than four external IR markers. The historical dataset for the CNN model contained 1003 logfiles, while the remaining 76 logfiles complemented the evaluation dataset. The logfiles recorded the external IR marker positions at a frequency of 60 Hz and fiducial markers as surrogates for the detected target positions every 80-640 ms for 20-40 s. For each logfile in the evaluation dataset, the prediction models were trained based on the data in the first three quarters of the recording period. In the last quarter, the performance of the patient-specific prediction models was tested and evaluated. The overall performance of the AI-driven prediction models was ranked by the percentage of predicted target position within 2 mm of the detected target position. Moreover, the performance of the AI-driven models was compared to a regression prediction model currently implemented in gimbal-head radiotherapy systems. [Results] The percentage of the predicted target position within 2 mm of the detected target position was 95.1%, 92.6% and 85.6% for the CNN, ANFIS, and regression model, respectively. In the evaluation dataset, the CNN, ANFIS, and regression model performed best in 43, 28 and 5 logfiles, respectively. [Conclusions] The proposed AI-driven prediction models outperformed the regression prediction model, and the overall performance of the CNN model was slightly better than that of the ANFIS model on the evaluation dataset

Independent calculation-based verification of volumetric-modulated arc therapy–stereotactic body radiotherapy plans for lung cancer

This study aimed to investigate the feasibility of independent calculation‐based verification of volumetric‐modulated arc therapy (VMAT)–stereotactic body radiotherapy (SBRT) for patients with lung cancer using a secondary treatment planning system (sTPS). In all, 50 patients with lung cancer who underwent VMAT‐SBRT between April 2018 and May 2019 were included in this study. VMAT‐SBRT plans were devised using the Collapsed‐Cone Convolution in RayStation (primary TPS: pTPS). DICOM files were transferred to Eclipse software (sTPS), which utilized the Eclipse software, and the dose distribution was then recalculated using Acuros XB. For the verification of dose distribution in homogeneous phantoms, the differences among pTPS, sTPS, and measurements were evaluated using passing rates of a dose difference of 5% (DD5%) and gamma index of 3%/2 mm (γ3%/2 mm). The ArcCHECK cylindrical diode array was used for measurements. For independent verification of dose‐volume parameters per the patient’s geometry, dose‐volume indices for the planning target volume (PTV) including D95% and the isocenter dose were evaluated. The mean differences (± standard deviations) between the pTPS and sTPS were then calculated. The gamma passing rates of DD5% and γ3%/2 mm criteria were 99.2 ± 2.4% and 98.6 ± 3.2% for pTPS vs. sTPS, 92.9 ± 4.0% and 94.1 ± 3.3% for pTPS vs. measurement, and 93.0 ± 4.4% and 94.3 ± 4.1% for sTPS vs. measurement, respectively. The differences between pTPS and sTPS for the PTVs of D95% and the isocenter dose were −3.1 ± 2.0% and −2.3 ± 1.8%, respectively. Our investigation of VMAT‐SBRT plans for lung cancer revealed that independent calculation‐based verification is a time‐efficient method for patient‐specific quality assurance

Impact of pre-Treatment C-reactive protein level and skeletal muscle mass on outcomes after stereotactic body radiotherapy for T1N0M0 non-small cell lung cancer: A supplementary analysis of the Japan Clinical Oncology Group study JCOG0403

This study aimed to evaluate the impact of pretreatment C-reactive protein (CRP) and skeletal muscle mass (SMM) on outcomes after stereotactic body radiotherapy (SBRT) for T1N0M0 non-small cell lung cancer (NSCLC) as a supplementary analysis of JCOG0403. Patients were divided into high and low CRP groups with a threshold value of 0.3 mg/dL. The paraspinous musculature area at the level of the 12th thoracic vertebra was measured on simulation computed tomography (CT). When the area was lower than the sex-specific median, the patient was classified into the low SMM group. Toxicities, overall survival (OS) and cumulative incidence of cause-specific death were compared between the groups. Sixty operable and 92 inoperable patients were included. In the operable cohort, OS significantly differed between the CRP groups (log-rank test p = 0.009; 58.8% and 83.6% at three years for high and low CRP, respectively). This difference in OS was mainly attributed to the difference in lung cancer deaths (Gray’s test p = 0.070; 29.4% and 7.1% at three years, respectively). No impact of SMM on OS was observed. The incidence of Grade 3–4 toxicities tended to be higher in the low SMM group (16.7% vs 0%, Fisher’s exact test p = 0.052). In the inoperable cohort, no significant impact on OS was observed for either CRP or SMM. The toxicity incidence was also not different between the CRP and SMM groups. The present study suggests that pretreatment CRP level may provide prognostic information in operable patients receiving SBRT for early-stage NSCLC

Peritumoral radiomics features on preoperative thin-slice CT images can predict the spread through air spaces of lung adenocarcinoma

The spread through air spaces (STAS) is recognized as a negative prognostic factor in patients with early-stage lung adenocarcinoma. The present study aimed to develop a machine learning model for the prediction of STAS using peritumoral radiomics features extracted from preoperative CT imaging. A total of 339 patients who underwent lobectomy or limited resection for lung adenocarcinoma were included. The patients were randomly divided (3:2) into training and test cohorts. Two prediction models were created using the training cohort: a conventional model based on the tumor consolidation/tumor (C/T) ratio and a machine learning model based on peritumoral radiomics features. The areas under the curve for the two models in the testing cohort were 0.70 and 0.76, respectively ( = 0.045). The cumulative incidence of recurrence (CIR) was significantly higher in the STAS high-risk group when using the radiomics model than that in the low-risk group (44% vs. 4% at 5 years;  = 0.002) in patients who underwent limited resection in the testing cohort. In contrast, the 5-year CIR was not significantly different among patients who underwent lobectomy (17% vs. 11%;  = 0.469). In conclusion, the machine learning model for STAS prediction based on peritumoral radiomics features performed better than the C/T ratio model