Automatic detection and tracking of marker seeds implanted in prostate cancer patients using a deep learning algorithm

Abstract The widespread influence of social media impacts every aspect of life, including the healthcare sector. Although medics and health professionals are the final decision makers, the advice and recommendations obtained from fellow patients are significant. In this context, the present paper explores the topics of discussion posted by breast cancer patients and survivors on online forums. The study examines an online forum, Breastcancer.org, maps the discussion entries to several topics, and proposes a machine learning model based on a classification algorithm to characterize the topics. To explore the topics of breast cancer patients and survivors, approximately 1000 posts are selected and manually labeled with annotations. In contrast, millions of posts are available to build the labels. A semi-supervised learning technique is used to build the labels for the unlabeled data; hence, the large data are classified using a deep learning algorithm. The deep learning algorithm BiLSTM with BERT word embedding technique provided a better f1-score of 79.5%. This method is able to classify the following topics: medication reviews, clinician knowledge, various treatment options, seeking and providing support, diagnostic procedures, financial issues and implications for everyday life. What matters the most for the patients is coping with everyday living as well as seeking and providing emotional and informational support. The approach and findings show the potential of studying social media to provide insight into patients' experiences with cancer like critical health problems.

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Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer

npj Digital Medicine ◽

10.1038/s41746-019-0112-2 ◽

2019 ◽

Vol 2 (1) ◽

Cited By ~ 66

Author(s):

Kunal Nagpal ◽

Davis Foote ◽

Yun Liu ◽

Po-Hsuan Cameron Chen ◽

Ellery Wulczyn ◽

...

Keyword(s):

Prostate Cancer ◽

Deep Learning ◽

Learning Algorithm ◽

Deep Learning Algorithm ◽

Development And Validation

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Development and Validation of a Deep Learning Algorithm for Gleason Grading of Prostate Cancer From Biopsy Specimens

JAMA Oncology ◽

10.1001/jamaoncol.2020.2485 ◽

2020 ◽

Vol 6 (9) ◽

pp. 1372 ◽

Cited By ~ 3

Author(s):

Kunal Nagpal ◽

Davis Foote ◽

Fraser Tan ◽

Yun Liu ◽

Po-Hsuan Cameron Chen ◽

...

Keyword(s):

Prostate Cancer ◽

Deep Learning ◽

Learning Algorithm ◽

Gleason Grading ◽

Deep Learning Algorithm ◽

Biopsy Specimens ◽

Development And Validation

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A deep learning algorithm to predict coexisting metastatic disease using intraprostatic [F18]DCFPYL PSMA image alone in veterans with prostate cancer.

Journal of Clinical Oncology ◽

10.1200/jco.2020.38.6_suppl.25 ◽

2020 ◽

Vol 38 (6_suppl) ◽

pp. 25-25

Author(s):

Nicholas George Nickols ◽

Aseem Anand ◽

Karl Sjöstrand ◽

Lida Jafari ◽

John Ceccoli ◽

...

Keyword(s):

Prostate Cancer ◽

Deep Learning ◽

Metastatic Disease ◽

Primary Tumor ◽

Learning Algorithm ◽

Distant Metastases ◽

Dense Layer ◽

Conventional Imaging ◽

Clinicopathologic Features ◽

Deep Learning Algorithm

25 Background: [F18]DCFPyL (PyL) is a PSMA targeted imaging agent for prostate cancer. Independent of manual feature selection, a deep learning algorithm might offer additional insight into the disease biology. We explored the performance of a deep learning algorithm on PyL images of the primary tumor to predict co-existing distant metastases. Methods: 74 veterans with high risk primary prostate cancer tumors were imaged with both PyL PSMA PET/CT and conventional imaging (bone scan and CT or MRI of the abdomen/pelvis). 26% were confirmed with metastatic disease (M1) by conventional imaging. The PyL images of the primary tumor were analyzed with EXINI’s PyL-AI algorithm. Location of the prostate was defined on low dose CT via automatic segmentation using a deep convolutional network. The segmentations were used to map the PyL PET image of the prostate. The image based PyL-AI model was made up of a Conv3D layer of 4 kernels, a Conv3D layer of 8 kernels, a dense layer of 64 nodes followed by a final dense layer with 2 nodes. The model training was performed on the images using 5-fold cross validation with non-overlapping validation sets. The test predictions were compared with ground truth (M1); the area under ROC curve (AUC) was computed to determine the performance of the model in predicting the presence of distant metastases. A logistical regression model from baseline clinicopathologic features of the primary tumor (baseline PSA, biopsy gleason score, percent cores positive, T stage) was created as a comparator. Results: The logistical regression model using clinicopathologic features had an AUC of 0.71, while the PyL-AI model based on intra-prostatic PyL Images alone had an AUC of 0.81 for prediction of metastatic disease as defined by conventional imaging. Adding clinical parameters in the image based PyL-AI model incrementally increased the AUC to 0.82. Conclusions: The image based PyL-AI deep learning model demonstrates a higher predictive accuracy over the logistic model using classical clinicopathologic features. The study is hypothesis generating observation that needs prospective validation in an independent data set.

