Fang-Fang Yin

IMPORTANCE: Spine metastasis can be treated with high-dose radiation therapy with advanced delivery technology for long-term tumor and pain control. OBJECTIVE: To assess whether patient-reported pain relief was improved with stereotactic radiosurgery (SRS) as compared with conventional external beam radiotherapy (cEBRT) for patients with 1 to 3 sites of vertebral metastases. DESIGN, SETTING, AND PARTICIPANTS: In this randomized clinical trial, patients with 1 to 3 vertebral metastases were randomized 2:1 to the SRS or cEBRT groups. This NRG 0631 phase 3 study was performed as multi-institutional enrollment within NRG Oncology. Eligibility criteria included the following: (1) solitary vertebral metastasis, (2) 2 contiguous vertebral levels involved, or (3) maximum of 3 separate sites. Each site may involve up to 2 contiguous vertebral bodies. A total of 353 patients enrolled in the trial, and 339 patients were analyzed. This analysis includes data extracted on March 9, 2020. INTERVENTIONS: Patients randomized to the SRS group were treated with a single dose of 16 or 18 Gy (to convert to rad, multiply by 100) given to the involved vertebral level(s) only, not including any additional spine levels. Patients assigned to cEBRT were treated with 8 Gy given to the involved vertebra plus 1 additional vertebra above and below. MAIN OUTCOMES AND MEASURES: The primary end point was patient-reported pain response defined as at least a 3-point improvement on the Numerical Rating Pain Scale (NRPS) without worsening in pain at the secondary site(s) or the use of pain medication. Secondary end points included treatment-related toxic effects, quality of life, and long-term effects on vertebral bone and spinal cord. RESULTS: A total of 339 patients (mean [SD] age of SRS group vs cEBRT group, respectively, 61.9 [13.1] years vs 63.7 [11.9] years; 114 [54.5%] male in SRS group vs 70 [53.8%] male in cEBRT group) were analyzed. The baseline mean (SD) pain score at the index vertebra was 6.06 (2.61) in the SRS group and 5.88 (2.41) in the cEBRT group. The primary end point of pain response at 3 months favored cEBRT (41.3% for SRS vs 60.5% for cEBRT; difference, -19 percentage points; 95% CI, -32.9 to -5.5; 1-sided P = .99; 2-sided P = .01). Zubrod score (a measure of performance status ranging from 0 to 4, with 0 being fully functional and asymptomatic, and 4 being bedridden) was the significant factor influencing pain response. There were no differences in the proportion of acute or late adverse effects. Vertebral compression fracture at 24 months was 19.5% with SRS and 21.6% with cEBRT (P = .59). There were no spinal cord complications reported at 24 months. CONCLUSIONS AND RELEVANCE: In this randomized clinical trial, superiority of SRS for the primary end point of patient-reported pain response at 3 months was not found, and there were no spinal cord complications at 2 years after SRS. This finding may inform further investigation of using spine radiosurgery in the setting of oligometastases, where durability of cancer control is essential. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT00922974.

This article has been retracted: please see Elsevier Policy on Article Withdrawal (http://www.elsevier.com/locate/withdrawalpolicy). This abstract has been retracted at the request of the authors. After submitting this abstract the authors realized that the core concept, the encoder-decoder design, was also being used by other investigators at their institution for similar radiation therapy applications. As a result the abstract had not been discussed with, nor approved by, all the relevant investigators before submission. International Journal of Radiation Oncology, Biology, Physics, 108, (2020) S128-S128, https://doi.org/10.1016/j.ijrobp.2020.07.854.

Parsing diverse nerve cells into biological types is necessary for understanding neural circuit organization. Morphology is an intuitive criterion for neuronal classification and a proxy of connectivity, but morphological diversity and variability often preclude resolving the granularity of neuron types. Combining genetic labeling with high-resolution, large-volume light microscopy, we established a single neuron anatomy platform that resolves, registers, and quantifies complete neuron morphologies in the mouse brain. We discovered that cortical axo-axonic cells (AACs), a cardinal GABAergic interneuron type that controls pyramidal neuron (PyN) spiking at axon initial segments, consist of multiple subtypes distinguished by highly laminar-specific soma position and dendritic and axonal arborization patterns. Whereas the laminar arrangements of AAC dendrites reflect differential recruitment by input streams, the laminar distribution and local geometry of AAC axons enable differential innervation of PyN ensembles. This platform will facilitate genetically targeted, high-resolution, and scalable single neuron anatomy in the mouse brain.

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/β= 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.

PURPOSE: To develop a neural ordinary differential equation (ODE) model for visualizing deep neural network behavior during multi-parametric MRI-based glioma segmentation as a method to enhance deep learning explainability. METHODS: By hypothesizing that deep feature extraction can be modeled as a spatiotemporally continuous process, we implemented a novel deep learning model, Neural ODE, in which deep feature extraction was governed by an ODE parameterized by a neural network. The dynamics of (1) MR images after interactions with the deep neural network and (2) segmentation formation can thus be visualized after solving the ODE. An accumulative contribution curve (ACC) was designed to quantitatively evaluate each MR image's utilization by the deep neural network toward the final segmentation results. The proposed Neural ODE model was demonstrated using 369 glioma patients with a 4-modality multi-parametric MRI protocol: T1, contrast-enhanced T1 (T1-Ce), T2, and FLAIR. Three Neural ODE models were trained to segment enhancing tumor (ET), tumor core (TC), and whole tumor (WT), respectively. The key MRI modalities with significant utilization by deep neural networks were identified based on ACC analysis. Segmentation results by deep neural networks using only the key MRI modalities were compared to those using all four MRI modalities in terms of Dice coefficient, accuracy, sensitivity, and specificity. RESULTS: All Neural ODE models successfully illustrated image dynamics as expected. ACC analysis identified T1-Ce as the only key modality in ET and TC segmentations, while both FLAIR and T2 were key modalities in WT segmentation. Compared to the U-Net results using all four MRI modalities, the Dice coefficient of ET (0.784→0.775), TC (0.760→0.758), and WT (0.841→0.837) using the key modalities only had minimal differences without significance. Accuracy, sensitivity, and specificity results demonstrated the same patterns. CONCLUSION: The Neural ODE model offers a new tool for optimizing the deep learning model inputs with enhanced explainability. The presented methodology can be generalized to other medical image-related deep-learning applications.