Evaluating county-level lung cancer incidence from environmental radiation exposure, PM2.5, and other exposures with regression and machine learning models.

Characterizing the interplay between exposures shaping the human exposome is vital for uncovering the etiology of complex diseases. For example, cancer risk is modified by a range of multifactorial external environmental exposures. Environmental, socioeconomic, and lifestyle factors all shape lung cancer risk. However, epidemiological studies of radon aimed at identifying populations at high risk for lung cancer often fail to consider multiple exposures simultaneously. For example, moderating factors, such as PM2.5, may affect the transport of radon progeny to lung tissue. This ecological analysis leveraged a population-level dataset from the National Cancer Institute's Surveillance, Epidemiology, and End-Results data (2013-17) to simultaneously investigate the effect of multiple sources of low-dose radiation (gross [Formula: see text] activity and indoor radon) and PM2.5 on lung cancer incidence rates in the USA. County-level factors (environmental, sociodemographic, lifestyle) were controlled for, and Poisson regression and random forest models were used to assess the association between radon exposure and lung and bronchus cancer incidence rates. Tree-based machine learning (ML) method perform better than traditional regression: Poisson regression: 6.29/7.13 (mean absolute percentage error, MAPE), 12.70/12.77 (root mean square error, RMSE); Poisson random forest regression: 1.22/1.16 (MAPE), 8.01/8.15 (RMSE). The effect of PM2.5 increased with the concentration of environmental radon, thereby confirming findings from previous studies that investigated the possible synergistic effect of radon and PM2.5 on health outcomes. In summary, the results demonstrated (1) a need to consider multiple environmental exposures when assessing radon exposure's association with lung cancer risk, thereby highlighting (1) the importance of an exposomics framework and (2) that employing ML models may capture the complex interplay between environmental exposures and health, as in the case of indoor radon exposure and lung cancer incidence.

Developmental Epidemiology of Pediatric Anxiety Disorders.

This review summarizes the developmental epidemiology of childhood and adolescent anxiety disorders. It discusses the coronavirus disease of 2019 (COVID-19) pandemic, sex differences, longitudinal course, and stability of anxiety disorders in addition to recurrence and remission. The trajectory of anxiety disorders-whether homotypic (ie, the same anxiety disorder persists over time) or heterotypic (ie, an anxiety disorder shifts to a different diagnosis over time) is discussed with regard to social, generalized, and separation anxiety disorders as well as specific phobia, and panic disorder. Finally, strategies for early recognition, prevention, and treatment of disorders are discussed.

In with the old, in with the new: machine learning for time to event biomedical research.

The predictive modeling literature for biomedical applications is dominated by biostatistical methods for survival analysis, and more recently some out of the box machine learning approaches. In this article, we show a presentation of a machine learning method appropriate for time-to-event modeling in the area of prostate cancer long-term disease progression. Using XGBoost adapted to long-term disease progression, we developed a predictive model for 118 788 patients with localized prostate cancer at diagnosis from the Department of Veterans Affairs (VA). Our model accounted for patient censoring. Harrell's c-index for our model using only features available at the time of diagnosis was 0.757 95% confidence interval [0.756, 0.757]. Our results show that machine learning methods like XGBoost can be adapted to use accelerated failure time (AFT) with censoring to model long-term risk of disease progression. The long median survival justifies and requires censoring. Overall, we show that an existing machine learning approach can be used for AFT outcome modeling in prostate cancer, and more generally for other chronic diseases with long observation times.

Identifying intragenic functional modules of genomic variations associated with cancer phenotypes by learning representation of association networks.

