Best Paper Selection

doi:10.1055/s-0038-1667096

Best Paper Selection

Yearbook of Medical Informatics ◽

10.1055/s-0038-1667096 ◽

2018 ◽

Vol 27 (01) ◽

pp. 226-226

Keyword(s):

Prostate Cancer ◽

Knowledge Base ◽

Prediction Models ◽

Precision Oncology ◽

Castration Resistant Prostate Cancer ◽

Short Term ◽

Castration Resistant

Chakravarty D, Gao J, Phillips SM, Kundra R, Zhang H, Wang J, Rudolph JE, Yaeger R, Soumerai T, Nissan MH, Chang MT, Chandarlapaty S, Traina TA, Paik PK, Ho AL, Hantash FM, Grupe A, Baxi SS, Callahan MK, Snyder A, Chi P, Danila D, Gounder M, Harding JJ, Hellmann MD, Iyer G, Janjigian Y, Kaley T, Levine DA, Lowery M, Omuro A, Postow MA, Rathkopf D, Shoushtari AN, Shukla N, Voss M, Paraiso E, Zehir A, Berger MF, Taylor BS, Saltz LB, Riely GJ, Ladanyi M, Hyman DM, Baselga J, Sabbatini P, Solit DB, Schultz N. OncoKB: a precision oncology knowledge base. JCO Precis Oncol 2017 Jul;2017 https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/28890946/ Newton Y, Novak AM, Swatloski T, McColl DC, Chopra S, Graim K, Weinstein AS, Baertsch R, Salama SR, Ellrott K, Chopra M, Goldstein TC, Haussler D, Morozova O, Stuart JM. TumorMap: exploring the molecular similarities of cancer samples in an interactive portal. Cancer Res 2017 Nov 1;77(21):e111-e114 https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/29092953/ Seyednasrollah F, Koestler DC, Wang T, Piccolo SR, Vega R, Greiner R, Fuchs C, Gofer E, Kumar L, Wolfinger RD, Winner KK, Bare C, Neto EC, Yu T, Shen L, Abdallah K, Norman T, Stolovitzky G, Soule HR, Sweeney CJ, Ryan CJ, Scher HI, Sartor O, Elo LL, Zhou FL, Guinney J, Costello JC, and Prostate Cancer DREAM Challenge Community. A DREAM challenge to build prediction models for short-term discontinuation of docetaxel in metastatic castration-resistant prostate cancer. JCO Clin Cancer Inform 2017 Aug 4;(1):1-15 http://ascopubs.org/doi/abs/10.1200/CCI.17.00018

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Castration-Resistant Prostate Cancer Outcome Prediction Using Phased Long Short-Term Memory with Irregularly Sampled Serial Data

Applied Sciences ◽

10.3390/app10062000 ◽

2020 ◽

Vol 10 (6) ◽

pp. 2000 ◽

Cited By ~ 1

Author(s):

Jihwan Park ◽

Mi Jung Rho ◽

Hyong Woo Moon ◽

Ji Youl Lee

Keyword(s):

Prostate Cancer ◽

Decision Making ◽

Outcome Prediction ◽

Short Term Memory ◽

Castration Resistant Prostate Cancer ◽

Short Term ◽

Term Memory ◽

Castration Resistant ◽

Serial Data ◽

Long Short Term Memory

It is particularly desirable to predict castration-resistant prostate cancer (CRPC) in prostate cancer (PCa) patients, and this study aims to predict patients’ likely outcomes to support physicians’ decision-making. Serial data is collected from 1592 PCa patients, and a phased long short-term memory (phased-LSTM) model with a special module called a “time-gate” is used to process the irregularly sampled data sets. A synthetic minority oversampling technique is used to overcome the data imbalance between two patient groups: those with and without CRPC treatment. The phased-LSTM model is able to predict the CRPC outcome with an accuracy of 88.6% (precision-recall: 91.6%) using 120 days of data or 94.8% (precision-recall: 96.9%) using 360 days of data. The validation loss converged slowly with 120 days of data and quickly with 360 days of data. In both cases, the prediction model takes four epochs to build. The overall CPRC outcome prediction model using irregularly sampled serial medical data is accurate and can be used to support physicians’ decision-making, which saves time compared to cumbersome serial data reviews. This study can be extended to realize clinically meaningful prediction models.

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Emerging Subtypes and New Treatments for Castration-Resistant Prostate Cancer

American Society of Clinical Oncology Educational Book ◽

10.1200/edbk_100025 ◽

2020 ◽

pp. e319-e332

Author(s):

Benedito A. Carneiro ◽

Tamara L. Lotan ◽

Andre de Souza ◽

Rahul Aggarwal

Keyword(s):

Prostate Cancer ◽

Parp Inhibitors ◽

Phase Iii ◽

Precision Oncology ◽

Castration Resistant Prostate Cancer ◽

Castration Resistant ◽

Treatment Paradigm ◽

Genomic Characterization ◽

Sequential Treatments ◽

New Treatments

Genomic characterization of metastatic castration-resistant prostate cancer (mCRPC) has been remodeling the treatment landscape of this disease in the past decade. The emergence of molecularly defined subsets of mCRPC is altering the treatment paradigm from therapeutics with nonspecific activity across the spectrum, including androgen receptor (AR)-directed treatments, docetaxel, and cabazitaxel, to targeted approaches directed at molecular subsets of disease. The meaningful benefit of PARP inhibitors in mCRPC carrying mutations in DNA repair genes demonstrated in a phase III trial epitomizes this transition in the treatment paradigm of mCRPC and brings new challenges related to how to sequence and integrate the targeted therapies on top of the treatments with broad activity in all mCRPC. To enable and sustain the advance of precision oncology in the management of mCRPC, genomic characterization is required, including somatic and germline testing, for all patients with the ultimate goal of longitudinal molecular profiling guiding treatment decisions and sequential treatments of this lethal disease. This article reviews the emerging molecular subtypes of mCRPC that are driving the evolution of mCRPC treatment.