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Automatic Detection of Diabetic Retinopathy in Retinal Fundus Photographs Based on Deep Learning Algorithm

Translational Vision Science & Technology ◽

10.1167/tvst.8.6.4 ◽

2019 ◽

Vol 8 (6) ◽

pp. 4 ◽

Cited By ~ 17

Author(s):

Feng Li ◽

Zheng Liu ◽

Hua Chen ◽

Minshan Jiang ◽

Xuedian Zhang ◽

...

Keyword(s):

Diabetic Retinopathy ◽

Deep Learning ◽

Learning Algorithm ◽

Automatic Detection ◽

Deep Learning Algorithm ◽

Fundus Photographs ◽

Retinal Fundus

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Deep learning algorithm improves identification of men with low-risk prostate cancer using PSMA-targeted 99mTc-MIP-1404 SPECT/CT.

Journal of Clinical Oncology ◽

10.1200/jco.2019.37.15_suppl.e16572 ◽

2019 ◽

Vol 37 (15_suppl) ◽

pp. e16572-e16572

Author(s):

Alexa Meyer ◽

Nancy Stambler ◽

Karl Sjöstrand ◽

Jens Richter ◽

Mohamad Allaf ◽

...

Keyword(s):

Prostate Cancer ◽

Deep Learning ◽

Active Surveillance ◽

Predictive Value ◽

Learning Algorithm ◽

Clinical Stage ◽

Low Risk ◽

Phase Iii ◽

Psa Density ◽

Deep Learning Algorithm

e16572 Background: Previous work has shown that the degree of expression of prostate-specific membrane antigen (PSMA) correlates with prostate cancer (PCa) grade and stage. We evaluated the additive value of a deep learning algorithm (PSMA-AI) of a PSMA-targeted small molecule SPECT/CT imaging agent (99mTc-MIP-1404) to identify men with low risk PCa who are potential active surveillance candidates. Methods: A secondary analysis of a phase III trial (NCT02615067) of men with PCa who underwent 99mTc-MIP-1404 SPECT/CT was conducted. Patients with a biopsy Gleason score (GS) of ≤6, clinical stage ≤T2, and prostate specific antigen (PSA) < 10 ng/mL who underwent radical prostatectomy (RP) following SPECT/CT were included in the present analysis. SPECT/CT images were retrospectively analyzed by PSMA-AI, which was developed and locked prior to analysis. PSMA-AI calculated the uptake of 99mTc-MIP-1404 against the background reference (TBR). The automated TBR of 14 was used as a threshold for PSMA-AI calls of positive disease. Multivariable logistic regression analysis was used to develop a base model for identifying men with occult GS ≥7 PCa in the RP specimen. This model included PSA density, % positive biopsy cores, and clinical stage. The diagnostic performance of this model was then compared to a second model that incorporated PSMA-AI calls. Results: In total, 87 patients enrolled in the original trial contributed to the analysis. The base model indicated that PSA density and % positive cores were significantly associated with occult GS ≥7 PCa (p < 0.05), but clinical stage was not (p = 0.23). The predictive ability of the model resulted in an area under the curve (AUC) of 0.73. Upon adding PSMA-AI calls, the AUC increased to 0.77. PSMA-AI calls (p = 0.045), pre-surgery PSA density (0.019) and % positive core (p < 0.004) remained statistically significant. PSMA-AI calls increased the positive predictive value from 70% to 77% and the negative predictive value from 57% to 74%. Conclusions: The addition of PSMA-AI calls demonstrated a significant improvement over known predictors for identifying men with occult GS ≥7 PCa, who are inappropriate candidates for active surveillance. Clinical trial information: NCT02615067.

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