BACKGROUND: Genome-wide Association Studies (GWAS) aims to uncover the link between genomic variation and phenotype. They have been actively applied in cancer biology to investigate associations between variations and cancer phenotypes, such as susceptibility to certain types of cancer and predisposed responsiveness to specific treatments. Since GWAS primarily focuses on finding associations between individual genomic variations and cancer phenotypes, there are limitations in understanding the mechanisms by which cancer phenotypes are cooperatively affected by more than one genomic variation. RESULTS: This paper proposes a network representation learning approach to learn associations among genomic variations using a prostate cancer cohort. The learned associations are encoded into representations that can be used to identify functional modules of genomic variations within genes associated with early- and late-onset prostate cancer. The proposed method was applied to a prostate cancer cohort provided by the Veterans Administration's Million Veteran Program to identify candidates for functional modules associated with early-onset prostate cancer. The cohort included 33,159 prostate cancer patients, 3181 early-onset patients, and 29,978 late-onset patients. The reproducibility of the proposed approach clearly showed that the proposed approach can improve the model performance in terms of robustness. CONCLUSIONS: To our knowledge, this is the first attempt to use a network representation learning approach to learn associations among genomic variations within genes. Associations learned in this way can lead to an understanding of the underlying mechanisms of how genomic variations cooperatively affect each cancer phenotype. This method can reveal unknown knowledge in the field of cancer biology and can be utilized to design more advanced cancer-targeted therapies.

Improved Dose Estimates for Fluoroscopically Guided Lumbar Epidural Injections.

OBJECTIVE: The goal of the study was to determine the potential impact of system inaccuracies and table attenuation on fluoroscope-reported dose values. DESIGN: An Institutional Review Board-approved study was conducted to collect detailed acquisition and patient exposure data for fluoroscopy-guided lumbar epidural injections. BACKGROUND: System-reported dosimetry values, especially the air Kinetic Energy Released per unit MAss and dose-area product metrics, are routinely used for estimating the radiation burden to patients undergoing fluoroscopy-guided procedures. However, these metrics do not account for other factors, such as acquisition geometry, where the table may attenuate a substantial fraction of the x-ray intensity, and system dosimetry inaccuracies, which are only required to be accurate within ±35%. METHODS: Acquisition data from 46 patients undergoing fluoroscopy-guided lumbar epidural injections were collected to better estimate the true incident dose-area product. Gantry angles, x-ray technique factors, and field sizes were collected to characterize each procedure. Additionally, the fluoroscope dosimetry accuracy and table attenuation properties were evaluated as a function of kVp to generate the correction factors necessary for accurate dosimetry estimates. RESULTS: The system-reported values overestimated the total patient entrance dose-area product by an average of 34% (13-44%). Errors may be substantially higher for systems with less accurate fluoroscopes or more anterior-posterior projections. Correcting system-reported dosimetry values for systematic inaccuracies and variability can substantially improve fluoroscopic dose values. CONCLUSIONS: Including corrections for system output inaccuracies and acquisition factors such as table attenuation is necessary for any reliable assessment of radiation burden to patients associated with fluoroscopy-guided procedures.

Estimability index for volume quantification of homogeneous spherical lesions in computed tomography.

Volume of lung nodules is an important biomarker, quantifiable from computed tomography (CT) images. The usefulness of volume quantification, however, depends on the precision of quantification. Experimental assessment of precision is time consuming. A mathematical estimability model was used to assess the quantification precision of CT nodule volumetry in terms of an index ([Formula: see text]), incorporating image noise and resolution, nodule properties, and segmentation software. The noise and resolution were characterized in terms of noise power spectrum and task transfer function. The nodule properties and segmentation algorithm were modeled in terms of a task function and a template function, respectively. The [Formula: see text] values were benchmarked against experimentally acquired precision values from an anthropomorphic chest phantom across 54 acquisition protocols, 2 nodule sizes, and 2 volume segmentation softwares. [Formula: see text] exhibited correlation with experimental precision across nodule sizes and acquisition protocols but dependence on segmentation software. Compared to the assessment of empirical precision, which required [Formula: see text] to perform the segmentation, the [Formula: see text] method required [Formula: see text] from data collection to mathematical computation. A mathematical modeling of volume quantification provides efficient prediction of quantitative performance. It establishes a method to verify quantitative compliance and to optimize clinical protocols for chest CT volumetry.

Can Breast Compression Be Reduced in Digital Mammography and Breast Tomosynthesis?

