Incidence and imaging characteristics of difficult to detect retrospectively identified brain metastases in patients receiving repeat courses of stereotactic radiosurgery.

PURPOSE: During stereotactic radiosurgery (SRS) planning for brain metastases (BM), brain MRIs are reviewed to select appropriate targets based on radiographic characteristics. Some BM are difficult to detect and/or definitively identify and may go untreated initially, only to become apparent on future imaging. We hypothesized that in patients receiving multiple courses of SRS, reviewing the initial planning MRI would reveal early evidence of lesions that developed into metastases requiring SRS. METHODS: Patients undergoing two or more courses of SRS to BM within 6 months between 2016 and 2018 were included in this single-institution, retrospective study. Brain MRIs from the initial course were reviewed for lesions at the same location as subsequently treated metastases; if present, this lesion was classified as a "retrospectively identified metastasis" or RIM. RIMs were subcategorized as meeting or not meeting diagnostic imaging criteria for BM (+ DC or -DC, respectively). RESULTS: Among 683 patients undergoing 923 SRS courses, 98 patients met inclusion criteria. There were 115 repeat courses of SRS, with 345 treated metastases in the subsequent course, 128 of which were associated with RIMs found in a prior MRI. 58% of RIMs were + DC. 17 (15%) of subsequent courses consisted solely of metastases associated with + DC RIMs. CONCLUSION: Radiographic evidence of brain metastases requiring future treatment was occasionally present on brain MRIs from prior SRS treatments. Most RIMs were + DC, and some subsequent SRS courses treated only + DC RIMs. These findings suggest enhanced BM detection might enable earlier treatment and reduce the need for additional SRS.

Surgery versus stereotactic body radiation therapy for stage I non-small cell lung cancer: A comprehensive review.

Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related death in the United States. With the implementation of lung cancer screening, the number and proportion of patients diagnosed with early-stage disease are anticipated to increase. Surgery is currently the standard of care for patients with operable stage I NSCLC. However, promising outcomes with stereotactic body radiation therapy (SBRT) in patients with inoperable disease has led to interest in directly comparing SBRT and surgery in operable patients. Unfortunately, early randomized trials comparing surgery and SBRT closed early because of poor accrual. In this article, the nuances of surgery and SBRT for early-stage NSCLC are reviewed. Furthermore, retrospective and prospective analyses of SBRT in early-stage NSCLC are discussed, and active randomized trials comparing these 2 approaches are described. Cancer 2018;124:667-78. © 2017 American Cancer Society.

Uncertainties in the dosimetric heterogeneity correction and its potential effect on local control in lung SBRT.

Objective. Dose calculation in lung stereotactic body radiation therapy (SBRT) is challenging due to the low density of the lungs and small volumes. Here we assess uncertainties associated with tissue heterogeneities using different dose calculation algorithms and quantify potential associations with local failure for lung SBRT.Approach. 164 lung SBRT plans were used. The original plans were prepared using Pencil Beam Convolution (PBC, n = 8) or Anisotropic Analytical Algorithm (AAA, n = 156). Each plan was recalculated with AcurosXB (AXB) leaving all plan parameters unchanged. A subset (n = 89) was calculated with Monte Carlo to verify accuracy. Differences were calculated for the planning target volume (PTV) and internal target volume (ITV) Dmean[Gy], D99%[Gy], D95%[Gy], D1%[Gy], and V100%[%]. Dose metrics were converted to biologically effective doses (BED) usingα/ß= 10Gy. Regression analysis was performed for AAA plans investigating the effects of various parameters on the extent of the dosimetric differences. Associations between the magnitude of the differences for all plans and outcome were investigated using sub-distribution hazards analysis.Main results. For AAA cases, higher energies increased the magnitude of the difference (ΔDmean of -3.6%, -5.9%, and -9.1% for 6X, 10X, and 15X, respectively), as did lung volume (ΔD99% of -1.6% per 500cc). Regarding outcome, significant hazard ratios (HR) were observed for the change in the PTV and ITV D1% BEDs upon univariate analysis (p = 0.042, 0.023, respectively). When adjusting for PTV volume and prescription, the HRs for the change in the ITV D1% BED remained significant (p = 0.039, 0.037, respectively).Significance. Large differences in dosimetric indices for lung SBRT can occur when transitioning to advanced algorithms. The majority of the differences were not associated with local failure, although differences in PTV and ITV D1% BEDs were associated upon univariate analysis. This shows uncertainty in near maximal tumor dose to potentially be predictive of treatment outcome.

