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Lung Cancer Dataset Lung cancer dataset without attribute definitions 56 features are given for each case 32 Text Classification 1992 [270] [271] Z. Hong et al. Arrhythmia Dataset Data for a group of patients, of which some have cardiac arrhythmia. 276 features for each instance. 452 Text Classification 1998 [272] [273] H. Altay et al.
They also used machine learning classifiers to predict the tissue of origin. Overall, the method can be used to identify if a patient has cancer. Even though the method does not specifically classify the cancer types during prediction, it is used for the detection of different cancers. [43]
Aerts et al. (2014) [21] performed the first large-scale radiomic study that included three lung and two head-and-neck cancer cohorts, consisting of over 1000 patients. They assessed the prognostic values of over 400 textural and shape- and intensity-based features extracted from the computed tomography (CT) images acquired before any treatment.
The composition of the extracted molecules can then be used to predict if the tissue sample analyzed contains cancerous cells using machine learning algorithms and statistical models. In early-stage clinical research, the MasSpec Pen system was able to distinguish various cancer tissues, including thyroid, breast, lung, and ovarian tumor ...
Collaborating with a team of researchers, he conducted a radiomic analysis of 1,019 patients with lung or head-and-neck cancer using computed tomography (CT) imaging data, identifying prognostic radiomic features that showed significant power in independent datasets of the cancer patients. [21]
In addition to their use in cancer medicine, biomarkers are often used throughout the cancer drug discovery process. For instance, in the 1960s, researchers discovered the majority of patients with chronic myelogenous leukemia possessed a particular genetic abnormality on chromosomes 9 and 22 dubbed the Philadelphia chromosome .
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