Background Stereotactic radiosurgery (SRS), a validated treatment for brain tumors, requires accurate tumor contouring. This manual segmentation process is time-consuming and prone to substantial inter-practitioner variability. Artificial intelligence (AI) with deep neural networks have increasingly been proposed for use in lesion detection and segmentation but have seldom been validated in a clinical setting. Methods We conducted a randomized, cross-modal, multi-reader, multi-specialty, multi-case study to evaluate the impact of AI assistance on brain tumor SRS. A state-of-the-art auto-contouring algorithm built on multi-modality imaging and ensemble neural networks was integrated into the clinical workflow. Nine medical professionals contoured the same case series in two reader modes (assisted or un-assisted) with a memory washout period of 6 weeks between each section. The case series consisted of ten algorithm-unseen cases, including five cases of brain metastases, three of meningiomas and two of acoustic neuromas. Among the nine readers, three experienced experts determined the ground truths of tumor contours. Results With the AI assistance, the inter-reader agreement significantly increased (Dice similarity coefficient [DSC] from 0.86 to 0.90, P<0.001). Algorithm-assisted physicians demonstrated a higher sensitivity for lesion detection than un-assisted physicians (91.3% versus 82.6%, P=0.030). AI assistance improved contouring accuracy, with an average increase in DSC of 0.028, especially for physicians with less SRS experience (average DSC from 0.847 to 0.865, P=0.002). In addition, AI assistance improved efficiency with a median of 30.8%-time savings. Less-experienced clinicians gained prominent improvement on contouring accuracy but less benefit in reduction of working hours. By contrast, SRS specialists had a relatively minor advantage in DSC, but greater timesaving with the aid of AI. Conclusions Deep learning neural networks can be optimally utilized to improve accuracy and efficiency for the clinical workflow in brain tumor SRS.

Deep learning-based blind image deblurring plays an essential role in solving image blur since all existing kernels are limited in modeling the real world blur. Thus far, researchers focus on powerful models to handle the deblurring problem and achieve decent results. For this work, in a new aspect, we discover the great opportunity for image enhancement (e.g., deblurring) directly from RAW images and investigate novel neural network structures benefiting RAW-based learning. However, to the best of our knowledge, there is no available RAW image deblurring dataset. Therefore, we built a new dataset containing both RAW images and processed sRGB images and design a new model to utilize the unique characteristics of RAW images. The proposed deblurring model, trained solely from RAW images, achieves the state-of-art performance and outweighs those trained on processed sRGB images. Furthermore, with fine-tuning, the proposed model, trained on our new dataset, can generalize to other sensors. Additionally, by a series of experiments, we demonstrate that existing deblurring models can also be improved by training on the RAW images in our new dataset. Ultimately, we show a new venue for further opportunities based on the devised novel raw-based deblurring method and the brand-new Deblur-RAW dataset.

Purpose: Although osimertinib has excellent intracranial activity in metastatic non-small cell lung cancer (NSCLC) with exon 19 deletion or L858R EGFR alterations, measures of local control of brain metastases are less well-reported. We describe lesion-level outcomes of brain metastases treated with osimertinib alone.Methods: We retrospectively reviewed patients with EGFR-mutant NSCLC with untreated brain metastasis measuring ≥5 mm at the time of initiating osimertinib. Cumulative incidence of local recurrence in brain (LRiB) was calculated with death as a competing risk, and univariable and multivariable analyses were conducted to identify factors associated with LRiB.Results: We included 284 brain metastases from 37 patients. Median follow-up was 20.1 months. On initial MRI after starting osimertinib, patient-level response was complete response (CR) in 11 (15%), partial response (PR) in 33 (45%), stable disease (SD) in 18 (25%) and progressive disease (PD) in 11 (15%). The 1-year cumulative incidence of LRiB was 14% (95% CI 9.9-17.9) and was signi cantly different in patients with a CR (0%), PR (4%), and SD (11%; p=0.02). Uncontrolled primary tumor (adjusted hazard ratio [aHR] 3.78, 95% CI 1.87-7.66; p<0.001), increasing number of prior systemic therapies (aHR 2.12, 95% CI 1.49-3.04; p<0.001), and higher ECOG score (aHR 7.8, 95% CI 1.99-31.81; p=0.003) were associated with LRiB.Conclusions: Although 1-year risk of LRiB is <4% with a CR or PR, 1-year risk of LRiB is over 10% for patients with less than a PR to osimertinib. These patients should be followed closely for need for additional treatment such as stereotactic radiosurgery.