Divergent Interpretations of Imaging After Stereotactic Body Radiation Therapy for Lung Cancer.

PURPOSE: Conflicting information from health care providers contributes to anxiety among cancer patients. The purpose of this study was to investigate discordant interpretations of follow-up imaging studies after lung stereotactic body radiation therapy (SBRT) between radiologists and radiation oncologists. METHODS AND MATERIALS: Patients treated with SBRT for stage I non-small cell lung cancer from 2007 to 2018 at Duke University Medical Center were included. Radiology interpretations of follow-up computed tomography (CT) chest or positron emission tomography (PET)/CT scans and the corresponding radiation oncology interpretations in follow-up notes from the medical record were assessed. Based on language used, interpretations were scored as concerning for progression (Progression), neutral differential listed (Neutral Differential), or favor stability/postradiation changes (Stable). Neutral Differential required that malignancy was specifically listed as a possibility in the differential. Encounters were categorized as discordant when either radiology or radiation oncology interpreted the surveillance imaging as Progression when the other interpreted the imaging study as Stable or Neutral Differential. The incidence of discordant interpretations was the primary endpoint of the study. RESULTS: From 2007 to 2018, 139 patients were treated with SBRT and had available follow-up CT or PET-CT imaging for the analysis. Median follow-up was 61 months and the median number of follow-up encounters per patient was 3. Of 534 encounters evaluated, 25 (4.7%) had overtly discordant interpretations of imaging studies. This most commonly arose when radiology felt the imaging study showed Progression but radiation oncology favored Stable or Neutral Differential (24/25, 96%). No patient or treatment variables were found to be significantly associated with discordant interpretations on univariate analysis including type of scan (CT 22/489, 4.5%; PET-CT 3/45, 7%; P = .46). CONCLUSIONS: Surveillance imaging after lung SBRT is often interpreted differently by radiologists and radiation oncologists, but overt discordance was relatively low at our institution. Providers should be aware of differences in interpretation patterns that may contribute to increased patient distress.

A Deep Learning-Based Computer Aided Detection (CAD) System for Difficult-to-Detect Brain Metastases.

PURPOSE: We sought to develop a computer-aided detection (CAD) system that optimally augments human performance, excelling especially at identifying small inconspicuous brain metastases (BMs), by training a convolutional neural network on a unique magnetic resonance imaging (MRI) data set containing subtle BMs that were not detected prospectively during routine clinical care. METHODS AND MATERIALS: Patients receiving stereotactic radiosurgery (SRS) for BMs at our institution from 2016 to 2018 without prior brain-directed therapy or small cell histology were eligible. For patients who underwent 2 consecutive courses of SRS, treatment planning MRIs from their initial course were reviewed for radiographic evidence of an emerging metastasis at the same location as metastases treated in their second SRS course. If present, these previously unidentified lesions were contoured and categorized as retrospectively identified metastases (RIMs). RIMs were further subcategorized according to whether they did (+DC) or did not (-DC) meet diagnostic imaging-based criteria to definitively classify them as metastases based upon their appearance in the initial MRI alone. Prospectively identified metastases (PIMs) from these patients, and from patients who only underwent a single course of SRS, were also included. An open-source convolutional neural network architecture was adapted and trained to detect both RIMs and PIMs on thin-slice, contrast-enhanced, spoiled gradient echo MRIs. Patients were randomized into 5 groups: 4 for training/cross-validation and 1 for testing. RESULTS: One hundred thirty-five patients with 563 metastases, including 72 RIMS, met criteria. For the test group, CAD sensitivity was 94% for PIMs, 80% for +DC RIMs, and 79% for PIMs and +DC RIMs with diameter <3 mm, with a median of 2 false positives per patient and a Dice coefficient of 0.79. CONCLUSIONS: Our CAD model, trained on a novel data set and using a single common MR sequence, demonstrated high sensitivity and specificity overall, outperforming published CAD results for small metastases and RIMs - the lesion types most in need of human performance augmentation.

