In-vivo detection of cervical cancer lesions using hyperspectral colposcopy

Cervical cancer originates from precursor lesions in the cervix, where early diagnosis is essential to avoid progression to invasive carcinoma. Today, lesion detection relies mainly on colposcopy, a highly operator-dependent procedure with considerable variability in accuracy, particularly among less-experienced clinicians. In this study, we present a hyperspectral imaging-based approach for assisted colposcopy. We developed a custom hyperspectral colposcope covering 470-900 nm and conducted a 32-month clinical study including 116 patients and 245 hyperspectral images acquired during routine examinations. First, a dedicated algorithm automatically delineated the cervical region and segmented the tissue into ectocervix, endocervix and abnormal areas, achieving a macro-level DICE of 0.84. Subsequently, two approaches were studied for pixel-wise lesion classification: a binary model for high-grade squamous intraepithelial lesion and invasive carcinoma versus healthy tissue, which achieved a mean F1-score of 0.74 on an independent test set; and a multiclass model for grading according to the Bethesda system, which showed lower generalisation (F1-score = 0.26) due to limited spectral resolution and spectral overlap. Overall, the results show the potential of hyperspectral colposcopy for non-invasive detection and delimitation of cervical lesions.