Pretreatment Spatially Aware Magnetic Resonance Imaging Radiomics Can Predict Distant Brain Metastases (DBMs) After Stereotactic Radiosurgery/Radiation Therapy (SRS/SRT).

Purpose: Stereotactic radiosurgery/radiation therapy (SRS/SRT) increasingly has been used to treat brain metastases. However, the development of distant brain metastases (DBMs) in the untreated brain remains a serious complication. We sought to develop a spatially aware radiomic signature to model the time-to-DBM development in a cohort of patients leveraging pretreatment magnetic resonance imaging (MRI) and radiation therapy treatment planning data including radiation dose distribution maps. Methods and Materials: We retrospectively analyzed a cohort of 105 patients with brain metastases treated by SRS/SRT with pretreatment multiparametric MRI (T1, T1 postcontrast, T2, fluid-attenuated inversion recovery). Three-dimensional radiomic features were extracted from each MRI sequence within 5 isodose regions of interest (ROIs) identified via radiation dose distribution maps and gross target volume (GTV) contours. Clinical features including patient performance status, number of lesions treated, tumor volume, and tumor stage were collected to serve as a baseline for comparison. Cox proportional hazards (CPH) modeling and Kaplan-Meier analysis were used to model time-to-DBM development. Results: CPH models trained using radiomic features achieved a mean concordance index (c-index) of 0.63 (standard deviation [SD], 0.08) compared with a c-index of 0.49 (SD, 0.09) for CPH models trained using clinical factors. A CPH model trained using both radiomic and clinical features achieved a c-index of 0.69 (SD, 0.08). The identified radiomic signature was able to stratify patients into distinct risk groups with statistically significant differences (P = .00007) in time-to-DBM development as measured by log-rank test. Clinical features were unable to do the same. Radiomic features from the peritumoral 50% to 75% isodose ROI and GTV region were most predictive of DBM development. Conclusions: Our results suggest that radiomic features extracted from pretreatment MRI and multiple isodose ROIs can model time-to-DBM development in patients receiving SRS/SRT for brain metastases, outperforming clinical feature baselines. Notably, we believe we are the first to leverage SRS/SRT dose maps for ROI identification and subsequent radiomic analysis of peritumoral and untargeted brain regions using multiparametric MRI. We observed that the peritumoral environment may be implicated in DBM development for SRS/SRT-treated brain metastases. Our preliminary results might enable the identification of patients with predisposition to DBM development and prompt subsequent changes in disease management.

Enhancing Modality-Agnostic Representations via Meta-learning for Brain Tumor Segmentation.

In medical vision, different imaging modalities provide complementary information. However, in practice, not all modalities may be available during inference or even training. Previous approaches, e.g., knowledge distillation or image synthesis, often assume the availability of full modalities for all subjects during training; this is unrealistic and impractical due to the variability in data collection across sites. We propose a novel approach to learn enhanced modality-agnostic representations by employing a meta-learning strategy in training, even when only limited full modality samples are available. Meta-learning enhances partial modality representations to full modality representations by meta-training on partial modality data and meta-testing on limited full modality samples. Additionally, we co-supervise this feature enrichment by introducing an auxiliary adversarial learning branch. More specifically, a missing modality detector is used as a discriminator to mimic the full modality setting. Our segmentation framework significantly outperforms state-of-the-art brain tumor segmentation techniques in missing modality scenarios.

Token Sparsification for Faster Medical Image Segmentation.

Can we use sparse tokens for dense prediction, e.g., segmentation? Although token sparsification has been applied to Vision Transformers (ViT) to accelerate classification, it is still unknown how to perform segmentation from sparse tokens. To this end, we reformulate segmentation as a sparse encoding → token completion → dense decoding (SCD) pipeline. We first empirically show that naïvely applying existing approaches from classification token pruning and masked image modeling (MIM) leads to failure and inefficient training caused by inappropriate sampling algorithms and the low quality of the restored dense features. In this paper, we propose Soft-topK Token Pruning (STP) and Multi-layer Token Assembly (MTA) to address these problems. In sparse encoding, STP predicts token importance scores with a lightweight sub-network and samples the topK tokens. The intractable topK gradients are approximated through a continuous perturbed score distribution. In token completion, MTA restores a full token sequence by assembling both sparse output tokens and pruned multi-layer intermediate ones. The last dense decoding stage is compatible with existing segmentation decoders, e.g., UNETR. Experiments show SCD pipelines equipped with STP and MTA are much faster than baselines without token pruning in both training (up to 120% higher throughput) and inference (up to 60.6% higher throughput) while maintaining segmentation quality. Code is available here: https://github.com/cvlab-stonybrook/TokenSparse-for-MedSeg.