OBJECTIVE: The objective of this study was to investigate the impact of decreasing breast compression during digital mammography and breast tomosynthesis (DBT) on perceived pain and image quality. MATERIALS AND METHODS: In this two-part study, two groups of women with prior mammograms were recruited. In part 1, subjects were positioned for craniocaudal (CC) and mediolateral oblique (MLO) views, and four levels of compression force were applied to evaluate changes in breast thickness, perceived pain, and relative tissue coverage. No imaging was performed. In part 2, two MLO DBT images of one breast of each patient were acquired at standard and reduced compression. Blurring artifacts and tissue coverage were judged by three breast imaging radiologists, and compression force, breast thickness, relative tissue coverage, and perceived pain were recorded. RESULTS: Only the first reduction in force was feasible because further reduction resulted in inadequate breast immobilization. Mean force reductions of 48% and 47% for the CC and MLO views, respectively, resulted in a significantly reduced perceived pain level, whereas the thickness of the compressed breast increased by 0.02 cm (CC view) and 0.09 (MLO view, part 1 of the study) and 0.38 cm (MLO view, part 2 of the study), respectively, with no change in tissue coverage or increase in motion blurring. CONCLUSION: Mammography and DBT acquisitions may be possible using half of the compression force used currently, with a significant and substantial reduction in perceived pain with no clinically significant change in breast thickness and tissue coverage.

Organ dose variability and trends in tomosynthesis and radiography.

The purpose of this study was to investigate relationships between patient attributes and organ dose for a population of computational phantoms for 20 tomosynthesis and radiography protocols. Organ dose was estimated from 54 adult computational phantoms (age: 18 to 78 years, weight 52 to 117 kg) using a validated Monte-Carlo simulation (PENELOPE) of a system capable of performing tomosynthesis and radiography. The geometry and field of view for each exam were modeled to match clinical protocols. For each protocol, the energy deposited in each organ was estimated by the simulations, converted to dose units, and then normalized by exposure in air. Dose to radiosensitive organs was studied as a function of average patient thickness in the region of interest and as a function of body mass index. For tomosynthesis, organ doses were also studied as a function of x-ray tube position. This work developed comprehensive information for organ dose dependencies across a range of tomosynthesis and radiography protocols. The results showed a protocol-dependent exponential decrease with an increasing patient size. There was a variability in organ dose across the patient population, which should be incorporated in the metrology of organ dose. The results can be used to prospectively and retrospectively estimate organ dose for tomosynthesis and radiography.

Breast dose reduction with organ-based, wide-angle tube current modulated CT.

This study aimed to estimate the organ dose reduction potential for organ-dose-based tube current modulated (ODM) thoracic computed tomography (CT) with a wide dose reduction arc. Twenty-one computational anthropomorphic phantoms (XCAT) were used to create a virtual patient population with clinical anatomic variations. The phantoms were created based on patient images with normal anatomy (age range: 27 to 66 years, weight range: 52.0 to 105.8 kg). For each phantom, two breast tissue compositions were simulated: [Formula: see text] and [Formula: see text] (glandular-to-adipose ratio). A validated Monte Carlo program (PENELOPE, Universitat de Barcelona, Spain) was used to estimate the organ dose for standard tube current modulation (TCM) (SmartmA, GE Healthcare) and ODM (GE Healthcare) for a commercial CT scanner (Revolution, GE Healthcare) using a typical clinical thoracic CT protocol. Both organ dose and [Formula: see text]-to-organ dose conversion coefficients ([Formula: see text] factors) were compared between TCM and ODM. ODM significantly reduced all radiosensitive organ doses ([Formula: see text]). The breast dose was reduced by [Formula: see text]. For [Formula: see text] factors, organs in the anterior region (e.g., thyroid and stomach) exhibited substantial decreases, and the medial, distributed, and posterior region saw either an increase of less than 5% or no significant change. ODM significantly reduced organ doses especially for radiosensitive superficial anterior organs such as the breasts.

The compressed breast during mammography and breast tomosynthesis: in vivo shape characterization and modeling.