Resistance Training for Patients with Cancer: A Conceptual Framework for Maximizing Strength, Power, Functional Mobility, and Body Composition to Optimize Health and Outcomes.

There are many benefits to the addition of exercise to cancer treatment and survivorship, particularly with resistance training regimens that target hypertrophy, bone mineral density, strength, functional mobility, and body composition. These goals are best achieved through a series of individualized high-intensity compound movements that mirror functional mobility patterns and sufficiently stress the musculoskeletal system. As a result of adequate stress, the body will engage compensatory cellular mechanisms that improve the structural integrity of bones and muscles, stimulate metabolism and the immune system, optimize functional performance, and minimize mechanical injury risk. The current evidence suggests that application of the above exercise principles, practiced in a safe environment under expert observation, may offer patients with cancer an effective means of improving overall health and cancer-specific outcomes. The following article poses several important questions certified exercise specialists and physicians should consider when prescribing resistance exercise for patients with cancer.

Development of a multi-feature-combined model: proof-of-concept with application to local failure prediction of post-SBRT or surgery early-stage NSCLC patients.

Objective: To develop a Multi-Feature-Combined (MFC) model for proof-of-concept in predicting local failure (LR) in NSCLC patients after surgery or SBRT using pre-treatment CT images. This MFC model combines handcrafted radiomic features, deep radiomic features, and patient demographic information in an integrated machine learning workflow. Methods: The MFC model comprised three key steps. (1) Extraction of 92 handcrafted radiomic features from the GTV segmented on pre-treatment CT images. (2) Extraction of 512 deep radiomic features from pre-trained U-Net encoder. (3) The extracted handcrafted radiomic features, deep radiomic features, along with 4 patient demographic information (i.e., gender, age, tumor volume, and Charlson comorbidity index), were concatenated as a multi-dimensional input to the classifiers for LR prediction. Two NSCLC patient cohorts from our institution were investigated: (1) the surgery cohort includes 83 patients with segmentectomy or wedge resection (7 LR), and (2) the SBRT cohort includes 84 patients with lung SBRT (9 LR). The MFC model was developed and evaluated independently for both cohorts, and was subsequently compared against the prediction models based on only handcrafted radiomic features (R models), patient demographic information (PI models), and deep learning modeling (DL models). ROC with AUC was adopted to evaluate model performance with leave-one-out cross-validation (LOOCV) and 100-fold Monte Carlo random validation (MCRV). The t-test was performed to identify the statistically significant differences. Results: In LOOCV, the AUC range (surgery/SBRT) of the MFC model was 0.858-0.895/0.868-0.913, which was higher than the three other models: 0.356-0.480/0.322-0.650 for PI models, 0.559-0.618/0.639-0.682 for R models, and 0.809/0.843 for DL models. In 100-fold MCRV, the MFC model again showed the highest AUC results (surgery/SBRT): 0.742-0.825/0.888-0.920, which were significantly higher than PI models: 0.464-0.564/0.538-0.628, R models: 0.557-0.652/0.551-0.732, and DL models: 0.702/0.791. Conclusion: We successfully developed an MFC model that combines feature information from multiple sources for proof-of-concept prediction of LR in patients with surgical and SBRT early-stage NSCLC. Initial results suggested that incorporating pre-treatment patient information from multiple sources improves the ability to predict the risk of local failure.

The Effect of Various Dose Normalization Strategies When Implementing Linear Boltzmann Transport Equation Dose Calculation for Lung Stereotactic Body Radiation Therapy Planning.