Predicting Mechanical Ventilation and Mortality in COVID-19 Using Radiomics and Deep Learning on Chest Radiographs: A Multi-Institutional Study.

In this study, we aimed to predict mechanical ventilation requirement and mortality using computational modeling of chest radiographs (CXRs) for coronavirus disease 2019 (COVID-19) patients. This two-center, retrospective study analyzed 530 deidentified CXRs from 515 COVID-19 patients treated at Stony Brook University Hospital and Newark Beth Israel Medical Center between March and August 2020. Linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and random forest (RF) machine learning classifiers to predict mechanical ventilation requirement and mortality were trained and evaluated using radiomic features extracted from patients' CXRs. Deep learning (DL) approaches were also explored for the clinical outcome prediction task and a novel radiomic embedding framework was introduced. All results are compared against radiologist grading of CXRs (zone-wise expert severity scores). Radiomic classification models had mean area under the receiver operating characteristic curve (mAUCs) of 0.78 ± 0.05 (sensitivity = 0.72 ± 0.07, specificity = 0.72 ± 0.06) and 0.78 ± 0.06 (sensitivity = 0.70 ± 0.09, specificity = 0.73 ± 0.09), compared with expert scores mAUCs of 0.75 ± 0.02 (sensitivity = 0.67 ± 0.08, specificity = 0.69 ± 0.07) and 0.79 ± 0.05 (sensitivity = 0.69 ± 0.08, specificity = 0.76 ± 0.08) for mechanical ventilation requirement and mortality prediction, respectively. Classifiers using both expert severity scores and radiomic features for mechanical ventilation (mAUC = 0.79 ± 0.04, sensitivity = 0.71 ± 0.06, specificity = 0.71 ± 0.08) and mortality (mAUC = 0.83 ± 0.04, sensitivity = 0.79 ± 0.07, specificity = 0.74 ± 0.09) demonstrated improvement over either artificial intelligence or radiologist interpretation alone. Our results also suggest instances in which the inclusion of radiomic features in DL improves model predictions over DL alone. The models proposed in this study and the prognostic information they provide might aid physician decision making and efficient resource allocation during the COVID-19 pandemic.

Predicting Clinical Outcomes in COVID-19 using Radiomics and Deep Learning on Chest Radiographs: A Multi-Institutional Study.

We predict mechanical ventilation requirement and mortality using computational modeling of chest radiographs (CXRs) for coronavirus disease 2019 (COVID-19) patients. This two-center, retrospective study analyzed 530 deidentified CXRs from 515 COVID-19 patients treated at Stony Brook University Hospital and Newark Beth Israel Medical Center between March and August 2020. DL and machine learning classifiers to predict mechanical ventilation requirement and mortality were trained and evaluated using patient CXRs. A novel radiomic embedding framework was also explored for outcome prediction. All results are compared against radiologist grading of CXRs (zone-wise expert severity scores). Radiomic and DL classification models had mAUCs of 0.78+/-0.02 and 0.81+/-0.04, compared with expert scores mAUCs of 0.75+/-0.02 and 0.79+/-0.05 for mechanical ventilation requirement and mortality prediction, respectively. Combined classifiers using both radiomics and expert severity scores resulted in mAUCs of 0.79+/-0.04 and 0.83+/-0.04 for each prediction task, demonstrating improvement over either artificial intelligence or radiologist interpretation alone. Our results also suggest instances where inclusion of radiomic features in DL improves model predictions, something that might be explored in other pathologies. The models proposed in this study and the prognostic information they provide might aid physician decision making and resource allocation during the COVID-19 pandemic.

Effects of Prone Ventilation on Oxygenation, Inflammation, and Lung Infiltrates in COVID-19 Related Acute Respiratory Distress Syndrome: A Retrospective Cohort Study.

