scholarly journals Radiomics-Based Outcome Prediction for Pancreatic Cancer Following Stereotactic Body Radiotherapy

Cancers ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 1051
Author(s):  
Elsa Parr ◽  
Qian Du ◽  
Chi Zhang ◽  
Chi Lin ◽  
Ahsan Kamal ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Overall Survival ◽  
Stereotactic Body Radiotherapy ◽  
Outcome Prediction ◽  
Prediction Models ◽  
Pancreatic Tumors ◽  
Survival Prediction ◽  
Clinical Prediction ◽  
Clinical Prediction Models ◽  
Clinical Model

(1) Background: Radiomics use high-throughput mining of medical imaging data to extract unique information and predict tumor behavior. Currently available clinical prediction models poorly predict treatment outcomes in pancreatic adenocarcinoma. Therefore, we used radiomic features of primary pancreatic tumors to develop outcome prediction models and compared them to traditional clinical models. (2) Methods: We extracted and analyzed radiomic data from pre-radiation contrast-enhanced CTs of 74 pancreatic cancer patients undergoing stereotactic body radiotherapy. A panel of over 800 radiomic features was screened to create overall survival and local-regional recurrence prediction models, which were compared to clinical prediction models and models combining radiomic and clinical information. (3) Results: A 6-feature radiomic signature was identified that achieved better overall survival prediction performance than the clinical model (mean concordance index: 0.66 vs. 0.54 on resampled cross-validation test sets), and the combined model improved the performance slightly further to 0.68. Similarly, a 7-feature radiomic signature better predicted recurrence than the clinical model (mean AUC of 0.78 vs. 0.66). (4) Conclusion: Overall survival and recurrence can be better predicted with models based on radiomic features than with those based on clinical features for pancreatic cancer.

A population-based validation of a clinical age-based prognostic tool to predict survival in melanoma patients (Preprint)

2021 ◽  
Author(s):  
Yu-Jen Wang ◽  
Mingchih Chen ◽  
Yen Chun Huang ◽  
Tian-Shyug Lee
Keyword(s):  
Machine Learning ◽  
Overall Survival ◽  
Prognostic Factors ◽  
Outcome Prediction ◽  
Prediction Models ◽  
Population Based ◽  
Survival Prediction ◽  
Prediction Tool ◽  
Old Patients ◽  
Melanoma Patients

BACKGROUND Melanoma is the most serious form of skin cancer, and the treatment can be challenging if the disease progresses to the metastatic stage. Depth of invasion is a good prognostic factor for predicting outcome. However, no good outcome prediction system that combines the staging system with other chronic systemic diseases is available to date. We investigated melanoma-related data from a population-based database and developed an outcome prediction tool for melanoma patients via machine learning. OBJECTIVE Build up a prediction tool for melanoma patients METHODS The clinical data of patients with melanoma were extracted from Taiwan’s National Health Insurance Research Database between 2008 and 2015 and were analysed in this study. Clinical data including demographic, pathologic, staging, and treatment data from melanoma patients over 18 years old were abstracted and collected. Prognostic factors were analyzed. Logistic regression (LR), random forest (RF) modelling, and multivariate adaptive regression splines (MARS) were applied to calculate predicted overall survival (OS). A 5-fold cross-validation method was applied. Two age groups (≥64 years old as the older age group and <64 years old as the general population group) with different prognostic factors were identified, and prognostic models for survival outcomes were built. RESULTS A total of 3481 patients were enrolled in our study. The 1-, 3-, and 5-year overall survival rates were 92.2%, 80.1%, and 70.3%, respectively. The Cox proportional hazard model showed that older age, male sex, higher grade, higher clinical stage, larger tumour size, positive surgical margins, no surgical intervention, and a higher Charlson comorbidity index (CCI) were associated with higher hazard ratios. LR, RF, and MARS techniques were used to validate the overall survival without tracking time, the accuracy of the MARS model for the <64-year-old patients and ≥64-year-old patients was 90.4% and 80.7%, respectively, with 3-, and 5-year the accuracy of prediction models are 94% and 89.6%. CONCLUSIONS Machine learning techniques offer excellent survival prediction in melanoma patients. Age-based survival prediction models may be applied for better clinical decision making. CLINICALTRIAL N/A

scholarly journals Systematic review of clinical prediction models for survival after surgery for resectable pancreatic cancer