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Surface-enhanced raman spectroscopy of serum predicts sensitivity to docetaxel-based chemotherapy in patients with metastatic castration-resistant prostate cancer

Journal of Innovative Optical Health Sciences ◽

10.1142/s1793545821410066 ◽

2021 ◽

pp. 2141006

Author(s):

Jianian Hu ◽

Xiaoguang Shao ◽

Chenfei Chi ◽

Yinjie Zhu ◽

Zhixiang Xin ◽

...

Keyword(s):

Prostate Cancer ◽

Raman Spectroscopy ◽

Cross Validation ◽

Prediction Models ◽

Surface Enhanced Raman Spectroscopy ◽

Castration Resistant Prostate Cancer ◽

Castration Resistant ◽

Surface Enhanced ◽

Surface Enhanced Raman ◽

Docetaxel Chemotherapy

Docetaxel-based chemotherapy, as the first-line treatment for metastatic castration-resistant prostate cancer (mCRPC), has succeeded in helping quite a number of patients to improve quality of life and prolong survival time. However, almost half of mCRPC patients are not sensitive to docetaxel chemotherapy initially. This study aimed to establish models to predict sensitivity to docetaxel chemotherapy in patients with mCRPC by using serum surface-enhanced Raman spectroscopy (SERS). A total of 32 mCPRC patients who underwent docetaxel chemotherapy at our center from July 2016 to March 2018 were included in this study. Patients were dichotomized in prostate-specific antigen (PSA) response group ([Formula: see text]) versus PSA failure group ([Formula: see text]) according to the response to docetaxel. 64 matched spectra from 32 mCRPC patients were obtained by using SERS of serum at baseline ([Formula: see text]0) and after 1 cycle of docetaxel chemotherapy ([Formula: see text]1). Comparing Raman peaks of serum samples at baseline ([Formula: see text]0) between two groups, significant differences revealed at the peaks of 638, 810, 890 ([Formula: see text]) and 1136[Formula: see text]cm[Formula: see text] ([Formula: see text]). The prediction models of peak 1363[Formula: see text]cm[Formula: see text] and principal component analysis and linear discriminant analysis (PCA–LDA) based on Raman data were established, respectively. The sensitivity and specificity of the prediction models were 71%, 80% and 69%, 78% through the way of leave-one-out cross-validation. According to the results of five-cross-validation, the PCA–LDA model revealed an accuracy of 0.73 and AUC of 0.83.

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Short-term outcome of enzalutamide in Japanese patients with castration-resistant prostate cancer

Annals of Oncology ◽

10.1093/annonc/mdv472.178 ◽

2015 ◽

Vol 26 ◽

pp. vii145

Author(s):

Satoshi Tamada ◽

Taro Iguchi ◽

Hirokazu Tachibana ◽

Tatsuya Nakatani

Keyword(s):

Prostate Cancer ◽

Japanese Patients ◽

Castration Resistant Prostate Cancer ◽

Short Term ◽

Term Outcome ◽

Castration Resistant ◽

Short Term Outcome

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A DREAM Challenge to Build Prediction Models for Short-Term Discontinuation of Docetaxel in Metastatic Castration-Resistant Prostate Cancer

JCO Clinical Cancer Informatics ◽

10.1200/cci.17.00018 ◽

2017 ◽

pp. 1-15 ◽

Cited By ~ 3

Author(s):

Fatemeh Seyednasrollah ◽

Devin C. Koestler ◽

Tao Wang ◽

Stephen R. Piccolo ◽

Roberto Vega ◽

...

Keyword(s):

Prostate Cancer ◽

Clinical Trial ◽

Clinical Trials ◽

High Risk ◽

Adverse Events ◽

Clinical Features ◽

Prediction Models ◽

Castration Resistant Prostate Cancer ◽

Early Discontinuation ◽

Castration Resistant

Purpose Docetaxel has a demonstrated survival benefit for patients with metastatic castration-resistant prostate cancer (mCRPC); however, 10% to 20% of patients discontinue docetaxel prematurely because of toxicity-induced adverse events, and the management of risk factors for toxicity remains a challenge. Patients and Methods The comparator arms of four phase III clinical trials in first-line mCRPC were collected, annotated, and compiled, with a total of 2,070 patients. Early discontinuation was defined as treatment stoppage within 3 months as a result of adverse treatment effects; 10% of patients discontinued treatment. We designed an open-data, crowd-sourced DREAM Challenge for developing models with which to predict early discontinuation of docetaxel treatment. Clinical features for all four trials and outcomes for three of the four trials were made publicly available, with the outcomes of the fourth trial held back for unbiased model evaluation. Challenge participants from around the world trained models and submitted their predictions. Area under the precision-recall curve was the primary metric used for performance assessment. Results In total, 34 separate teams submitted predictions. Seven models with statistically similar area under precision-recall curves (Bayes factor ≤ 3) outperformed all other models. A postchallenge analysis of risk prediction using these seven models revealed three patient subgroups: high risk, low risk, or discordant risk. Early discontinuation events were two times higher in the high-risk subgroup compared with the low-risk subgroup. Simulation studies demonstrated that use of patient discontinuation prediction models could reduce patient enrollment in clinical trials without the loss of statistical power. Conclusion This work represents a successful collaboration between 34 international teams that leveraged open clinical trial data. Our results demonstrate that routinely collected clinical features can be used to identify patients with mCRPC who are likely to discontinue treatment because of adverse events and establishes a robust benchmark with implications for clinical trial design.

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