Genotyping‐in‐Thousands by sequencing (GT‐seq) is a method that uses next‐generation sequencing of multiplexed PCR products to generate genotypes from relatively small panels (50–500) of targeted ...single‐nucleotide polymorphisms (SNPs) for thousands of individuals in a single Illumina HiSeq lane. This method uses only unlabelled oligos and PCR master mix in two thermal cycling steps for amplification of targeted SNP loci. During this process, sequencing adapters and dual barcode sequence tags are incorporated into the amplicons enabling thousands of individuals to be pooled into a single sequencing library. Post sequencing, reads from individual samples are split into individual files using their unique combination of barcode sequences. Genotyping is performed with a simple perl script which counts amplicon‐specific sequences for each allele, and allele ratios are used to determine the genotypes. We demonstrate this technique by genotyping 2068 individual steelhead trout (Oncorhynchus mykiss) samples with a set of 192 SNP markers in a single library sequenced in a single Illumina HiSeq lane. Genotype data were 99.9% concordant to previously collected TaqMan™ genotypes at the same 192 loci, but call rates were slightly lower with GT‐seq (96.4%) relative to Taqman (99.0%). Of the 192 SNPs, 187 were genotyped in ≥90% of the individual samples and only 3 SNPs were genotyped in <70% of samples. This study demonstrates amplicon sequencing with GT‐seq greatly reduces the cost of genotyping hundreds of targeted SNPs relative to existing methods by utilizing a simple library preparation method and massive efficiency of scale.
Background
The Prostate Imaging Reporting and Data System version 2 (PI‐RADSv2) has been in use since 2015; while interreader reproducibility has been studied, there has been a paucity of studies ...investigating the intrareader reproducibility of PI‐RADSv2.
Purpose
To evaluate both intra‐ and interreader reproducibility of PI‐RADSv2 in the assessment of intraprostatic lesions using multiparametric magnetic resonance imaging (mpMRI).
Study Type
Retrospective.
Population/Subjects
In all, 102 consecutive biopsy‐naïve patients who underwent prostate MRI and subsequent MR/transrectal ultrasonography (MR/TRUS)‐guided biopsy.
Field Strength/Sequences
Prostate mpMRI at 3T using endorectal with phased array surface coils (TW MRI, DW MRI with ADC maps and b2000 DW MRI, DCE MRI).
Assessment
Previously detected and biopsied lesions were scored by four readers from four different institutions using PI‐RADSv2. Readers scored lesions during two readout rounds with a 4‐week washout period.
Statistical Tests
Kappa (κ) statistics and specific agreement (Po) were calculated to quantify intra‐ and interreader reproducibility of PI‐RADSv2 scoring. Lesion measurement agreement was calculated using the intraclass correlation coefficient (ICC).
Results
Overall intrareader reproducibility was moderate to substantial (κ = 0.43–0.67, Po = 0.60–0.77), while overall interreader reproducibility was poor to moderate (κ = 0.24, Po = 46). Readers with more experience showed greater interreader reproducibility than readers with intermediate experience in the whole prostate (P = 0.026) and peripheral zone (P = 0.002). Sequence‐specific interreader agreement for all readers was similar to the overall PI‐RADSv2 score, with κ = 0.24, 0.24, and 0.23 and Po = 0.47, 0.44, and 0.54 in T2‐weighted, diffusion‐weighted imaging (DWI), and dynamic contrast‐enhanced (DCE), respectively. Overall intrareader and interreader ICC for lesion measurement was 0.82 and 0.71, respectively.
Data Conclusion
PI‐RADSv2 provides moderate intrareader reproducibility, poor interreader reproducibility, and moderate interreader lesion measurement reproducibility. These findings suggest a need for more standardized reader training in prostate MRI.
Level of Evidence: 2
Technical Efficacy: Stage 2
Pathologic grading plays a key role in prostate cancer risk stratification and treatment selection, traditionally assessed from systemic core needle biopsies sampled throughout the prostate gland. ...Multiparametric magnetic resonance imaging (mpMRI) has become a well-established clinical tool for detecting and localizing prostate cancer. However, both pathologic and radiologic assessment suffer from poor reproducibility among readers. Artificial intelligence (AI) methods show promise in aiding the detection and assessment of imaging-based tasks, dependent on the curation of high-quality training sets. This review provides an overview of recent advances in AI applied to mpMRI and digital pathology in prostate cancer which enable advanced characterization of disease through combined radiology-pathology assessment.
Chest X-ray plays a key role in diagnosis and management of COVID-19 patients and imaging features associated with clinical elements may assist with the development or validation of automated image ...analysis tools. We aimed to identify associations between clinical and radiographic features as well as to assess the feasibility of deep learning applied to chest X-rays in the setting of an acute COVID-19 outbreak.