To characterize and develop a patient-based 3D model of the compressed breast undergoing mammography and breast tomosynthesis. During this IRB-approved, HIPAA-compliant study, 50 women were recruited to undergo 3D breast surface imaging with structured light (SL) during breast compression, along with simultaneous acquisition of a tomosynthesis image. A pair of SL systems were used to acquire 3D surface images by projecting 24 different patterns onto the compressed breast and capturing their reflection off the breast surface in approximately 12-16 s. The 3D surface was characterized and modeled via principal component analysis. The resulting surface model was combined with a previously developed 2D model of projected compressed breast shapes to generate a full 3D model. Data from ten patients were discarded due to technical problems during image acquisition. The maximum breast thickness (found at the chest-wall) had an average value of 56 mm, and decreased 13% towards the nipple (breast tilt angle of 5.2°). The portion of the breast not in contact with the compression paddle or the support table extended on average 17 mm, 18% of the chest-wall to nipple distance. The outermost point along the breast surface lies below the midline of the total thickness. A complete 3D model of compressed breast shapes was created and implemented as a software application available for download, capable of generating new random realistic 3D shapes of breasts undergoing compression. Accurate characterization and modeling of the breast curvature and shape was achieved and will be used for various image processing and clinical tasks.

CT breast dose reduction with the use of breast positioning and organ-based tube current modulation.

PURPOSE: This study aimed to investigate the breast dose reduction potential of a breast-positioning (BP) technique for thoracic CT examinations with organ-based tube current modulation (OTCM). METHODS: This study included 13 female anthropomorphic computational phantoms (XCAT, age range: 27-65 y.o., weight range: 52-105.8 kg). Each phantom was modified to simulate three breast sizes in standard supine geometry. The modeled breasts were then morphed to emulate BP that constrained the majority of the breast tissue inside the 120° anterior tube current (mA) reduction zone. The OTCM mA value was modeled using a ray-tracing program, which reduced the mA to 20% in the anterior region with a corresponding increase to the posterior region. The organ doses were estimated by a validated Monte Carlo program for a typical clinical CT system (SOMATOM Definition Flash, Siemens Healthcare). The simulated organ doses and organ doses normalized by CTDIvol were used to compare three CT protocols: attenuation-based tube current modulation (ATCM), OTCM, and OTCM with BP (OTCMBP ). RESULTS: On average, compared to ATCM, OTCM reduced breast dose by 19.3 ± 4.5%, whereas OTCMBP reduced breast dose by 38.6 ± 8.1% (an additional 23.8 ± 9.4%). The dose saving of OTCMBP was more significant for larger breasts (on average 33, 38, and 44% reduction for 0.5, 1, and 2 kg breasts, respectively). Compared to ATCM, OTCMBP also reduced thymus and heart dose by 15.1 ± 7.4% and 15.9 ± 6.2% respectively. CONCLUSIONS: In thoracic CT examinations, OTCM with a breast-positioning technique can markedly reduce unnecessary exposure to radiosensitive organs in anterior chest wall, specifically breast tissue. The breast dose reduction is more notable for women with larger breasts.

Simulations of breast cancer imaging using gamma-ray stimulated emission computed tomography.

Here, we present an innovative imaging technology for breast cancer using gamma-ray stimulated spectroscopy based on the nuclear resonance fluorescence (NRF) technique. In NRF, a nucleus of a given isotope selectively absorbs gamma rays with energy exactly equal to one of its quantized energy states, emitting an outgoing gamma ray with energy nearly identical to that of the incident gamma ray. Due to its application of NRF, gamma-ray stimulated spectroscopy is sensitive to trace element concentration changes, which are suspected to occur at early stages of breast cancer, and therefore can be potentially used to noninvasively detect and diagnose cancer in its early stages. Using Monte-Carlo simulations, we have designed and demonstrated an imaging system that uses gamma-ray stimulated spectroscopy for visualizing breast cancer. We show that gamma-ray stimulated spectroscopy is able to visualize breast cancer lesions based primarily on the differences in the concentrations of trace elements between diseased and healthy tissue, rather than differences in density that are crucial for X-ray mammography. The technique shows potential for early breast cancer detection; however, improvements are needed in gamma-ray laser technology for the technique to become a clinically feasible method of detecting and diagnosing cancer at early stages.