Time-range based sequential mining for survival prediction in prostate cancer

Kaur, Ishleen; Doja, M. N.; Ahmad, Tanvir

doi:10.1016/j.jbi.2020.103550

Cited by 16 publications

(6 citation statements)

References 25 publications

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“…Contrary to the previous study using six months of time intervals for prostate cancer [ 33 ], we have evaluated different time intervals in our study to assess the appropriate time interval for advanced ovarian cancer patients. Thus, time intervals may vary according to cancer type as medications and cancer management differ for each cancer type.…”

Section: Results Analysis and Discussionmentioning

confidence: 99%

“…The time intervals chosen belonged to the 6 months range, i.e., <=6, 7–12, 13–18, till 31–36. The selection of time intervals was intuitive for 3-year survival and based on the previous literature [ 33 ]. Yet, when the data was analyzed, it was observed that most of the patients received their next treatments within 6–8 months of the previous treatment.…”

Section: Methodsmentioning

confidence: 99%

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An Integrated Approach for Cancer Survival Prediction Using Data Mining Techniques

Kaur

Doja

Ahmad

et al. 2021

Computational Intelligence and Neuroscience

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Ovarian cancer is the third most common gynecologic cancers worldwide. Advanced ovarian cancer patients bear a significant mortality rate. Survival estimation is essential for clinicians and patients to understand better and tolerate future outcomes. The present study intends to investigate different survival predictors available for cancer prognosis using data mining techniques. Dataset of 140 advanced ovarian cancer patients containing data from different data profiles (clinical, treatment, and overall life quality) has been collected and used to foresee cancer patients’ survival. Attributes from each data profile have been processed accordingly. Clinical data has been prepared corresponding to missing values and outliers. Treatment data including varying time periods were created using sequence mining techniques to identify the treatments given to the patients. And lastly, different comorbidities were combined into a single factor by computing Charlson Comorbidity Index for each patient. After appropriate preprocessing, the integrated dataset is classified using appropriate machine learning algorithms. The proposed integrated model approach gave the highest accuracy of 76.4% using ensemble technique with sequential pattern mining including time intervals of 2 months between treatments. Thus, the treatment sequences and, most importantly, life quality attributes significantly contribute to the survival prediction of cancer patients.

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Section: Results Analysis and Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

An Integrated Approach for Cancer Survival Prediction Using Data Mining Techniques

Kaur

Doja

Ahmad

et al. 2021

Computational Intelligence and Neuroscience

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“…[8] It has been previously recognized that data mining techniques perform better than conventional statistical methods for discriminating survival outcomes in metastatic prostate cancer. [9] This study applies this knowledge to analyze different treatment sequences in detail,…”

Section: Introductionmentioning

confidence: 99%

Comparative analysis of real-world data of frequent treatment sequences in metastatic prostate cancer

et al. 2023

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Background: The incidence of prostate cancer is increasing worldwide. A significant proportion of patients develop metastatic disease and are initially prescribed androgen deprivation therapy (ADT). However, subsequent sequences of treatments in real-world settings that may improve overall survival remain an area of active investigation. Materials and methods: Data were collected from 384 patients presenting with de novo metastatic prostate cancer from 2011 to 2015 at a tertiary cancer center. Patients were categorized into surviving (n = 232) and deceased (n = 152) groups at the end of 3 years. Modified sequence pattern mining techniques (Generalized Sequential Pattern Mining and Sequential Pattern Discovery using Equivalence Classes) were applied to determine the exact order of the most frequent sets of treatments in each group. Results: Degarelix, as the initial form of ADT, was uniquely in the surviving group. The sequence of ADT followed by abiraterone and docetaxel was uniquely associated with a higher 3-year overall survival. Orchiectomy followed by fosfestrol was found to have a unique niche among surviving patients with a long duration of response to the initial ADT. Patients who received chemotherapy followed by radiotherapy and those who received radiotherapy followed by chemotherapy were found more frequently in the deceased group. Conclusions: We identified unique treatment sequences among surviving and deceased patients at the end of 3 years. Degarelix should be the preferred form of ADT. Patients who received ADT followed by abiraterone and chemotherapy showed better results. Patients requiring palliative radiation and chemotherapy in any sequence were significantly more frequent in the deceased group, identifying the need to offer such patients the most efficacious agents and to target them in clinical trial design.

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“…Although surgical techniques and medical therapies (e.g. chemotherapy, radiotherapy, and immunotherapy) have been rapidly developed in the last 10 years, the 5-year survival rate of patients with urinary system cancers remains dismal [8,9]. Therefore, useful new biomarkers to diagnose, evaluatee and even the prognosis of urinary system cancers should be identified in clinical practice.…”

Section: Introductionmentioning

confidence: 99%

Systemic immune-inflammation index is a promising non-invasive biomarker for predicting the survival of urinary system cancers: a systematic review and meta-analysis

Chen

et al. 2021

Annals of Medicine

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Objective: Systemic immune-inflammation index (SII) has been reported in numerous studies to effectively predict the survival outcomes of urinary system cancers; however no agreement has been reached. This meta-analysis aimed to explore the prognostic significance of pre-treatment SII in tumours of the urinary system. Methods: Relevant published articles were selected from Web of Science, PubMed, Embase, and the Cochrane Library up to 30 August 2020. The hazard ratios (HRs) with 95% confidence intervals (CIs) were computed to estimate the associations of pre-treatment SII with overall survival (OS), progression-free survival (PFS), cancer-specific survival (CSS) in urinary system cancers. Results: 13 papers were included in our meta-analysis. From the combined data, we found that a high pre-treatment SII indicated a markedly worse OS (

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Time-range based sequential mining for survival prediction in prostate cancer

Cited by 16 publications

References 25 publications

An Integrated Approach for Cancer Survival Prediction Using Data Mining Techniques

An Integrated Approach for Cancer Survival Prediction Using Data Mining Techniques

Comparative analysis of real-world data of frequent treatment sequences in metastatic prostate cancer

Systemic immune-inflammation index is a promising non-invasive biomarker for predicting the survival of urinary system cancers: a systematic review and meta-analysis

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