title: Fourier transform infrared microspectroscopy as a quantitative diagnostic tool for assignment of premalignancy grading in cervical neoplasia.
abstract: The early diagnosis and proper identification of cervical squamous intraepithelial lesions plays an important role in a good prognosis for the patient. However, the present practice of screening based on PAP (Papanicolaou) smear and histopathology makes it tedious and prone to human errors. We assess the validity of FTIR microspectroscopy (FTIR-MSP) of biopsies as a method to properly assign the correct stage of premalignancy in patients with symptoms of cervical intraepithelial neoplasia. For the first time we evaluate the biopsies based on the FTIR spectra for different grades of neoplasia in tandem with probabilistic neural networks (PNNs) and histopathology. The results show that the grading of neoplasia based on FTIR-MSP and a PNN differentiates the normal from premalignant with a high level of accuracy. The false positive identification of the normal as cervical intraepithelial neoplasia 1 (CIN1), CIN2, and CIN3 patients is 9.04, 0.01, and 0.01%, respectively. The false negative identification of CIN2 patients as normal and CIN1 patients is 0.01 and 4.4%, respectively. Similarly, the false negative identification of CIN3 patients as normal, CIN1, and CIN2 is 0.14, 6.99, and 9.61%, respectively. The small errors encountered in the grading are comparable to current methods, encouraging advanced studies for the development of mechanized equipment for the diagnosis and grading of premalignant cervical neoplasia.
authors.affiliation: Department of Physics, Ben Gurion University, Beer-Sheva, IL-84105, Israel.
authors.forename: S
authors.initials: S
authors.lastname: Mark
authors.forename: R K
authors.initials: RK
authors.lastname: Sahu
authors.forename: K
authors.initials: K
authors.lastname: Kantarovich
authors.forename: A
authors.initials: A
authors.lastname: Podshyvalov
authors.forename: H
authors.initials: H
authors.lastname: Guterman
authors.forename: J
authors.initials: J
authors.lastname: Goldstein
authors.forename: R
authors.initials: R
authors.lastname: Jagannathan
authors.forename: S
authors.initials: S
authors.lastname: Argov
authors.forename: S
authors.initials: S
authors.lastname: Mordechai
mesh_terms: D000465:Algorithms; D001706:Biopsy / Q000379:methods; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005103:Expert Systems; D005260:Female; D006801:Humans; D015591:Microspectrophotometry / Q000379:methods*; D009367:Neoplasm Staging / Q000379:methods; D016571:Neural Networks, Computer; D011230:Precancerous Conditions / Q000145:classification / Q000175:diagnosis* / Q000473:pathology; D012680:Sensitivity and Specificity; D017550:Spectroscopy, Fourier Transform Infrared / Q000379:methods*; D002583:Uterine Cervical Neoplasms / Q000145:classification / Q000175:diagnosis* / Q000473:pathology