Introduction Artificial intelligence-based tools can significantly impact detection and segmentation of brain metastases for stereotactic radiosurgery (SRS). VBrain is a deep learning algorithm, recently FDA-cleared, to assist in brain tumor contouring. In this study, we aimed to further validate this tool in patients treated with SRS for brain metastases at Stanford Cancer Center. Methods We included randomly selected patients with brain metastases treated with SRS from 2008 to 2020. Computed tomography (CT) and axial T1-weighted post-contrast magnetic resonance (MR) image data were extracted for each patient and uploaded to VBrain. Subsequent analyses compared the output contours from VBrain with the physician-defined contours used for SRS. A brain metastasis was considered “detected” when the VBrain “predicted” contours overlapped with the corresponding physician contours (“ground-truth” contours). We evaluated performance against ground-truth contours using the following metrics: lesion-wise Dice similarity coefficient (DSC), lesion-wise average Hausdorff distance (AVD), false positive count (FP), and lesion-wise sensitivity (%). Results We analyzed 60 patients with 321 intact brain metastases treated over 70 SRS courses. Resection cavities were excluded from the analysis. The median (range) tumor size was 132 mm3 (7 to 24,765). Input CT scan slice thickness was 1.250 mm, and median (range) pixel resolution was 0.547 mm (0.457 to 0.977). Input MR scan median (range) slice thickness was 1.000 mm (0.940 to 2.000), and median (range) pixel resolution was 0.469 mm (0.469 to 1.094). In assessing VBrain performance, we found mean lesion-wise DSC to be 0.70, mean lesion-wise AVD to be 9.40% of lesion size (0.805 mm), mean FP to be 0.657 tumors per case, and lesion-wise sensitivity to be 84.5%. Conclusion Retrospective analysis of our brain metastases cohort using a deep learning algorithm yielded promising results. Integration of VBrain into the clinical workflow can provide further clinical and research insights.

Purpose: Although osimertinib has excellent intracranial activity in metastatic non-small cell lung cancer (NSCLC) with exon 19 deletion or L858R EGFR alterations, measures of local control of brain metastases are less well-reported. We describe lesion-level outcomes of brain metastases treated with osimertinib alone.Methods: We retrospectively reviewed patients with EGFR-mutant NSCLC with untreated brain metastasis measuring ≥5 mm at the time of initiating osimertinib. Cumulative incidence of local recurrence in brain (LRiB) was calculated with death as a competing risk, and univariable and multivariable analyses were conducted to identify factors associated with LRiB. Results: We included 284 brain metastases from 37 patients. Median follow-up was 20.1 months. On initial MRI after starting osimertinib, patient-level response was complete response (CR) in 11 (15%), partial response (PR) in 33 (45%), stable disease (SD) in 18 (25%) and progressive disease (PD) in 11 (15%). The 1-year cumulative incidence of LRiB was 14% (95% CI 9.9-17.9) and was significantly different in patients with a CR (0%), PR (4%), and SD (11%; p=0.02). Uncontrolled primary tumor (adjusted hazard ratio [aHR] 3.78, 95% CI 1.87-7.66; p<0.001), increasing number of prior systemic therapies (aHR 2.12, 95% CI 1.49-3.04; p<0.001), and higher ECOG score (aHR 7.8, 95% CI 1.99-31.81; p=0.003) were associated with LRiB. Conclusions: Although 1-year risk of LRiB is <4% with a CR or PR, 1-year risk of LRiB is over 10% for patients with less than a PR to osimertinib. These patients should be followed closely for need for additional treatment such as stereotactic radiosurgery.