HPB ◽  
2020 ◽  
Vol 22 ◽  
pp. S257-S258
Author(s):  
M. Strijker ◽  
J. Chen ◽  
T. Mungroop ◽  
N. Jamieson ◽  
C. Van Eijck ◽  
...  
Keyword(s):  
Systematic Review ◽  
Pancreatic Cancer ◽  
Prediction Models ◽  
Clinical Prediction ◽  
Resectable Pancreatic Cancer ◽  
Clinical Prediction Models

scholarly journals Systematic review of clinical prediction models for survival after surgery for resectable pancreatic cancer

2019 ◽  
Vol 106 (4) ◽  
pp. 342-354 ◽  
Author(s):  
M. Strijker ◽  
J. W. Chen ◽  
T. H. Mungroop ◽  
N. B. Jamieson ◽  
C. H. van Eijck ◽  
...  
Keyword(s):  
Systematic Review ◽  
Pancreatic Cancer ◽  
Prediction Models ◽  
Clinical Prediction ◽  
Resectable Pancreatic Cancer ◽  
Clinical Prediction Models

scholarly journals Association of chemotherapy with survival in stage II colon cancer patients who received radical surgery: a retrospective cohort study

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Zhihao Lv ◽  
Yuqi Liang ◽  
Huaxi Liu ◽  
Delong Mo
Keyword(s):  
Colon Cancer ◽  
Overall Survival ◽  
Cancer Patients ◽  
Prediction Models ◽  
Radical Surgery ◽  
Survival Rates ◽  
Stage Ii ◽  
Survival Prediction ◽  
Stage Ii Colon Cancer ◽  
Overall Survival Rates

Abstract Background It remains controversial whether patients with Stage II colon cancer would benefit from chemotherapy after radical surgery. This study aims to assess the real effectiveness of chemotherapy in patients with stage II colon cancer undergoing radical surgery and to construct survival prediction models to predict the survival benefits of chemotherapy. Methods Data for stage II colon cancer patients with radical surgery were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database. Propensity score matching (1:1) was performed according to receive or not receive chemotherapy. Competitive risk regression models were used to assess colon cancer cause-specific death (CSD) and non-colon cancer cause-specific death (NCSD). Survival prediction nomograms were constructed to predict overall survival (OS) and colon cancer cause-specific survival (CSS). The predictive abilities of the constructed models were evaluated by the concordance indexes (C-indexes) and calibration curves. Results A total of 25,110 patients were identified, 21.7% received chemotherapy, and 78.3% were without chemotherapy. A total of 10,916 patients were extracted after propensity score matching. The estimated 3-year overall survival rates of chemotherapy were 0.7% higher than non- chemotherapy. The estimated 5-year and 10-year overall survival rates of non-chemotherapy were 1.3 and 2.1% higher than chemotherapy, respectively. Survival prediction models showed good discrimination (the C-indexes between 0.582 and 0.757) and excellent calibration. Conclusions Chemotherapy improves the short-term (43 months) survival benefit of stage II colon cancer patients who received radical surgery. Survival prediction models can be used to predict OS and CSS of patients receiving chemotherapy as well as OS and CSS of patients not receiving chemotherapy and to make individualized treatment recommendations for stage II colon cancer patients who received radical surgery.

Reviewing the use and quality of machine learning in developing clinical prediction models for cardiovascular disease

2021 ◽  
pp. postgradmedj-2020-139352
Author(s):  
Simon Allan ◽  
Raphael Olaiya ◽  
Rasan Burhan
Keyword(s):  
Machine Learning ◽  
Cardiovascular Disease ◽  
Prediction Models ◽  
Bone Fractures ◽  
Statin Treatment ◽  
Computer Algorithms ◽  
Clinical Prediction ◽  
Clinical Prediction Models ◽  
Heart Attacks

Cardiovascular disease (CVD) is one of the leading causes of death across the world. CVD can lead to angina, heart attacks, heart failure, strokes, and eventually, death; among many other serious conditions. The early intervention with those at a higher risk of developing CVD, typically with statin treatment, leads to better health outcomes. For this reason, clinical prediction models (CPMs) have been developed to identify those at a high risk of developing CVD so that treatment can begin at an earlier stage. Currently, CPMs are built around statistical analysis of factors linked to developing CVD, such as body mass index and family history. The emerging field of machine learning (ML) in healthcare, using computer algorithms that learn from a dataset without explicit programming, has the potential to outperform the CPMs available today. ML has already shown exciting progress in the detection of skin malignancies, bone fractures and many other medical conditions. In this review, we will analyse and explain the CPMs currently in use with comparisons to their developing ML counterparts. We have found that although the newest non-ML CPMs are effective, ML-based approaches consistently outperform them. However, improvements to the literature need to be made before ML should be implemented over current CPMs.

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