Sample A: Cover Page of Thesis, Project, or Dissertation Proposal

Chapter 5: Data Mining the Multiclass Dataset
Lung cancer is the most prominent form of cancer worldwide, representing 12.3% of all cancer
diagnoses [1]. It is a devastating disease, for 90% of those diagnosed with lung cancer will
eventually succumb to it, representing 17.8% of cancer deaths worldwide. Smoking tobacco is
strongly correlated to the development of lung cancer, with 80-90% of all diagnoses being
attributed to smokers, although only 11% of cigarette smokers will develop lung cancer [1].
There exist four histologically distinct lung cancer variants: 3 are of non small cell lung cancer
(NSCLC), and the other is small cell lung cancer (SMLC) [1]. NSMLC is the predominant form
of lung cancer, encompassing 80% of all lung cancer cases [2]. Adenocarcinoma has surpassed
squamous cell carcinoma in prevalence; both are subtypes of NSCLC, the most frequent subtype
of lung cancer [1, 2]. Alarmingly, adenocarcinoma is most common in women, non-smokers, and
the young [1, 2]. Adenocarcinomas are peripherally located in the lungs and develop from clara
cells, alveoli, and mucin producing cells. Squamous carcinomas arise in the central airways and
are the direct result of smoking, as there are no squamous epithelial cells in normal lungs.
Surgical intervention for patients without mediastinal involvement still results in only a 30-50%
chance of disease-free survival, with long-term survival greatly reduced for patients with
mediastinal involvement [1].
We have separated the samples from the Bhattacharjee dataset into several subsets, in order to
produce a NSCLC multiclass dataset consisting of adenocarcinomas, squamous cell carcinomas,
and normal (actually adjacent) biopsies. There exist 155 samples which survived the BaFL
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