Predicting Cervical Cancer Outcomes: Statistics, Images, and Machine Learning
Cervical cancer is a very common and severe disease in women worldwide. Accurate prediction of its clinical outcomes will help adjust or optimize the treatment of cervical cancer and benefit the patients. Statistical models, various types of medical images, and machine learning have been used for ou...
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| Published in | Frontiers in artificial intelligence Vol. 4; p. 627369 |
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| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
Frontiers Media S.A
07.06.2021
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| Subjects | |
| Online Access | Get full text |
| ISSN | 2624-8212 2624-8212 |
| DOI | 10.3389/frai.2021.627369 |
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| Abstract | Cervical cancer is a very common and severe disease in women worldwide. Accurate prediction of its clinical outcomes will help adjust or optimize the treatment of cervical cancer and benefit the patients. Statistical models, various types of medical images, and machine learning have been used for outcome prediction and obtained promising results. Compared to conventional statistical models, machine learning has demonstrated advantages in dealing with the complexity in large-scale data and discovering prognostic factors. It has great potential in clinical application and improving cervical cancer management. However, the limitations of prediction studies and prediction models including simplification, insufficient data, overfitting and lack of interpretability, indicate that more work is needed to make clinical outcome prediction more accurate, more reliable, and more practical for clinical use. |
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| AbstractList | Cervical cancer is a very common and severe disease in women worldwide. Accurate prediction of its clinical outcomes will help adjust or optimize the treatment of cervical cancer and benefit the patients. Statistical models, various types of medical images, and machine learning have been used for outcome prediction and obtained promising results. Compared to conventional statistical models, machine learning has demonstrated advantages in dealing with the complexity in large-scale data and discovering prognostic factors. It has great potential in clinical application and improving cervical cancer management. However, the limitations of prediction studies and prediction models including simplification, insufficient data, overfitting and lack of interpretability, indicate that more work is needed to make clinical outcome prediction more accurate, more reliable, and more practical for clinical use.Cervical cancer is a very common and severe disease in women worldwide. Accurate prediction of its clinical outcomes will help adjust or optimize the treatment of cervical cancer and benefit the patients. Statistical models, various types of medical images, and machine learning have been used for outcome prediction and obtained promising results. Compared to conventional statistical models, machine learning has demonstrated advantages in dealing with the complexity in large-scale data and discovering prognostic factors. It has great potential in clinical application and improving cervical cancer management. However, the limitations of prediction studies and prediction models including simplification, insufficient data, overfitting and lack of interpretability, indicate that more work is needed to make clinical outcome prediction more accurate, more reliable, and more practical for clinical use. Cervical cancer is a very common and severe disease in women worldwide. Accurate prediction of its clinical outcomes will help adjust or optimize the treatment of cervical cancer and benefit the patients. Statistical models, various types of medical images, and machine learning have been used for outcome prediction and obtained promising results. Compared to conventional statistical models, machine learning has demonstrated advantages in dealing with the complexity in large-scale data and discovering prognostic factors. It has great potential in clinical application and improving cervical cancer management. However, the limitations of prediction studies and prediction models including simplification, insufficient data, overfitting and lack of interpretability, indicate that more work is needed to make clinical outcome prediction more accurate, more reliable, and more practical for clinical use. |
| Author | Luo, Wei |
| AuthorAffiliation | Department of Radiation Medicine, University of Kentucky, Lexington , KY , United States |
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| Cites_doi | 10.1007/s00330-019-06265-x 10.1016/s0140-6736(97)02250-2 10.3322/caac.21590 10.1200/jco.2004.07.197 10.1038/s41598-018-30336-6 10.7150/jca.33945 10.1016/j.jclinepi.2019.02.004 10.1002/mp.13570 10.1088/1361-6560/aa8d09 10.1002/cncr.22974 10.1016/j.brachy.2019.04.004 10.1016/j.ajog.2018.12.030 10.1016/j.ajog.2017.08.012 10.1097/IGC.0b013e31822c2769 10.1007/s00259-017-3898-7 10.1111/igc.0b013e318197f185 10.1016/0360-3016(84)90231-1 10.1186/s12885-017-3806-3 10.1002/1097-0142(19900715)66:2<251::aid-cncr2820660210>3.0.co;2-e 10.1016/j.ijrobp.2015.07.2286 10.18632/oncotarget.21041 10.1002/ijc.31937 10.1186/s13014-018-1068-0 10.1016/s0167-8140(98)00143-1 10.1007/s101470200043 10.2214/ajr.102.1.147 10.1002/1097-0142(19901215)66:12<2451::aid-cncr2820661202>3.0.co;2-5 10.1259/bjro.20190021 |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 ObjectType-Review-3 content type line 23 Lanchun Lu, The Ohio State University, United States Edited by: Bertram Müller-Myhsok, Max Planck Institute of Psychiatry (MPI), Germany Reviewed by: Shivanand Sharanappa Gornale, Rani Channamma University, India This article was submitted to Medicine and Public Health, a section of the journal Frontiers in Artificial Intelligence |
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| Title | Predicting Cervical Cancer Outcomes: Statistics, Images, and Machine Learning |
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