File Name: machine learning applications in cancer prognosis and prediction .zip
Universitatea Lucian Blaga din Sibiu. An early diagnosis of breast cancer offers treatment for it; therefore, several experiments are in development establishing approaches for the early detection of breast cancer.
Blood Adv ; 4 23 : — Machine learning ML is rapidly emerging in several fields of cancer research. ML algorithms can deal with vast amounts of medical data and provide a better understanding of malignant disease. Its ability to process information from different diagnostic modalities and functions to predict prognosis and suggest therapeutic strategies indicates that ML is a promising tool for the future management of hematologic malignancies; acute myeloid leukemia AML is a model disease of various recent studies. An integration of these ML techniques into various applications in AML management can assure fast and accurate diagnosis as well as precise risk stratification and optimal therapy. Nevertheless, these techniques come with various pitfalls and need a strict regulatory framework to ensure safe use of ML. This comprehensive review highlights and discusses recent advances in ML techniques in the management of AML as a model disease of hematologic neoplasms, enabling researchers and clinicians alike to critically evaluate this upcoming, potentially practice-changing technology.
Providing prognostic information at the time of cancer diagnosis has important implications for treatment and monitoring. Although cancer staging, histopathological assessment, molecular features, and clinical variables can provide useful prognostic insights, improving risk stratification remains an active research area. We used a weakly-supervised approach without pixel-level annotations, and tested three different survival loss functions. The DLS was developed using 9, slides from 3, cases and evaluated using 3, slides from 1, cases. In multivariable Cox regression analysis of the combined cohort including all 10 cancers, the DLS was significantly associated with disease specific survival hazard ratio of 1. In a per-cancer adjusted subanalysis, the DLS remained a significant predictor of survival in 5 of 10 cancer types. Compared to a baseline model including stage, age, and sex, the c-index of the model demonstrated an absolute 3.
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S urgical site infection SSI following neurosurgical operations is a burdensome complication in the field. Such complications can impact morbidity, mortality, and economics. The financial burden caused by craniotomy infections is often compounded by the direct costs incurred by prolonged hospitalization of the patient, diagnostic tests, treatment, and reoperation. Machine learning ML is used for outcome prediction in the neurosurgical field. Several ML algorithms have been developed using complex mathematical models that can learn from clinical data from, for example, neuro-oncology, neurovascular surgery, traumatic brain injury, and epilepsy. From literature reviews we find evidence that ML has been applied in predicting neurosurgical complications, particularly SSI.
Breast cancer accounts for the largest number of cancer cases all around the world. These numbers are particularly high in developing countries. In United States US , breast cancer disease is the most common diagnosed cancer in women. It is ranked as second cause of cancer death in women. Early detection is the key to reduce the mortality rates.
This capacity is especially appropriate to restorative applications, particularly those that rely upon complex proteomic and genomic estimations. Therefore, AI is much of the time utilized in malignant growth determination and discovery. All the more as of late AI has been applied to malignant growth guess and forecast. This last methodology is especially intriguing as it is a piece of a developing pattern towards customized, prescient medication. In amassing this audit we directed a wide study of the various kinds of AI strategies being utilized, the sorts of information being incorporated and the exhibition of these techniques in malignant growth expectation and anticipation.
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It is clear that the application of ML methods could improve the accuracy of cancer susceptibility, recurrence and survival prediction. Based on , the accuracy of cancer prediction outcome has significantly improved by 15%–20% the last years, with the application of ML techniques.Matilda V. 21.03.2021 at 20:31
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