Prediction of survival from carcinoma of oesophagus and oesophago‐gastric junction following surgical resection using an artificial neural network

Mofidi, R.; Deans, Chris; Duff, Michael; Beaux, A. C. de; Brown, S. Paterson

doi:10.1016/j.ejso.2006.02.020

Cited by 24 publications

(17 citation statements)

References 37 publications

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“…On the basis of Agrawal study accurate prediction of survival in lung cancer using Ensamble classification model for 6, 9 month and 1-, 2-and 5-years was 73.61%, 74.45%, 76.80%, 85.45% and 91.35% (13). In Modifi study ANN precision in predicting 1-and 3-year survival of esophageal cancer was comparable to other investigations and was 88% and 91.5% respectively (11). As reviewing other previous studies showed, ANN has shown good results in lung, breast and esophageal cancer survival prediction.…”

Section: Resultssupporting

confidence: 60%

“…Results show that this system was significantly better than UICC and TNM staging system in predicting 1-and 3-years survival. Consequently results of this study shows that ANN is superior to UICC and TNM staging in predicting survival in esophageal carcinoma and could be a valuable tool in the management of esophageal cancer patients (11). In another article which aims to predict lung cancer survival with the use of Surveillance, Epidemiology and End Result (SEER) database.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Survival in Thyroid Cancer Using Data Mining Technique

Jajroudi

Baniasadi

Kamkar

et al. 2014

Technol Cancer Res Treat

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Cancer is the second leading cause of death after cardiovascular diseases in the world. Health professionals are seeking ways for suitable treatment and quality of care in these groups of patients. Survival prediction is important for both physicians and patients in order to choose the best way of management. Artificial Neural Network (ANN) is one of the most efficient data mining methods. This technique is able to evaluate the relationship between different variables spontaneously without any prevalent data. In our study ANN and Logistic Regression were used to predict survival in thyroid cancer and compare these results. SEER (Surveillance, Epidemiology and End Result) data were got from SEER site 1 . Effective features in thyroid cancer have been selected based on supervision by radiation oncologists and evidence. After data pruning 7706 samples were studied with 16 attributes. Multi Layer Prediction (MLP) was used as the chosen neural network and survival was predicted for 1-, 3-and 5-years. Accuracy, sensitivity and specificity were parameters to evaluate the model.

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Section: Resultssupporting

confidence: 60%

Section: Introductionmentioning

confidence: 99%

Prediction of Survival in Thyroid Cancer Using Data Mining Technique

Jajroudi

Baniasadi

Kamkar

et al. 2014

Technol Cancer Res Treat

View full text Add to dashboard Cite

show abstract

“…Most include pathological information gained from tumour resection and these data cannot be used at the time of diagnosis to inform management. Other prognostic studies have used sophisticated novel molecular techniques to determine prognosis, for example DNA microarray 42 , or involved the development of complex computer models, such as artificial neural networks, for which extensive clinical, investigative and pathological information is required to estimate prognosis 43 . A study in patients with inoperable oesophagogastric cancer devised an inflammation-based prognostic scoring system 36 .…”

Section: Discussionmentioning

confidence: 99%