The intelligent knife (iKnife) and its intraoperative diagnostic advantage for the treatment of cervical disease

Abstract

Clearance of surgical margins in cervical cancer prevents the need for adjuvant chemoradiation and allows fertility preservation. In this study, we determined the capacity of the rapid evaporative ionization mass spectrometry (REIMS), also known as intelligent knife (iKnife), to discriminate between healthy, preinvasive, and invasive cervical tissue. Cervical tissue samples were collected from women with healthy, human papilloma virus (HPV) ± cervical intraepithelial neoplasia (CIN), or cervical cancer. A handheld diathermy device generated surgical aerosol, which was transferred into a mass spectrometer for subsequent chemical analysis. Combination of principal component and linear discriminant analysis and least absolute shrinkage and selection operator was employed to study the spectral differences between groups. Significance of discriminatory m/z features was tested using univariate statistics and tandem MS performed to elucidate the structure of the significant peaks allowing separation of the two classes. We analyzed 87 samples (normal = 16, HPV ± CIN = 50, cancer = 21 patients). The iKnife discriminated with 100% accuracy normal (100%) vs. HPV ± CIN (100%) vs. cancer (100%) when compared to histology as the gold standard. When comparing normal vs. cancer samples, the accuracy was 100% with a sensitivity of 100% (95% CI 83.9 to 100) and specificity 100% (79.4 to 100). Univariate analysis revealed significant MS peaks in the cancer-to-normal separation belonging to various classes of complex lipids. The iKnife discriminates healthy from premalignant and invasive cervical lesions with high accuracy and can improve oncological outcomes and fertility preservation of women treated surgically for cervical cancer. Larger in vivo research cohorts are required to validate these findings.

Keywords: REIMS; cervical cancer; fertility preservation; iKnife; mass spectrometry.

Fig. 1.

Tissue collection and iKnife processing. (A) Cervical biopsy obtained with the use of cervical punch biopsy forceps during colposcopic assessment and snap-frozen. (B) Fresh frozen sample thawed to room temperature and diathermized with the iKnife, leading to ionization and aerosol (smoke) extraction of the tissue’s unique lipid profile. (C) Aerosol produced from diathermy is transported to the mass spectrometer with the use of suction tubing. (D) Aerosol is processed, producing translational results on screen. (E) Population and samples: 103 samples from 103 patients collected; 16 samples were excluded. The analysis included 87 samples from 87 patients. HPV ± CIN included samples with HPV changes and/or CIN.

Fig. 2.

iKnife analysis of benign and cancerous cervical biopsies. Benign (A) and malignant (D) uterine cervical biopsy with iKnife burns labeled (1 through 3) as the sampling sites. Total ion chromatogram (B = normal, E = cancer) obtained during the sampling of the specimen with the iKnife in the equivalent tissue type. Representative mass spectra, obtained in negative-ion mode from uterine cervical biopsies, showing the degree of variability in the peaks for different tissue types (C = normal, F = cancer). Marker explanation: Each spectral peak reflects one burn in the tissue images (1 through 3). The spectral image (B and E) is the outcome of the burn.

Fig. 3.

Multivariate analysis of obtained spectra per sample for identification of spectral differences between Normal, HPV ± CIN and Cancerous tissue. (A) LDA (for model normal vs. HPV ± CIN vs. cancer). (B) Proportion (percent) of correct classification when correlating iKnife diagnosis to gold-standard histology following LDA, leave-one-sample-out cross-validation of all samples.

Fig. 4.

(A) LDA multivariate analysis of obtained spectra per sample for identification of spectral differences between normal and abnormal (HPV ± CIN or cancerous) tissue. (B) Proportion (percent) of correct classification when correlating iKnife diagnosis to gold-standard histology following PC-LD analysis. Leave-one-sample-out cross-validation: (a) normal (normal cervical tissue) vs. abnormal (HPV + CIN + cancer); (b) normal vs. cancer; (c) normal vs. HPV ± CIN; (d) HPV ± CIN vs. cancer.

Fig. 5.

Lipid groups of the identified 12 significant peaks. (A) Box plots of the top m/z peaks contributing to class separation (log₂ fold change), based on the univariate analysis between normal and cancerous cervical tissue. The log2 fold changes are determined based on the median spectrum over all observations. The 12 most significant and intense REIMS spectra peaks are shown with P < 0.001, q < 0.001. (B) Forest plot of the P values of the 12 significant peaks identified during the univariate analysis between normal and cancerous cervical tissue.