International ophthalmology · 2026
Diagnostic ability of corneal surface irregularity biomarkers across different stages of keratoconus:A prospective cross-sectional study.
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Bibi Ayesha, Asrar Umair, Akhtar Maria, Zahid Arooj, Ali Waqas
Аннотация
PURPOSE: To evaluate the diagnostic performance of corneal surface irregularity biomarkers (CSI-f, CSI-b, SI-f, SI-b, Epi-TI, and TSI) in differentiating healthy eyes from those with forme fruste keratoconus and various stages of keratoconus, and to assess their utility for early disease detection.
METHODS: This prospective cross-sectional diagnostic study included 173 eyes from 173 participants, comprising 62 healthy controls, 21 forme fruste keratoconus eyes (FFKC), and 90 keratoconus eyes (KC). Pentacam TKC grading was used to categorize keratoconus eyes into mild, moderate, and severe stages. The MS-39 was used to obtain corneal surface irregularity biomarkers (CSI-f, CSI-b, SI-f, SI-b, Epi-TI, and TSI). Diagnostic performance was evaluated using receiver operating characteristic (ROC) analysis. The Corneal Surface Irregularity Index (CSII) was created using Firth-penalized logistic regression with internal cross-validation using fully nested repeated stratified 10-fold cross-validation.
RESULTS: All corneal surface irregularity biomarkers increased considerably with increasing keratoconus severity (p < 0.001). Compared with healthy controls, FFKC showed markedly higher SI-f, SI-b, Epi-TI, and TSI values, suggesting visible surface irregularity in the preclinical stage. SI-f demonstrated the best diagnostic performance among individual biomarkers for distinguishing FFKC from normal eyes (AUC = 0.916), whereas SI-b achieved the highest accuracy for distinguishing KC from normal eyes (0.999). After fully nested repeated stratified 10-fold cross-validation, the CSII model maintained significant internal discrimination with a nested out-of-fold AUC of 0.890 (95% CI, 0.786-0.995) for FFKC and 0.997-0.999 for manifest KC and its severity phases. In benchmark analysis, the MS-39 biomarker model considerably outperformed traditional tomographic parameters for FFKC detection (P = 0.030).
CONCLUSION: MS-39-derived corneal surface irregularity biomarkers demonstrated excellent diagnostic performance for detecting FFKC, differentiating keratoconus from healthy eyes, and distinguishing FFKC from early manifest keratoconus. However, the CSII model retained strong internal discrimination after fully nested validation, although the performance was lower for FFKC than manifest KC. These results imply that including biomarkers for ocular surface irregularities in standard tomographic evaluation could enhance the early identification and clinical treatment of keratoconus.
METHODS: This prospective cross-sectional diagnostic study included 173 eyes from 173 participants, comprising 62 healthy controls, 21 forme fruste keratoconus eyes (FFKC), and 90 keratoconus eyes (KC). Pentacam TKC grading was used to categorize keratoconus eyes into mild, moderate, and severe stages. The MS-39 was used to obtain corneal surface irregularity biomarkers (CSI-f, CSI-b, SI-f, SI-b, Epi-TI, and TSI). Diagnostic performance was evaluated using receiver operating characteristic (ROC) analysis. The Corneal Surface Irregularity Index (CSII) was created using Firth-penalized logistic regression with internal cross-validation using fully nested repeated stratified 10-fold cross-validation.
RESULTS: All corneal surface irregularity biomarkers increased considerably with increasing keratoconus severity (p < 0.001). Compared with healthy controls, FFKC showed markedly higher SI-f, SI-b, Epi-TI, and TSI values, suggesting visible surface irregularity in the preclinical stage. SI-f demonstrated the best diagnostic performance among individual biomarkers for distinguishing FFKC from normal eyes (AUC = 0.916), whereas SI-b achieved the highest accuracy for distinguishing KC from normal eyes (0.999). After fully nested repeated stratified 10-fold cross-validation, the CSII model maintained significant internal discrimination with a nested out-of-fold AUC of 0.890 (95% CI, 0.786-0.995) for FFKC and 0.997-0.999 for manifest KC and its severity phases. In benchmark analysis, the MS-39 biomarker model considerably outperformed traditional tomographic parameters for FFKC detection (P = 0.030).
CONCLUSION: MS-39-derived corneal surface irregularity biomarkers demonstrated excellent diagnostic performance for detecting FFKC, differentiating keratoconus from healthy eyes, and distinguishing FFKC from early manifest keratoconus. However, the CSII model retained strong internal discrimination after fully nested validation, although the performance was lower for FFKC than manifest KC. These results imply that including biomarkers for ocular surface irregularities in standard tomographic evaluation could enhance the early identification and clinical treatment of keratoconus.