A retrospective study of X-rays, clinical, and laboratory data was performed from 48 SARS-CoV-2 RT-PCR positive patients (age 60±17 years, 15 women) between February 22 and March 6, 2020 from a tertiary care hospital in Milan, Italy. Sixty-five chest X-rays were reviewed by two radiologists for alveolar and interstitial opacities and classified by severity on a scale from 0 to 3. Clinical factors (age, symptoms, comorbidities) were investigated for association with opacity severity and also with placement of central line or endotracheal tube. Deep learning models were then trained for two tasks: lung segmentation and opacity detection. Imaging characteristics were compared to clinical datapoints using the unpaired student's t-test or Mann-Whitney U test. Cohen's kappa analysis was used to evaluate the concordance of deep learning to conventional radiologist interpretation.
Fifty-six percent of patients presented with alveolar opacities, 73% had interstitial opacities, and 23% had normal X-rays. The presence of alveolar or interstitial opacities was statistically correlated with age (P = 0.008) and comorbidities (P = 0.005). The extent of alveolar or interstitial opacities on baseline X-ray was significantly associated with the presence of endotracheal tube (P = 0.0008 and P = 0.049) or central line (P = 0.003 and P = 0.007). In comparison to human interpretation, the deep learning model achieved a kappa concordance of 0.51 for alveolar opacities and 0.71 for interstitial opacities.
Chest X-ray analysis in an acute COVID-19 outbreak showed that the severity of opacities was associated with advanced age, comorbidities, as well as acuity of care. Artificial intelligence tools based upon deep learning of COVID-19 chest X-rays are feasible in the acute outbreak setting.
Background
The Prostate Imaging Reporting and Data System (PI‐RADS) provides guidelines for risk stratification of lesions detected on multiparametric MRI (mpMRI) of the prostate but suffers from ...high intra/interreader variability.
Purpose
To develop an artificial intelligence (AI) solution for PI‐RADS classification and compare its performance with an expert radiologist using targeted biopsy results.
Study Type
Retrospective study including data from our institution and the publicly available ProstateX dataset.
Population
In all, 687 patients who underwent mpMRI of the prostate and had one or more detectable lesions (PI‐RADS score >1) according to PI‐RADSv2.
Field Strength/Sequence
T2‐weighted, diffusion‐weighted imaging (DWI; five evenly spaced b values between b = 0–750 s/mm2) for apparent diffusion coefficient (ADC) mapping, high b‐value DWI (b = 1500 or 2000 s/mm2), and dynamic contrast‐enhanced T1‐weighted series were obtained at 3.0T.
Assessment
PI‐RADS lesions were segmented by a radiologist. Bounding boxes around the T2/ADC/high‐b value segmentations were stacked and saved as JPEGs. These images were used to train a convolutional neural network (CNN). The PI‐RADS scores obtained by the CNN were compared with radiologist scores. The cancer detection rate was measured from a subset of patients who underwent biopsy.
Statistical Tests
Agreement between the AI and the radiologist‐driven PI‐RADS scores was assessed using a kappa score, and differences between categorical variables were assessed with a Wald test.
Results
For the 1034 detection lesions, the kappa score for the AI system vs. the expert radiologist was moderate, at 0.40. However, there was no significant difference in the rates of detection of clinically significant cancer for any PI‐RADS score in 86 patients undergoing targeted biopsy (P = 0.4–0.6).
Data Conclusion
We developed an AI system for assignment of a PI‐RADS score on segmented lesions on mpMRI with moderate agreement with an expert radiologist and a similar ability to detect clinically significant cancer.
Level of Evidence
4
Technical Efficacy Stage
2
Patients diagnosed with high risk localized prostate cancer have variable outcomes following surgery. Trials of intense neoadjuvant androgen deprivation therapy (NADT) have shown lower rates of ...recurrence among patients with minimal residual disease after treatment. The molecular features that distinguish exceptional responders from poor responders are not known.
To identify genomic and histologic features associated with treatment resistance at baseline.
Targeted biopsies were obtained from 37 men with intermediate- to high-risk prostate cancer before receiving 6 mo of ADT plus enzalutamide. Biopsy tissues were used for whole-exome sequencing and immunohistochemistry (IHC).
We assessed the relationship of molecular features with final pathologic response using a cutpoint of 0.05 cm3 for residual cancer burden to compare exceptional responders to incomplete and nonresponders. We assessed intratumoral heterogeneity at the tissue and genomic level, and compared the volume of residual disease to the Shannon diversity index for each tumor. We generated multivariate models of resistance based on three molecular features and one histologic feature, with and without multiparametric magnetic resonance imaging estimates of baseline tumor volume.
Loss of chromosome 10q (containing PTEN) and alterations to TP53 were predictive of poor response, as were the expression of nuclear ERG on IHC and the presence of intraductal carcinoma of the prostate. Patients with incompletely and nonresponding tumors harbored greater tumor diversity as estimated via phylogenetic tree reconstruction from DNA sequencing and analysis of IHC staining. Our four-factor binary model (area under the receiver operating characteristic curve AUC 0.89) to predict poor response correlated with greater diversity in our cohort and a validation cohort of 57 Gleason score 8–10 prostate cancers from The Cancer Genome Atlas. When baseline tumor volume was added to the model, it distinguished poor response to NADT with an AUC of 0.98. Prospective use of this model requires further retrospective validation with biopsies from additional trials.
A subset of prostate cancers exhibit greater histologic and genomic diversity at the time of diagnosis, and these localized tumors have greater fitness to resist therapy.
