• Over 90 % of cancers assessable with ultrasound have a mean stiffness >50 kPa. • 'Soft' invasive cancers are frequently small (≤10 mm), low grade and screen-detected. • Pure DCIS masses are more often soft than invasive cancers (>40 %). • Large symptomatic masses are better evaluated with SWE than small clinically occult lesions. • When assessing small lesions, 'softness' should not raise the threshold for biopsy.

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IMPORTANCE The Gleason grading system has been the most reliable tool for the prognosis of prostate cancer since its development. However, its clinical application remains limited by interobserver variability in grading and quantification, which has negative consequences for risk assessment and clinical management of prostate cancer. OBJECTIVE To examine the impact of an artificial intelligence (AI)-assisted approach to prostate cancer grading and quantification. DESIGN, SETTING, AND PARTICIPANTSThis diagnostic study was conducted at the University of Wisconsin-Madison from August 2, 2017, to December 30, 2019. The study chronologically selected 589 men with biopsy-confirmed prostate cancer who received care in the University of Wisconsin

PURPOSE To develop a novel artificial intelligence (AI)–powered method for the prediction of prostate cancer (PCa) early recurrence and identification of driver regions in PCa of all Gleason Grade Group (GGG). MATERIALS AND METHODS Deep convolutional neural networks were used to develop the AI model. The AI model was trained on The Cancer Genome Atlas Prostatic Adenocarcinoma (TCGA-PRAD) whole slide images (WSI) and data set (n = 243) to predict 3-year biochemical recurrence after radical prostatectomy (RP) and was subsequently validated on WSI from patients with PCa (n = 173) from the University of Wisconsin-Madison. RESULTS Our AI-powered platform can extract visual and subvisual morphologic features from WSI to identify driver regions predictive of early recurrence of PCa (regions of interest [ROIs]) after RP. The ROIs were ranked with AI-morphometric scores, which were prognostic for 3-year biochemical recurrence (area under the curve [AUC], 0.78), which is significantly better than the GGG overall (AUC, 0.62). The AI-morphometric scores also showed high accuracy in the prediction of recurrence for low- or intermediate-risk PCa—AUC, 0.76, 0.84, and 0.81 for GGG1, GGG2, and GGG3, respectively. These patients could benefit the most from timely adjuvant therapy after RP. The predictive value of the high-scored ROIs was validated by known PCa biomarkers studied. With this focused biomarker analysis, a potentially new STING pathway–related PCa biomarker—TMEM173—was identified. CONCLUSION Our study introduces a novel approach for identifying patients with PCa at risk for early recurrence regardless of their GGG status and for identifying cancer drivers for focused evolution-aware novel biomarker discovery.

A survey of industrial firms that regularly purchased wood products from a large, chain of custody certified sawmill (at the time of the study also Wisconsin's largest certified forest landholder) was conducted in 2003. Results were examined to: 1) provide baseline data on this topic from a region characterized as having a highly active wood products industry 2) ascertain the salience of environmentally certified wood products in this region and extrapolate the market implications this poses for other regions and 3) aid efforts to understand supply and demand implications for environmentally certified wood both in and beyond Wisconsin's borders. These companies, generally categorized as secondary wood products manufacturers, reported company perceptions and adoption trends regarding certified wood.Most industrial wood product consumers were not chain of custody certified. While wood product quality and price were found the most important sourcing criteria for this group, environmental certification ranked last.