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Neural Networks for the Estimation of Prognosis in Lung Cancer
Published in Raouf N.G. Naguib, Gajanan V. Sherbet, Artificial Neural Networks in Cancer Diagnosis, Prognosis, and Patient Management, 2001
H. Esteva, M. Bellotti, A.M. Marchevsky
The World Health Organisation (WHO) classifies lung cancer from a histopathological point of view as: (1) squamous cell carcinoma (well, moderately and poorly differentiated); (2) adenocarcinoma (not otherwise specified and bronchoalveolar); (3) adenosquamous carcinoma; (4) small cell undifferentiated carcinoma (classic, combined); (5) large cell undifferentiated carcinoma; and (6) giant cell carcinoma [1]. With general consensus, small cell undifferentiated carcinoma is considered separately from other primary bronchogenic carcinomas. Embryological origin and early systemic spread of this neuro-endocrine malignancy are different than those of other pulmonary neoplasms. Bronchogenic carcinomas have broadly been divided into small cell and nonsmall cell bronchogenic carcinomas. This chapter is related to the latter category.
Predicting cervical cancer biopsy results using demographic and epidemiological parameters: a custom stacked ensemble machine learning approach
Published in Cogent Engineering, 2022
Krishnaraj Chadaga, Srikanth Prabhu, Niranjana Sampathila, Rajagopala Chadaga, Swathi K S, Saptarshi Sengupta
In this research, machine learning is used to examine a set of clinical parameters which could possibly predict cervical cancer. The change in these parameters can be crucial to identifying this deadly disease. Various tests such as the Schiller’s test, cytology test, Hinselmann’s test and the biopsy test are used to come to a final diagnosis. According to the study, the Schiller’s test is the most important in predicting the biopsy results. In this test, iodine solution is applied to the cervical tissues. The color of the healthy cells changes to brown and the color of the abnormal cells do not change (Jaya & Latha, 2022). This iodine solution is also known as the Lugol’s solution. The cytology test is also very important in cervical cancer diagnosis (da Silva et al., 2022). This method is used to identify pre-invasive cervical lesions (high grade cervical intraepithelial neo plasma) at a preliminary stage. It is a process in which tissues are removed from the surface of the cervix. The examination of these tissues is done under a microscope to find all alterations which may result in cervical cancer. The Hinselmann’s test is a technique to accurately examine the cervix for signs of infection and diseases. During the test, a special instrument called the colposcope is utilized (Yoon et al., 2022). Studies have shown that taking oral contraceptives over a period of time increases the risk of contracting cervical cancer (La Vecchia & Boccia, 2014). Our research agrees with the same and women using hormonal contraceptives for a long period of time had tested positive for the biopsy result. According to studies, women between the ages of 36 and 45 are most susceptible to this disease (Bruni et al., 2022). Our studies agree with the same. Cervical cancer can also be caused by other STD’s such as syphilis, herpes, chlamydia and gonorrhea (Pillai et al., 2022). Generally, cervical cancer is associated with HPV virus, but it can also be caused by other viruses. The usage of IUD’s decreases the chance of contracting cervical cancer according to many researches (Averbach et al., 2018). The results obtained by the models agree with the above concept. IUD use lowers the risk of contracting adenosquamous carcinoma and squamous cell carcinoma, the two main kinds of cervical cancer. During the initial year of IUD use, the likelihood for both malignancies was found to be lowered by almost half. Figure 23 describes the user interface built to make biopsy result predictions. The final stacked model was deployed using the “gradio” library (Abid et al., 2019). There are various textboxes which can be filled with attribute values. Once the submit button is pressed, the prediction is made in the output text box. The model can be deployed in various hospitals as a preliminary screening tool.