Evidence map›Paper›PMID 37898290›Full record

ArticleLaboratory investigation; a journal of technical methods and pathology2023

Quantitative Histomorphometric Features of Prostate Cancer Predict Patients Who Biochemically Recur Following Prostatectomy.

Savannah R Duenweg, Michael Brehler, Allison K Lowman, Samuel A Bobholz, Fitzgerald Kyereme, Aleksandra Winiarz, Biprojit Nath, Kenneth A Iczkowski, Kenneth M Jacobsohn, Peter S LaViolette

Abstract read
In one paragraph

Article in Laboratory investigation; a journal of technical methods and pathology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Savannah R DuenwegDepartments of Biophysics, Medical College of Wisconsin, Milwaukee, Wisconsin.
Michael BrehlerRadiology, Medical College of Wisconsin, Milwaukee, Wisconsin.
Allison K LowmanRadiology, Medical College of Wisconsin, Milwaukee, Wisconsin.
Samuel A BobholzRadiology, Medical College of Wisconsin, Milwaukee, Wisconsin.
Fitzgerald KyeremeRadiology, Medical College of Wisconsin, Milwaukee, Wisconsin.
Aleksandra WiniarzDepartments of Biophysics, Medical College of Wisconsin, Milwaukee, Wisconsin.
Biprojit NathDepartments of Biophysics, Medical College of Wisconsin, Milwaukee, Wisconsin.
Kenneth A IczkowskiPathology, Medical College of Wisconsin, Milwaukee, Wisconsin.
Kenneth M JacobsohnUrology, Medical College of Wisconsin, Milwaukee, Wisconsin.
Peter S LaVioletteRadiology, Medical College of Wisconsin, Milwaukee, Wisconsin; Biomedical Engineering, Medical College of Wisconsin, Milwaukee, Wisconsin. Electronic address: plaviole@mcw.edu.

Funding

Prostate Cancer Radio-Pathomics for Differentiating Clinically Significant DiseaseR01CA249882 · NCI · MEDICAL COLLEGE OF WISCONSIN · PI LAVIOLETTE, PETER S · 2021 to 2025
$3.0M
Brain Cancer Radio-Pathomics for Predicting Heterogeneous CytologyR01CA218144 · NCI · MEDICAL COLLEGE OF WISCONSIN · PI LAVIOLETTE, PETER S · 2017 to 2021
$1.9M
Bayesian learning algorithms for identifying and classifying heterogeneity of cell types in variety of solid tumorsR21CA231892 · NCI · MEDICAL COLLEGE OF WISCONSIN · PI BANERJEE, ANJISHNU · 2018 to 2019
$362k
NCI NIH HHS R01 CA218144NCI NIH HHS R01 CA249882NCI NIH HHS R21 CA231892
6 · The paper itself

Abstract

Prostate cancer is the most commonly diagnosed cancer in men, accounting for 27% of the new male cancer diagnoses in 2022. If organ-confined, removal of the prostate through radical prostatectomy is considered curative; however, distant metastases may occur, resulting in a poor patient prognosis. This study sought to determine whether quantitative pathomic features of prostate cancer differ in patients who biochemically experience biological recurrence after surgery. Whole-mount prostate histology from 78 patients was analyzed for this study. In total, 614 slides were hematoxylin and eosin stained and digitized to produce whole slide images (WSI). Regions of differing Gleason patterns were digitally annotated by a genitourinary fellowship-trained pathologist, and high-resolution tiles were extracted from each annotated region of interest for further analysis. Individual glands within the prostate were identified using automated image processing algorithms, and histomorphometric features were calculated on a per-tile basis and across WSI and averaged by patients. Tiles were organized into cancer and benign tissues. Logistic regression models were fit to assess the predictive value of the calculated pathomic features across tile groups and WSI; additionally, models using clinical information were used for comparisons. Logistic regression classified each pathomic feature model at accuracies >80% with areas under the curve of 0.82, 0.76, 0.75, and 0.72 for all tiles, cancer only, noncancer only, and across WSI. This was comparable with standard clinical information, Gleason Grade Groups, and CAPRA score, which achieved similar accuracies but areas under the curve of 0.80, 0.77, and 0.70, respectively. This study demonstrates that the use of quantitative pathomic features calculated from digital histology of prostate cancer may provide clinicians with additional information beyond the traditional qualitative pathologist assessment. Further research is warranted to determine possible inclusion in treatment guidance.

Indexed as

Prostatic NeoplasmsHumansImage Processing, Computer-AssistedMaleNeoplasm GradingProstateProstatectomyannotationsdigital pathologyimage processingpathomic featuresprostate cancerwhole slide images

Identifiers

PMID37898290
PMCPMC10872376

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.