Computer-aided gleason grading of prostate cancer histopathological images using texton forests

Analysis of histopathological image supposes the most reliable procedure to identify prostate cancer. Most studies try to develop computer aid-systems to face the Gleason grading problem. On the contrary, we delve into the discrimination between healthy and cancerous tissues in its earliest stage, only focusing on the information contained in the automatically segmented gland candidates. We propose a hand-driven learning approach, in which we perform an exhaustive hand-crafted feature extraction stage combining in a novel way descriptors of morphology, texture, fractals and contextual information of the candidates under study. Then, we carry out an in-depth statistical analysis to select the most relevant features that constitute the inputs to the optimised machine-learning classifiers. Additionally, we apply for the first time on prostate segmented glands, deep-learning algorithms modifying the popular VGG19 neural network. We fine-tuned the last convolutional block of the architecture to provide the model specific knowledge about the gland images. The hand-driven learning approach, using a nonlinear Support Vector Machine, reports a slight outperforming over the rest of experiments with a final multi-class accuracy of 0.876 ± 0.026 in the discrimination between false glands (artefacts), benign glands and Gleason grade 3 glands.

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Faculty Opinions recommendation of Computer-aided ultrasonography (HistoScanning): a novel technology for locating and characterizing prostate cancer.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.1127819.584905 ◽

2008 ◽

Author(s):

Thomas Polascik ◽

Matvey Tsivian

Keyword(s):

Prostate Cancer ◽

Novel Technology ◽

Computer Aided

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Faculty Opinions recommendation of ERbeta impedes prostate cancer EMT by destabilizing HIF-1alpha and inhibiting VEGF-mediated snail nuclear localization: implications for Gleason grading.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.2916957.2746056 ◽

2010 ◽

Author(s):

Kathleen Kelly

Keyword(s):

Prostate Cancer ◽

Nuclear Localization ◽

Gleason Grading

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Interchangeability of light and virtual microscopy for histopathological evaluation of prostate cancer

Scientific Reports ◽

10.1038/s41598-021-82911-z ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Renata Zelic ◽

Francesca Giunchi ◽

Luca Lianas ◽

Cecilia Mascia ◽

Gianluigi Zanetti ◽

...

Keyword(s):

Prostate Cancer ◽

Virtual Microscopy ◽

Observer Agreement ◽

Gleason Grade ◽

Gleason Grading ◽

Gleason Pattern ◽

Inter Observer Variability ◽

Histopathological Evaluation ◽

Good Repeatability ◽

Repeatability And Reproducibility

AbstractVirtual microscopy (VM) holds promise to reduce subjectivity as well as intra- and inter-observer variability for the histopathological evaluation of prostate cancer. We evaluated (i) the repeatability (intra-observer agreement) and reproducibility (inter-observer agreement) of the 2014 Gleason grading system and other selected features using standard light microscopy (LM) and an internally developed VM system, and (ii) the interchangeability of LM and VM. Two uro-pathologists reviewed 413 cores from 60 Swedish men diagnosed with non-metastatic prostate cancer 1998–2014. Reviewer 1 performed two reviews using both LM and VM. Reviewer 2 performed one review using both methods. The intra- and inter-observer agreement within and between LM and VM were assessed using Cohen’s kappa and Bland and Altman’s limits of agreement. We found good repeatability and reproducibility for both LM and VM, as well as interchangeability between LM and VM, for primary and secondary Gleason pattern, Gleason Grade Groups, poorly formed glands, cribriform pattern and comedonecrosis but not for the percentage of Gleason pattern 4. Our findings confirm the non-inferiority of VM compared to LM. The repeatability and reproducibility of percentage of Gleason pattern 4 was poor regardless of method used warranting further investigation and improvement before it is used in clinical practice.

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Computer-Aided Diagnosis Improves the Detection of Clinically Significant Prostate Cancer on Multiparametric-MRI: A Multi-Observer Performance Study Involving Inexperienced Readers

Diagnostics ◽

10.3390/diagnostics11060973 ◽

2021 ◽

Vol 11 (6) ◽

pp. 973

Author(s):

Valentina Giannini ◽

Simone Mazzetti ◽

Giovanni Cappello ◽

Valeria Maria Doronzio ◽

Lorenzo Vassallo ◽

...

Keyword(s):

Prostate Cancer ◽

Computer Aided Diagnosis ◽

Multiparametric Mri ◽

Performance Study ◽

Predictive Values ◽

Cad System ◽

Computer Aided ◽

Clinically Significant ◽

Sensitivity Specificity ◽

Aided Diagnosis

Recently, Computer Aided Diagnosis (CAD) systems have been proposed to help radiologists in detecting and characterizing Prostate Cancer (PCa). However, few studies evaluated the performances of these systems in a clinical setting, especially when used by non-experienced readers. The main aim of this study is to assess the diagnostic performance of non-experienced readers when reporting assisted by the likelihood map generated by a CAD system, and to compare the results with the unassisted interpretation. Three resident radiologists were asked to review multiparametric-MRI of patients with and without PCa, both unassisted and assisted by a CAD system. In both reading sessions, residents recorded all positive cases, and sensitivity, specificity, negative and positive predictive values were computed and compared. The dataset comprised 90 patients (45 with at least one clinically significant biopsy-confirmed PCa). Sensitivity significantly increased in the CAD assisted mode for patients with at least one clinically significant lesion (GS > 6) (68.7% vs. 78.1%, p = 0.018). Overall specificity was not statistically different between unassisted and assisted sessions (94.8% vs. 89.6, p = 0.072). The use of the CAD system significantly increases the per-patient sensitivity of inexperienced readers in the detection of clinically significant PCa, without negatively affecting specificity, while significantly reducing overall reporting time.

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