PURPOSE: To explore implications of various plan normalizations when implementing a linear Boltzmann transport equation solver dose calculation algorithm (LBTE) for lung stereotactic body radiation therapy (SBRT). METHODS AND MATERIALS: Eighty-seven plans originally calculated with a convolution-superposition algorithm (CS) were recalculated with LBTE and normalized in 3 ways: prescription covering 95% of planning target volume (PTV), 99% of internal target volume (ITV), and keeping the original planned PTV coverage. Effect on delivered dose after implementing the new algorithm was quantified using change in total monitor units for each renormalization strategy. Treatment planning system-reported changes in PTV, ITV, and organ at risk (OAR) doses were also quantified, along with the feasibility of LBTE plans to meet institutional OAR planning objectives. RESULTS: LBTE renormalization resulted in monitor unit increases of 7.0 ± 8.8%, 0.31 ± 5.8%, and 7.9 ± 8.6% when normalizing to the PTV D95%, ITV D99%, and planned coverage, respectively. When normalizing to PTV D95%, the LBTE reported increased PTV and ITV D1% (Gy) relative to CS (median, 3.4% and 3.2%, respectively), and normalizing to ITV D99% showed a median 1.9% decrease. For LBTE plans, reported OAR doses were increased when normalizing to PTV D95% or planned coverage (median chest wall V30 Gy [cc] increase of 0.85 and 1.7 cc, respectively) and normalizing to ITV D99% resulted in decreased dose (median chest wall V30 Gy [cc] decrease of 1.8 cc). LBTE plans normalized to PTV D95% showed inferior ability to meet the OAR objectives, but reoptimizing kept the objectives manageable while maintaining PTV coverage. CONCLUSIONS: When transitioning from CS to LBTE dose calculation for lung SBRT, maintaining a PTV coverage-based normalization generally results in increased dose delivered relative to CS and increased reported target and OAR dose. In cases where PTV normalization results in unacceptably high doses to targets or OARs, normalizing based on ITV coverage can be considered to maintain similar target dose as CS.

Recumbent Total Skin Electron Beam Therapy.

PURPOSE: Our purpose was to describe preliminary dosimetric and clinical results of a recumbent total skin electron beam therapy (TSEBT) technique and compare this to a conventional standing TSEBT technique. METHODS AND MATERIALS: A customized treatment platform with recessed side wheels was constructed and commissioned for patients to be treated in a recumbent position. Dosimetric and clinical information was collected for patients treated with this new recumbent technique in addition to that of a cohort of patients treated contemporaneously using the conventional standing method. Dose delivery and clinical outcomes were compared for patients treated with the recumbent and standing techniques. RESULTS: Between 2017 and 2019, 27 patients were treated with TSEBT with the recumbent (n = 13) or conventional standing technique (n = 14) at our institution. Measured dose at 15 body sites could be directly compared. Of these, 10 showed no significant difference between the two techniques while five sites showed significant differences in median measured dose, including the top of left shoulder, right biceps, bend of left elbow, upper back, and medial right thigh (P < .003). Measured dose was significantly higher with the standing technique at these sites with the exception of the upper back. Rates of complete response (25% vs 23%), partial response (50% vs 69%), and stable disease (17% vs 8%) were similar between the standing and recumbent cohorts, respectively (P = .78). CONCLUSIONS: We have developed, commissioned, and implemented a floor-based, recumbent technique that allows for treatment of patients who would otherwise not be eligible for TSEBT. Dosimetric and clinical measurements suggest that this technique is a viable alternative to the standing method.

Association of pre-treatment radiomic features with lung cancer recurrence following stereotactic body radiation therapy.

The purpose of this work was to investigate the potential relationship between radiomic features extracted from pre-treatment x-ray CT images and clinical outcomes following stereotactic body radiation therapy (SBRT) for non-small-cell lung cancer (NSCLC). Seventy patients who received SBRT for stage-1 NSCLC were retrospectively identified. The tumor was contoured on pre-treatment free-breathing CT images, from which 43 quantitative radiomic features were extracted to collectively capture tumor morphology, intensity, fine-texture, and coarse-texture. Treatment failure was defined based on cancer recurrence, local cancer recurrence, and non-local cancer recurrence following SBRT. The univariate association between each radiomic feature and each clinical endpoint was analyzed using Welch's t-test, and p-values were corrected for multiple hypothesis testing. Multivariate associations were based on regularized logistic regression with a singular value decomposition to reduce the dimensionality of the radiomics data. Two features demonstrated a statistically significant association with local failure: Homogeneity2 (p = 0.022) and Long-Run-High-Gray-Level-Emphasis (p = 0.048). These results indicate that relatively dense tumors with a homogenous coarse texture might be linked to higher rates of local recurrence. Multivariable logistic regression models produced maximum [Formula: see text] values of [Formula: see text], and [Formula: see text], for the recurrence, local recurrence, and non-local recurrence endpoints, respectively. The CT-based radiomic features used in this study may be more associated with local failure than non-local failure following SBRT for stage I NSCLC. This finding is supported by both univariate and multivariate analyses.

