Parathyroid differentiation during thyroid and parathyroid surgery by spectroscopy 157 Figure 10.5B Parathyroid versus thyroid tissue: Scatter plot of two selected features within InGaAs‐range Scatter plot showing selected features (gradients Ft12 and Ft14) Table 10.3 Classification performance of selected InGaAs‐sensor features LOO CV TP TN Sensitivity Specificity PPV NPV Accuracy Parathyroid – Adipose 19/21 13/18 90 (68‐98) 72 (46‐89) 79 (57‐92) 87 (58‐98) 82 Parathyroid – Thyroid 16/21 17/23 76 (52‐91) 74 (51‐89) 73 (50‐88) 77 (54‐91) 75 TP = true positive; TN = true negative numbers indicate identified tissue spots. A positive test is defined as the tissue observed being parathyroid gland; a negative test is defined as the tissue observed being adipose tissue / thyroid. Sensitivity; specificity; PPV = positive predictive value; NPV = negative predictive value; accuracy numbers are percentages; numbers in parentheses indicate 95% confidence interval. LOO CV = leave‐one‐out cross–validation. Combining Si‐sensor and InGaAs‐sensor ranges for spectral data classification Classification performance was also evaluated when combining the Si‐sensor and InGaAs‐sensor detector ranges, including up to 3 spectral features. After binary logistic regression, gradients Ft12 (W1 – F4), Ft17 (F4 – F5) and Ft36 (650 – 700 nm) were selected as the most promising combination for differentiation of parathyroid from surrounding adipose tissue. Gradients Ft27 (W1 – F4) and Ft32 (F2 – F1) and Ft36 (650 – 700 nm) were identified as best distinctive feature combination for differentiation of parathyroid from thyroid tissue.
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