Recent trends of characteristics and treatments in adults with newly diagnosed brain metastases.

OBJECTIVE: We aimed to evaluate recent trends in characteristics and treatments among patients with brain metastases in clinical practice. METHODS: All newly diagnosed patients with brain metastases during 2016-2021 at a single cancer center were enrolled. We collected the detailed features of each patient and estimated the number of candidates considered to meet the following criteria used in common clinical trials: Karnofsky performance status ≥ 70 and mutated non-small cell lung cancer, breast cancer or melanoma. The brain metastases treatments were classified as follows: (i) stereotactic radiosurgery, (ii) stereotactic radiosurgery and systemic therapy, (iii) whole-brain radiotherapy, (iv) whole-brain radiotherapy and systemic therapy, (v) surgery, (vi) immune checkpoint inhibitor or targeted therapy, (vii) cytotoxic agents and (ix) palliative care. Overall survival and intracranial progression-free survival were estimated from brain metastases diagnosis to death or intracranial progression. RESULTS: A total of 800 brain metastases patients were analyzed; 597 (74.6%) underwent radiotherapy, and 422 (52.7%) underwent systemic therapy. In addition, 250 (31.3%) patients were considered candidates for common clinical trials. Compared to 2016, the later years tended to shift from whole-brain radiotherapy to stereotactic radiosurgery (whole-brain radiotherapy: 35.7-29.1% and stereotactic radiosurgery: 33.4-42.8%) and from cytotoxic agents to immune checkpoint inhibitor/targeted therapy (cytotoxic agents: 10.1-5.0 and immune checkpoint inhibitor/targeted therapy: 7.8-10.9%). There was also an increase in the proportion of systemic therapy combined with radiation therapy (from 26.4 to 36.5%). The median overall survival and progression-free survival were 12.7 and 5.3 months, respectively. CONCLUSIONS: This study revealed the diversity of brain metastases patient characteristics, recent changes in treatment selection and the percentage of candidates in clinical trials.

Radiotherapy or systemic therapy versus combined therapy in patients with brain metastases: a propensity-score matched study.

AIM: This study aimed to evaluate the clinical benefits of systemic therapy (ST) combined with stereotactic radiosurgery (SRS) for brain metastases (BM). METHODS: The patient data were extracted from the institutional disease database from 2016 to 2021. Surgical and whole-brain radiotherapy cases and poor Karnofsky performance status (KPS < 70) were excluded. The eligible patients were divided into monotherapy (SRS alone or ST alone) and combined therapy (SRS and ST, combined within a month). Univariate and multivariate Cox proportional hazards analyses were used to examine factors associated with increased risk of death and intracranial progression. The propensity score for selecting treatment was calculated based on existing prognostic covariates. Two groups were matched 1:1 and compared for intracranial progression-free survival (PFS) and overall survival (OS). RESULTS: We identified 1605 patients and analyzed 928 (monotherapy: n = 494, combined therapy: n = 434). In a multivariable model, the combined therapy was independently associated with improved PFS and OS relative to the monotherapy. At the median follow-up of 383 days in the matched dataset, the combined therapy group showed significantly longer PFS (median, 7.4 vs. 5.0 months, P < 0.001) and OS (median, 23.1 vs. 17.2 months, P = 0.036) than the monotherapy group. The overall intracranial progression and mortality risk was reduced in the combined therapy group, with an estimated HR of 0.70 and 0.78. CONCLUSIONS: Combined therapy exhibited longer PFS and OS than monotherapy in BM patients. The results support the recent trend toward combining systemic and local therapies, encouraging future clinical trials.

Preoperative spirometry and BMI in deep inspiration breath-hold radiotherapy: the early detection of cardiac and lung dose predictors without radiation exposure.

BACKGROUND: This study aimed to investigate preoperative spirometry and BMI as early predictors of the mean heart and lung dose (MHD, MLD) in deep inspiration breath-hold (DIBH) radiotherapy. METHODS: Left-sided breast cancer patients underwent breast-conserving surgery followed by DIBH radiotherapy enrolled. Patients who were not available for preoperative spirometry were excluded. One hundred eligible patients were performed free-breathing (FB-) CT and DIBH-CT for plan comparison. We completed the correlative and multivariate analysis to develop the linear regression models for dose prediction. The residuals were calculated to explore the unpreferable subgroups and compare the prediction accuracy. RESULTS: Among the parameters, vital capacity (VC) and BMI showed the strongest negative correlation with MHD (r = - 0.33) and MLD (r = - 0.34), respectively. They were also significant in multivariate analysis (P < 0.001). Elderly and less VC were independent predictors of increasing absolute residuals (AR). The VC model showed no significant difference in AR compared to the model using the CT parameter of lung volume in FB (LV-FB): median AR of the LV-FB model vs. the VC model was 0.12 vs. 0.11 Gy (P = 0.79). On the other hand, the median AR of the MLD model was 0.38 Gy, showing no specific subgroups of larger AR. CONCLUSION: Preoperative spirometry and BMI are significant predictors of MHD and MLD, respectively. Although elderly and low-VC patients may have larger predictive variations, spirometry might be a substitute for LV-FB as a predictor of MHD.

Development of deep learning chest X-ray model for cardiac dose prediction in left-sided breast cancer radiotherapy.

