Using Naïve Bayesian Analysis to Determine Imaging Characteristics of KRAS Mutations in Metastatic Colon Cancer

Abstract

Genotype, particularly Ras status, greatly affects prognosis and treatment of liver metastasis in colon cancer patients. This pilot aimed to apply word frequency analysis and a naive Bayes classifier on radiology reports to extract distinguishing imaging descriptors of wild-type colon cancer patients and those with v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog (KRAS) mutations. In this institutional-review-board-approved study, we compiled a SNaPshot mutation analysis dataset from 457 colon adenocarcinoma patients. From this cohort of patients, we analyzed radiology reports of 299 patients (> 32,000 reports) who either were wild-type (147 patients) or had a KRAS (152 patients) mutation. Our algorithm determined word frequency within the wild-type and mutant radiology reports and used a naive Bayes classifier to determine the probability of a given word belonging to either group. The classifier determined that words with a greater than 50% chance of being in the KRAS mutation group and which had the highest absolute probability difference compared to the wild-type group included: “several”, “innumerable”, “confluent”, and “numerous” (p < 0.01). In contrast, words with a greater than 50% chance of being in the wild type group and with the highest absolute probability difference included: “few”, “discrete”, and “[no] recurrent” (p = 0.03). Words used in radiology reports, which have direct implications on disease course, tumor burden, and therapy, appear with differing frequency in patients with KRAS mutations versus wild-type colon adenocarcinoma. Moreover, likely characteristic imaging traits of mutant tumors make probabilistic word analysis useful in identifying unique characteristics and disease course, with applications ranging from radiology and pathology reports to clinical notes.

Comprehensive Analysis of the Relationship Between RAS and RAF Mutations and MSI Status of Colorectal Cancer in Northeastern China

“…By comparing the mutation rates of KRAS in different countries (Table 3), the following conclusions were drawn: the KRAS mutation rate among Americans is the highest [15][16][17][18][19], followed by Europeans [33][34][35][36][37][38][39][40][41][42], and then Asians [20][21][22][23][24][25][26][27][28][29][30][31][32]. KRAS is the most important member of the RAS family.…”

“…In Western European countries [33][34][35][36][37][38][39][40][43][44], most studies demonstrated that the RAF mutation rate was over 10%. In the United States, only a few studies demonstrated a higher mutation rate of RAF [15][16][17]45]. In Asia, the mutation rate is the lowest, ranging between 4-9% [24][25][26][27][28][45][46][47].…”

Natural Language Processing for Automated Quantification of Brain Metastases Reported in Free-Text Radiology Reports

“…NLP for the analysis of radiology reports has also been explored in the context of other cancer types, including hepatocellular carcinomas, [13][14][15][16] breast cancer, [17][18][19][20] lung cancer, [21][22][23] and other abdominal or pelvic tumors. 11,[24][25][26][27] All studies that provided sufficient insight into their modeling approach used a bag-of-words approach. To our knowledge, this study presents the first sequence-based NLP approach for analyzing free-text radiology reports in oncology patients as well as the first head-to-head comparison of sequence-based and bag-of-words models for medical text analysis.…”

Radiogenomics: bridging imaging and genomics

“…Outside the domain of image analysis, Pershad et al used a purely text-based approach to process the descriptive words used by radiologists in their reports in order to predict KRAS mutational status [178]. The trained classifier determined the word frequency within both mutant and wildtype radiology report.…”