A Stacked Generalization of 3D Orthogonal Deep Learning Convolutional Neural Networks for Improved Detection of White Matter Hyperintensities in 3D FLAIR Images

BACKGROUND AND PURPOSE:Accurate and reliable detection of white matter hyperintensities and their volume quantification can provide valuable clinical information to assess neurologic disease progression. In this work, a stacked generalization ensemble of orthogonal 3D convolutional neural networks, StackGen-Net, is explored for improving automated detection of white matter hyperintensities in 3D T2-FLAIR images.MATERIALS AND METHODS:Individual convolutional neural networks in StackGen-Net were trained on 2.5D patches from orthogonal reformatting of 3D-FLAIR (n = 21) to yield white matter hyperintensity posteriors. A meta convolutional neural network was trained to learn the functional mapping from orthogonal white matter hyperintensity posteriors to the final white matter hyperintensity prediction. The impact of training data and architecture choices on white matter hyperintensity segmentation performance was systematically evaluated on a test cohort (n = 9). The segmentation performance of StackGen-Net was compared with state-of-the-art convolutional neural network techniques on an independent test cohort from the Alzheimer’s Disease Neuroimaging Initiative-3 (n = 20).RESULTS:StackGen-Net outperformed individual convolutional neural networks in the ensemble and their combination using averaging or majority voting. In a comparison with state-of-the-art white matter hyperintensity segmentation techniques, StackGen-Net achieved a significantly higher Dice score (0.76 [SD, 0.08], F1-lesion (0.74 [SD, 0.13]), and area under precision-recall curve (0.84 [SD, 0.09]), and the lowest absolute volume difference (13.3% [SD, 9.1%]). StackGen-Net performance in Dice scores (median = 0.74) did not significantly differ (P = .22) from interobserver (median = 0.73) variability between 2 experienced neuroradiologists. We found no significant difference (P = .15) in white matter hyperintensity lesion volumes from StackGen-Net predictions and ground truth annotations.CONCLUSIONS:A stacked generalization of convolutional neural networks, utilizing multiplanar lesion information using 2.5D spatial context, greatly improved the segmentation performance of StackGen-Net compared with traditional ensemble techniques and some state-of-the-art deep learning models for 3D-FLAIR.

White matter hyperintensities (WMHs) correspond to pathologic features of axonal degeneration, demyelination, and gliosis observed within cerebral white matter.¹ Clinically, the extent of WMHs in the brain has been associated with cognitive impairment, Alzheimer’s disease and vascular dementia, and increased risk of stroke.^2,3 The detection and quantification of WMH volumes to monitor lesion burden evolution and its correlation with clinical outcomes have been of interest in clinical research.^4,5 Although the extent of WMHs can be visually scored,⁶ the categoric nature of such scoring systems makes quantitative evaluation of disease progression difficult. Manually segmenting WMHs is tedious, prone to inter- and intraobserver variability, and is, in most cases, impractical. Thus, there is an increased interest in developing fast, accurate, and reliable computer-aided automated techniques for WMH segmentation.Convolutional neural network (CNN)-based approaches have been successful in several semantic segmentation tasks in medical imaging.⁷ Recent works have proposed using deep learning–based methods for segmenting WMHs using 2D-FLAIR images.^8-11 More recently, a WMH segmentation challenge¹² was also organized (http://wmh.isi.uu.nl/) to facilitate comparison of automated segmentation of WMHs of presumed vascular origin in 2D multislice T2-FLAIR images. Architectures that used an ensemble of separately trained CNNs showed promising results in this challenge, with 3 of the top 5 winners using ensemble-based techniques.¹²Conventional 2D-FLAIR images are typically acquired with thick slices (3–4 mm) and possible slice gaps. Partial volume effects from a thick slice are likely to affect the detection of smaller lesions, both in-plane and out-of-plane. 3D-FLAIR images, with isotropic resolution, have been shown to achieve higher resolution and contrast-to-noise ratio¹³ and have shown promising results in MS lesion detection using 3D CNNs.¹⁴ Additionally, the isotropic resolution enables viewing and evaluation of the images in multiple planes. This multiplanar reformatting of 3D-FLAIR without the use of interpolating kernels is only possible due to the isotropic nature of the acquisition. Network architectures that use information from the 3 orthogonal views have been explored in recent works for CNN-based segmentation of 3D MR imaging data.¹⁵ The use of data from multiple planes allows more spatial context during training without the computational burden associated with full 3D training.¹⁶ The use of 3 orthogonal views simultaneously mirrors how humans approach this segmentation task.Ensembles of CNNs have been shown to average away the variances in the solution and the choice of model- and configuration-specific behaviors of CNNs.¹⁷ Traditionally, the solutions from these separately trained CNNs are combined by averaging or using a majority consensus. In this work, we propose the use of a stacked generalization framework (StackGen-Net) for combining multiplanar lesion information from 3D CNN ensembles to improve the detection of WMH lesions in 3D-FLAIR. A stacked generalization¹⁸ framework learns to combine solutions from individual CNNs in the ensemble. We systematically evaluated the performance of this framework and compared it with traditional ensemble techniques, such as averaging or majority voting, and state-of-the-art deep learning techniques.     

