scholarly journals Quantitative Analysis of Benign and Malignant Tumors in Histopathology: Predicting Prostate Cancer Grading Using SVM

2019 ◽  
Vol 9 (15) ◽  
pp. 2969 ◽  
Author(s):  
Bhattacharjee ◽  
Park ◽  
Kim ◽  
Prakash ◽  
Madusanka ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Image Features ◽  
Morphological Features ◽  
Support Vector ◽  
Epithelial Tissue ◽  
Gleason Grade ◽  
Svm Classification ◽  
Gleason Grading ◽  
Grade 5 ◽  
Grade 3

An adenocarcinoma is a type of malignant cancerous tissue that forms from a glandular structure in epithelial tissue. Analyzed stained microscopic biopsy images were used to perform image manipulation and extract significant features for support vector machine (SVM) classification, to predict the Gleason grading of prostate cancer (PCa) based on the morphological features of the cell nucleus and lumen. Histopathology biopsy tissue images were used and categorized into four Gleason grade groups, namely Grade 3, Grade 4, Grade 5, and benign. The first three grades are considered malignant. K-means and watershed algorithms were used for color-based segmentation and separation of overlapping cell nuclei, respectively. In total, 400 images, divided equally among the four groups, were collected for SVM classification. To classify the proposed morphological features, SVM classification based on binary learning was performed using linear and Gaussian classifiers. The prediction model yielded an accuracy of 88.7% for malignant vs. benign, 85.0% for Grade 3 vs. Grade 4, 5, and 92.5% for Grade 4 vs. Grade 5. The SVM, based on biopsy-derived image features, consistently and accurately classified the Gleason grading of prostate cancer. All results are comparatively better than those reported in the literature.

scholarly journals First-Stage Prostate Cancer Identification on Histopathological Images: Hand-Driven versus Automatic Learning

Entropy ◽  
2019 ◽  
Vol 21 (4) ◽  
pp. 356 ◽  
Author(s):  
Gabriel García ◽  
Adrián Colomer ◽  
Valery Naranjo
Keyword(s):  
Prostate Cancer ◽  
Contextual Information ◽  
Support Vector ◽  
Learning Approach ◽  
Gleason Grade ◽  
Gleason Grading ◽  
Histopathological Image ◽  
Histopathological Images ◽  
Grade 3 ◽  
First Time

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.

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

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.

Gleason grade 5 and the risk of death from prostate cancer following radiation with or without 6 months of conventional androgen deprivation therapy.

2015 ◽  
Vol 33 (15_suppl) ◽  
pp. e16099-e16099
Author(s):  
Laura Elizabeth Warren ◽  
Ming-Hui Chen ◽  
James William Denham ◽  
Allison Steigler ◽  
Andrew A. Renshaw ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Androgen Deprivation Therapy ◽  
Androgen Deprivation ◽  
Gleason Grade ◽  
Risk Of Death ◽  
Grade 5

Gleason grade 5 prostate cancer: sub-patterns and prognosis

Pathology ◽  
2021 ◽  
Vol 53 (1) ◽  
pp. 3-11 ◽  
Author(s):  
Chantal Atallah ◽  
Ants Toi ◽  
Theodorus H. van der Kwast
Keyword(s):  
Prostate Cancer ◽  
Gleason Grade ◽  
Grade 5

Ixabepilone (Epothilone B Analogue BMS-247550) Is Active in Chemotherapy-Naive Patients With Hormone-Refractory Prostate Cancer: A Southwest Oncology Group Trial S0111

2005 ◽  
Vol 23 (34) ◽  
pp. 8724-8729 ◽  
Author(s):  
Maha Hussain ◽  
Catherine M. Tangen ◽  
Primo N. Lara ◽  
Ulka N. Vaishampayan ◽  
Daniel P. Petrylak ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Performance Status ◽  
Specific Antigen ◽  
Progression Free Survival ◽  
Hematologic Toxicity ◽  
Hormone Refractory Prostate Cancer ◽  
Grade 5 ◽  
Psa Response ◽  
Hormone Refractory ◽  
Grade 3