Stereotactic body radiation therapy versus sublobar resection for stage I NSCLC.

PURPOSE: To compare sublobar resection and stereotactic body radiation therapy (SBRT) in patients with stage I non-small cell lung cancer (NSCLC). METHODS: Patients undergoing sublobar resection or SBRT for stage I NSCLC from 2007 to 2014 at Duke University Medical Center were evaluated. The primary endpoint of interest was freedom from local recurrence. Kaplan-Meier survival estimates and Cox proportional hazards multivariate analyses were performed. RESULTS: 221 patients with stage I NSCLC undergoing sublobar resection (n = 151; 105 wedge and 46 segmentectomy) or SBRT (n = 70) were evaluated. The majority (89%) of patients receiving SBRT were medically inoperable, and compared with surgical patients, were significantly older (74 vs 70 years, p = 0.019), had higher Charlson Comorbidity Indices (3.7 vs 2.7, p < 0.001), larger tumors (2.4 cm vs 1.7 cm, p < 0.001), and worse baseline pulmonary function. At 3 years, freedom from local recurrence was 90% (95% CI 82-94%) for surgery and 85% (95% CI 65-94%) for SBRT (p = 0.71). While overall survival and disease-free survival were higher in the surgical cohort, no differences were noted in cancer-specific disease-free survival (60% vs. 65%, p = 0.84). On multivariate analysis, higher Charlson Comorbidity Index (HR 1.38, 95% CI 1.19-1.61, p < 0.001) and lower diffusion capacity (HR 0.97, 95% CI 0.96-0.98, p < 0.001) were independently associated with inferior overall survival. No differences in overall survival between surgery and SBRT (HR 1.20, 95% CI 0.74-1.95, p = 0.46) were observed after correcting for baseline imbalances in prognostic factors. CONCLUSIONS: SBRT and sublobar resection provided similar rates of local tumor control and overall clinical outcomes in stage I NSCLC.

Intratreatment Response Assessment With 18F-FDG PET: Correlation of Semiquantitative PET Features With Pathologic Response of Esophageal Cancer to Neoadjuvant Chemoradiotherapy.

PURPOSE: This prospective study seeks to extract semiquantitative positron emission tomography (PET) features from 18F-fluorodeoxyglucose PET scans performed before and during neoadjuvant chemoradiotherapy for esophageal cancer and to compare their accuracy in predicting histopathologic response. METHODS AND MATERIALS: From 2012 to 2016, 26 patients with esophageal cancer underwent pretreatment and intratreatment PET scans during chemoradiotherapy followed by surgery. Median patient age was 63 years (interquartile range, 58-68 years); 26 patients had esophageal adenocarcinoma, and 3 had esophageal squamous cell carcinoma. The intratreatment PET scan was performed at a median of 32.4 Gy (interquartile range, 30.6-32.4 Gy). PET features of the primary site including maximum standardized uptake value (SUV), SUV mean, metabolic tumor volume, and total lesion glycolysis were extracted from the pretreatment and intratreatment PET scans. Patients were histopathologic responders if there was complete or near-complete tumor response by modified Ryan scheme. Mean values of PET features were compared between histopathologic responders and nonresponders. The area under the receiver operating characteristic curve (AUC) was used to compare the accuracy of PET features in predicting histopathologic response. RESULTS: Eleven patients (42%) were histopathologic responders. PET features most discriminatory of histopathologic response on AUC analysis were volumetric PET features from the intratreatment PET including metabolic tumor volume based on manual contour (AUC, 0.73; 95% confidence interval, 0.52-0.93) and total lesion glycolysis based on semiautomatic 40% SUV threshold (AUC, 0.73; 95% confidence interval, 0.53-0.94). CONCLUSIONS: Volumetric PET features from the intratreatment PET were the most accurate predictors of histopathologic response.