Chest wall pain following lung stereotactic body radiation therapy using 48 Gy in three fractions: A search for predictors

Purpose

Chest wall pain is an uncommon but bothersome late complication following lung stereotactic body radiation therapy. Despite numerous studies investigating predictors of chest wall pain, no clear consensus has been established for a chest wall constraint. The aim of our study was to investigate factors related to chest wall pain in a homogeneous group of patients treated at our institution.

Exposure to ambient air pollution and the incidence of lung cancer and breast cancer in the Ontario Population Health and Environment Cohort

Lung and female breast cancers are highly prevalent worldwide. Although the association between exposure to ambient fine particulate matter (PM_2.5) and lung cancer has been recognized, there is less evidence for associations with other common air pollutants such as nitrogen dioxide (NO₂) and ozone (O₃). Even less is known about potential associations between these pollutants and breast cancer. We conducted a population-based cohort study to investigate the associations of chronic exposure to PM_2.5, NO₂, O₃ and redox-weighted average of NO₂ and O₃ (O_x) with incident lung and breast cancer, using the Ontario Population Health and Environment Cohort (ONPHEC), which includes all long-term residents aged 35–85 years who lived in Ontario, Canada, 2001–2015. Incident lung and breast cancers were ascertained using the Ontario Cancer Registry. Annual estimates of exposures were assigned to the residential postal codes of subjects for each year during follow-up. We used Cox proportional-hazards models adjusting for personal- and neighborhood-level covariates. Our cohorts for lung and breast cancer analyses included ~4.9 million individuals and ~2.5 million women, respectively. During follow-up, 100,146 incident cases of lung cancer and 91,146 incident cases of breast cancer were diagnosed. The fully adjusted analyses showed positive associations of lung cancer incidence with PM_2.5 (hazard ratio [HR] = 1.02 [95% CI: 1.01–1.05] per 5.3 μg/m³) and NO₂ (HR = 1.05 [95% CI: 1.03–1.07] per 14 ppb). No associations with lung cancer were observed for O₃ or O_x. Relationships between PM_2.5 and NO₂ with lung cancer exhibited a sublinear shape. We did not find compelling evidence linking air pollution to breast cancer.     

Phase I and pharmacokinetic study of veliparib,a PARP inhibitor,and pegylated liposomal doxorubicin (PLD) in recurrent gynecologic cancer and triple negative breast cancer with long-term follow-up

Poly(ADP-ribosyl) polymerases (PARPs) are nuclear enzymes with roles in DNA damage recognition and repair. PARP1 inhibition enhances the effects of DNA-damaging agents like doxorubicin. We sought to determine the recommended phase two dose (RP2D) of veliparib with pegylated liposomal doxorubicin (PLD) in breast and recurrent gynecologic cancer patients. Veliparib and PLD were administered in a standard phase 1, 3 + 3 dose-escalation design starting at 50 mg veliparib BID on days 1–14 with PLD 40 mg/mg2 on day 1 of a 28-day cycle. Dose escalation proceeded in two strata: A (prior PLD exposure) and B (no prior PLD exposure). Patients underwent limited pharmacokinetic (PK) sampling; an expansion PK cohort was added. 44 patients with recurrent ovarian or triple negative breast cancer were enrolled. Median age 56 years; 23 patients BRCA mutation carriers; median prior regimens four. Patients received a median of four cycles of veliparib/PLD. Grade 3/4 toxicities were observed in 10% of patients. Antitumor activity was observed in both sporadic and BRCA-deficient cancers. Two BRCA mutation carriers had complete responses. Two BRCA patients developed oral squamous cell cancers after completing this regimen. PLD exposure was observed to be higher when veliparib doses were > 200 mg BID. The RP2D is 200 mg veliparib BID on days 1–14 with 40 mg/m2 PLD on day 1 of a 28-day cycle. Anti-tumor activity was seen in both strata. However, given development of long-term squamous cell cancers and the PK interaction observed, efforts should focus on other targeted combinations to improve efficacy.