Purpose The epothilones are a new class of tubulin-polymerizing agents with activity in taxane-sensitive and resistant tumor models. We evaluated ixabepilone (BMS-247550) in patients with metastatic hormone-refractory prostate cancer (HRPC). Methods Eligible patients had chemotherapy-naive metastatic HRPC, a Zubrod performance status of 0 to 2, and adequate organ function. All patients received BMS-247550 at 40 mg/m2 over 3 hours every 3 weeks. The primary end point was proportion of patients achieving a prostate-specific antigen (PSA) response. Results Forty-eight patients with metastatic HRPC were registered. Forty-two patients were eligible, with a median age of 73 years and a median PSA level of 111 ng/mL; 78% had bone-only or bone and soft tissue metastases, and 88% had objective radiologic disease progression at registration. Grade 3 and 4 adverse events (AEs) occurred in 16 and three patients, respectively. All grade 4 toxicities were neutropenia or leukopenia. The most frequent grade 3 AEs were neuropathy (eight patients), hematologic toxicity (seven patients), flu-like symptoms, and infection (five patients each). There were no grade 3/4 thrombocytopenia or grade 5 AEs. There were 14 confirmed PSA responses (33%; 95% CI, 20% to 50%); 72% of PSA responders had declines greater than 80%, and two patients achieved an undetectable PSA. The estimated median progression-free survival is 6 months (95% CI, 4 to 8 months), and the median survival is 18 months (95% CI, 13 to 24 months). Conclusion Ixabepilone has demonstrated activity in patients with chemotherapy-naive metastatic HRPC. Major toxicities were neutropenia and neuropathy. Further testing to define its activity relative to standard therapy is warranted.

scholarly journals Predicting Prostate Cancer Upgrading of Biopsy Gleason Grade Group at Radical Prostatectomy Using Machine Learning-assisted Decision-support Models 

2020 ◽  
Author(s):  
Hailang Liu ◽  
Kun Tang ◽  
Ejun Peng ◽  
Liang Wang ◽  
Ding Xia ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Machine Learning ◽  
Logistic Regression ◽  
Radical Prostatectomy ◽  
Predictive Value ◽  
Support Vector ◽  
Gleason Grade ◽  
Good Discrimination ◽  
Grade Group ◽  
Testing Dataset

Abstract Background: This study aimed to develop a machine learning (ML)-assisted model capable of accurately predicting the probability of biopsy Gleason grade group upgrading before making treatment decisions.Methods: We retrospectively collected data from prostate cancer (PCa) patients who underwent systematic biopsy and radical prostatectomy from January 2015 to December 2019 at Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology. The study cohort was divided into training and testing datasets in a 70:30 ratio for further analysis. Four ML-assisted models were developed from 16 clinical features using logistic regression (LR), logistic regression optimized by least absolute shrinkage and selection operator (Lasso) regularization (Lasso-LR), random forest (RF) and support vector machine (SVM). The area under the curve (AUC) was applied to determine the model with the highest discrimination. Calibration plots were used to investigate the extent of over- or underestimation of predicted probabilities relative to the observed probabilities in models. Results: In total, 530 PCa patients were included, with 371 patients in the training dataset and 159 patients in the testing dataset. The Lasso-LR model showed good discrimination with an AUC, accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of 0.776, 0.712, 0.679, 0.745, 0.730 and 0.695, respectively, followed by SVM (AUC 0.740, 95% confidence interval [CI]: 0.690–0.790), LR (AUC 0.725, 95% CI: 0.674–0.776) and RF (AUC 0.666, 95% CI: 0.618–0.714). Validation of the model showed that the Lasso-LR model had the best discriminative power (AUC 0.735, 95% CI: 0.656–0.813), followed by SVM (AUC 0.723, 95% CI: 0.644–0.802), LR (AUC 0.697, 95% CI: 0.615–0.778) and RF (AUC 0.607, 95% CI: 0.531–0.684) in the testing dataset. Both the Lasso-LR and SVM models were well-calibrated. Conclusion: The Lasso-LR model had good discrimination in the prediction of patients at high risk of harboring incorrect Gleason grade group assignment, and the use of this model may be greatly beneficial to urologists in treatment planning, patient selection, and the decision-making process for PCa patients.

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