Some prostate cancer tumors do not respond well to a hormonal treatment called androgen deprivation therapy (ADT). We used tumor volume and four other parameters to develop a model to identify tumors that will not respond well to ADT. Treatments other than ADT should be considered for these patients.
A subset of patients present with high-risk localized prostate cancer that exhibits greater histologic and genomic diversity. These patients are less likely to respond to intense neoadjuvant androgen deprivation therapy.
The purpose of this study was to prospectively evaluate Prostate Imaging Reporting and Data and System version 2.1 (PI-RADSv2.1), which was released in March 2019 to update version 2.0, for prostate ...cancer detection with transrectal ultrasound-MRI fusion biopsy and 12-core systematic biopsy.
This prospective study included 110 consecutively registered patients who underwent multiparametric MRI evaluated with PI-RADSv2.1 criteria followed by fusion biopsy and systematic biopsy between April and September 2019. Lesion-based cancer detection rates (CDRs) were calculated for prostate cancer (Gleason grade group, > 0) and clinically significant prostate cancer (Gleason grade group, > 1).
A total of 171 lesions (median size, 1.1 cm) in 110 patients were detected and evaluated with PI-RADSv2.1. In 16 patients no lesion was detected, and only systematic biopsy was performed. Lesions were categorized as follows: PI-RADS category 1, 1 lesion; PI-RADS category 2, 34 lesions; PI-RADS category 3, 54 lesions; PI-RADS category 4, 52 lesions; and PI-RADS category 5, 30 lesions. Histopathologic analysis revealed prostate cancer in 74 of 171 (43.3%) lesions and clinically significant prostate cancer in 57 of 171 (33.3%) lesions. The CDRs of prostate cancer for PI-RADS 2, 3, 4, and 5 lesions were 20.0%, 24.1%, 51.9%, and 90.0%. The CDRs of clinically significant prostate cancer for PI-RADS 1, 2, 3, 4, and 5 lesions were 0%, 5.7%, 14.8%, 44.2%, and 80.0%. In 16 patients with normal multiparametric MRI findings (PI-RADS 1), the CDRs were 50.0% for PCa and 18.8% for clinically significant prostate cancer.
This investigation yielded CDRs assessed with prospectively assigned PI-RADSv2.1 scores. CDRs increased with higher PI-RADSv2.1 scores. These results can be compared with previously published outcomes derived with PI-RADS version 2.0.
Localized prostate cancers are genetically variable and frequently multifocal, comprising spatially distinct regions with multiple independently-evolving clones. To date there is no understanding of ...whether this variability can influence management decisions for patients with prostate tumors. Here, we present a single case from a clinical trial of neoadjuvant intense androgen deprivation therapy. A patient was diagnosed with a large semi-contiguous tumor by imaging, histologically composed of a large Gleason score 9 tumor with an adjacent Gleason score 7 nodule. DNA sequencing demonstrates these are two independent tumors, as only the Gleason 9 tumor harbors single-copy losses of PTEN and TP53. The PTEN/TP53-deficient tumor demonstrates treatment resistance, selecting for subclones with mutations to the remaining copies of PTEN and TP53, while the Gleason 7 PTEN-intact tumor is almost entirely ablated. These findings indicate that spatiogenetic variability is a major confounder for personalized treatment of patients with prostate cancer.
Background
Digital health devices (DHDs), technologies designed to gather, monitor, and sometimes share data about health-related behaviors or symptoms, can support the prevention or management of ...chronic conditions. DHDs range in complexity and utility, from tracking lifestyle behaviors (e.g., pedometer) to more sophisticated biometric data collection for disease self-management (e.g., glucometers). Despite these positive health benefits, supporting adoption and sustained use of DHDs remains a challenge.
Objective
This analysis examined the prevalence of, and factors associated with, DHD use within the Veterans Health Administration (VHA).
Design
National survey.
Participants
Veterans who receive VHA care and are active secure messaging users.
Main Measures
Demographics, access to technology, perceptions of using health technologies, and use of lifestyle monitoring and self-management DHDs.
Results
Among respondents, 87% were current or past users of at least one DHD, and 58% were provided a DHD by VHA. Respondents 65 + years were less likely to use a lifestyle monitoring device (AOR 0.57, 95% CI 0.39, 0.81,
P
= .002), but more likely to use a self-management device (AOR 1.69, 95% 1.10, 2.59,
P
=
.
016). Smartphone owners were more likely to use a lifestyle monitoring device (AOR 2.60, 95% CI 1.42, 4.75,
P
= .002) and a self-management device (AOR 1.83, 95% CI 1.04, 3.23,
P
= .037).
Conclusions
The current analysis describes the types of DHDs that are being adopted by Veterans and factors associated with their adoption. Results suggest that various factors influence adoption, including age, access to technology, and health status, and that these relationships may differ based on the functionalities of the device. VHA provision of devices was frequent among device users. Providing Veterans with DHDs and the training needed to use them may be important factors in facilitating device adoption. Taken together, this knowledge can inform future implementation efforts, and next steps to support patient-team decision making about DHD use.
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