Deep inspiration breath-hold (DIBH) is widely used to reduce the cardiac dose in left-sided breast cancer radiotherapy. This study aimed to develop a deep learning chest X-ray model for cardiac dose prediction to select patients with a potentially high risk of cardiac irradiation and need for DIBH radiotherapy. We used 103 pairs of anteroposterior and lateral chest X-ray data of left-sided breast cancer patients (training cohort: n = 59, validation cohort: n = 19, test cohort: n = 25). All patients underwent breast-conserving surgery followed by DIBH radiotherapy: the treatment plan consisted of three-dimensional, two opposing tangential radiation fields. The prescription dose of the planning target volume was 42.56 Gy in 16 fractions. A convolutional neural network-based regression model was developed to predict the mean heart dose (∆MHD) reduction between free-breathing (MHDFB) and DIBH. The model performance is evaluated as a binary classifier by setting the cutoff value of ∆MHD > 1 Gy. The patient characteristics were as follows: the median (IQR) age was 52 (47-61) years, MHDFB was 1.75 (1.14-2.47) Gy, and ∆MHD was 1.00 (0.52-1.64) Gy. The classification performance of the developed model showed a sensitivity of 85.7%, specificity of 90.9%, a positive predictive value of 92.3%, a negative predictive value of 83.3%, and a diagnostic accuracy of 88.0%. The AUC value of the ROC curve was 0.864. The proposed model could predict ∆MHD in breast radiotherapy, suggesting the potential of a classifier in which patients are more desirable for DIBH.

Fully automated rigid image registration versus human registration in postoperative spine stereotactic body radiation therapy: a multicenter non-inferiority study.

To confirm the fully automated rigid image registration (A-RIR) accuracy in postoperative spine stereotactic body radiation therapy (SBRT), we conducted a multicenter non-inferiority study compared to the human rigid image registration (H-RIR). Twenty-eight metastatic cancer patients who underwent postoperative spine SBRT are enrolled-image registration (IR) of planning computed tomography (CT) and CT-myelogram for delineating the spinal cord. The adopted A-RIR workflow is a contour-focused algorithm performing a rigid registration by maximizing normalized mutual information (NMI) restricted to the data contained within the automatically extracted contour. Three radiation oncologists (ROs) from multicenters were prompted to review two blinded registrations and choose one for clinical use. Indistinguishable cases were allowed to vote equivalent, counted A-RIR side. A-RIR is considered non-inferior to H-RIR if the lower limit of the 95% confidence interval (CI) of A-RIR preferable/equivalent is greater than 0.45. We also evaluated the NMI improvement from the baseline and the translational/rotational errors between A-RIR and H-RIR. The A-RIR preferable/equivalent was selected in 21 patients (0.75, 95% CI: 0.55-0.89), demonstrating non-inferiority to H-RIR. The A-RIR's NMI improvement was greater than H-RIR in 24 patients: the mean value ± SD was 0.225 ± 0.115 in A-RIR and 0.196 ± 0.114 in H-RIR (P < 0.001). The absolute translational error was 0.38 ± 0.31 mm. The rotational error was -0.03 ± 0.20, 0.05 ± 0.19, -0.04 ± 0.20 degrees in axial, coronal, and sagittal planes (range: -0.66-0.52). In conclusion, A-RIR shows non-inferior to H-RIR in CT and CT-myelogram registration for postoperative spine SBRT planning.

A predictive model for pain response following radiotherapy for treatment of spinal metastases.

To establish a predictive model for pain response following radiotherapy using a combination of radiomic and clinical features of spinal metastasis. This retrospective study enrolled patients with painful spine metastases who received palliative radiation therapy from 2018 to 2019. Pain response was defined using the International Consensus Criteria. The clinical and radiomic features were extracted from medical records and pre-treatment CT images. Feature selection was performed and a random forests ensemble learning method was used to build a predictive model. Area under the curve (AUC) was used as a predictive performance metric. 69 patients were enrolled with 48 patients showing a response. Random forest models built on the radiomic, clinical, and 'combined' features achieved an AUC of 0.824, 0.702, 0.848, respectively. The sensitivity and specificity of the combined features model were 85.4% and 76.2%, at the best diagnostic decision point. We built a pain response model in patients with spinal metastases using a combination of clinical and radiomic features. To the best of our knowledge, we are the first to examine pain response using pre-treatment CT radiomic features. Our model showed the potential to predict patients who respond to radiation therapy.

Synthetic breath-hold CT generation from free-breathing CT: a novel deep learning approach to predict cardiac dose reduction in deep-inspiration breath-hold radiotherapy.

Deep-inspiration breath-hold radiotherapy (DIBH-RT) to reduce the cardiac dose irradiation is widely used but some patients experience little or no reduction. We constructed and compared two prediction models to evaluate the usefulness of our new synthetic DIBH-CT (sCT) model. Ninety-four left-sided breast cancer patients (training cohort: n = 64, test cohort: n = 30) underwent both free-breathing and DIBH planning. The U-Net-based sCT generation model was developed to create the sCT treatment plan. A linear prediction model was constructed for comparison by selecting anatomical predictors of past literature. The primary prediction outcome is the mean heart dose (MHD) reduction, and the coefficient of determination (R2), root mean square error (RMSE) and mean absolute error (MAE) were calculated. Moreover, we evaluated the heart and lungs contours' similarity and Hounsfield unit (HU) difference between both images. The median MHD reduction was 1.14 Gy in DIBH plans and 1.09 Gy in sCT plans (P = 0.96). The sCT model achieved better performance than the linear model (R2: 0.972 vs 0.450, RMSE: 0.120 vs 0.551, MAE: 0.087 vs 0.412). The organ contours were similar between DIBH-CT and sCT: the median Dice (DSC) and Jaccard similarity coefficients (JSC) were 0.912 and 0.838 for the heart and 0.910 and 0.834 for the lungs. The HU difference in the soft-tissue region was smaller than in the air or bone. In conclusion, our new model can generate the affected CT by breath-holding, resulting in high performance and well-visualized prediction, which may have many potential uses in radiation oncology.