Patients receiving mechanical ventilation for coronavirus disease 2019 (COVID-19) related, moderate-to-severe acute respiratory distress syndrome (CARDS) have mortality rates between 76-98%. The objective of this retrospective cohort study was to identify differences in prone ventilation effects on oxygenation, pulmonary infiltrates (as observed on chest X-ray (CXR)), and systemic inflammation in CARDS patients by survivorship and to identify baseline characteristics associated with survival after prone ventilation. The study cohort included 23 patients with moderate-to-severe CARDS who received prone ventilation for ≥16 h/day and was segmented by living status: living (n = 6) and deceased (n = 17). Immediately after prone ventilation, PaO2/FiO2 improved by 108% (p < 0.03) for the living and 150% (p < 3 × 10-4) for the deceased. However, the 48 h change in lung infiltrate severity in gravity-dependent lung zones was significantly better for the living than for the deceased (p < 0.02). In CXRs of the lower lungs before prone ventilation, we observed 5 patients with confluent infiltrates bilaterally, 12 patients with ground-glass opacities (GGOs) bilaterally, and 6 patients with mixed infiltrate patterns; 80% of patients with confluent infiltrates were alive vs. 8% of patients with GGOs. In conclusion, our small study indicates that CXRs may offer clinical utility in selecting patients with moderate-to-severe CARDS who will benefit from prone ventilation. Additionally, our study suggests that lung infiltrate severity may be a better indicator of patient disposition after prone ventilation than PaO2/FiO2.

COVID-19 and ethnicity: A novel pathophysiological role for inflammation.

INTRODUCTION: There have been recent mounting concerns regarding multiple reports stating a significantly elevated relative-risk of COVID-19 mortality amongst the Black and Minority Ethnic (BAME) population. An urgent national enquiry investigating the possible reasons for this phenomenon has been issued in the UK. Inflammation is at the forefront of COVID-19 research as disease severity appears to correlate with pro-inflammatory cytokine dysregulation. This narrative review aims to shed light on the novel, pathophysiological role of inflammation in contributing towards the increased COVID-19 mortality risk amongst the BAME population. METHODS: Searches in PubMed, Medline, Scopus, medRxiv and Google Scholar were performed to identify articles published in English from inception to 18th June 2020. These databases were searched using keywords including: 'COVID-19' or 'Black and Minority Ethnic' or 'Inflammation'. A narrative review was synthesized using these included articles. RESULTS: We suggest a novel pathophysiological mechanism by which acute inflammation from COVID-19 may augment existing chronic inflammation, in order to potentiate a 'cytokine storm' and thus the more severe disease phenotype observed in the BAME population. Obesity, insulin resistance, cardiovascular disease, psychological stress, chronic infections and genetic predispositions are all relevant factors which may be contributing to elevated chronic systemic inflammation amongst the BAME population. CONCLUSION: Overall, this review provides early insights and directions for ongoing research regarding the pathophysiological mechanisms that may explain the severe COVID-19 disease phenotype observed amongst the BAME population. We suggest 'personalization' of chronic disease management, which can be used with other interventions, in order to tackle this.

Intensity-modulated radiotherapy for head-and-neck cancer in the community setting.

PURPOSE: To review outcomes with intensity-modulated radiation therapy (IMRT) in the community setting for the treatment of nasopharyngeal and oropharyngeal cancer. METHODS AND MATERIALS: Between April 2003 and April 2007, 69 patients with histologically confirmed cancer of the nasopharynx and oropharynx underwent IMRT in our practice. The primary sites included nasopharynx (11), base of tongue (18), and tonsil (40). The disease stage distribution was as follows: 2 Stage I, 11 Stage II, 16 Stage III, and 40 Stage IV. All were treated with a simultaneous integrated boost IMRT technique. The median prescribed doses were 70 Gy to the planning target volume, 59.4 Gy to the high-risk subclinical volume, and 54 Gy to the low-risk subclinical volume. Forty-five patients (65%) received concurrent chemotherapy. Toxicity was graded according to the Radiation Therapy Oncology Group toxicity criteria. Progression-free and overall survival rates were estimated with the Kaplan-Meier product-limit method. RESULTS: Median duration of follow-up was 18 months. The estimated 2-year local control, regional control, distant control, and overall survival rates were 98%, 100%, 98%, and 90%, respectively. The most common acute toxicities were dermatitis (32 Grade 1, 32 Grade 2, 5 Grade 3), mucositis (8 Grade 1, 33 Grade 2, 28 Grade 3), and xerostomia (0 Grade 1, 29 Grade 2, 40 Grade 3). CONCLUSIONS: Intensity-modulated radiotherapy in the community setting can be accomplished safely and effectively. Systematic internal review systems are recommended for quality control until sufficient experience develops.