|
| Type of cancer | Number of patients | Data analysis | Outcome | Reference |
|
| Breast | 9 (31 spectra acquired) | Fitting of spectra based on linear combination model coefficients from ex vivo tissue samples | Sensitivity: 100%
Specificity: 100%
Accuracy: 93.3%
| [31] |
|
| Skin | 19 (21 tissue sites with total of 42 spectra) | Nonlinear maximum representation and discrimination (MRDF) and Sparse linear multinomial logistic regression (SMLR) | Sensitivity: 100%
Specificity: 91%
Accuracy: 95%
| [45] |
|
| Brain | Rat model (C6 glioma cells for glioblastoma model) | Prinicipal component analysis (PCA), Ward’s Clustering algorithm and square Euclidian distance measures | In vivo classification based on ex vivo model with 100% accuracy | [51] |
|
| Brain | Rat model (cortical and subcortical melanotic tumor model) | k-means cluster analysis | Development of false-color tissue map of brain tumors;tumor margins delineated | [54] |
|
| Gastric* | 67 (238 total tissue sites) | Nonnegative constrained least squares minimization with Classification and Regression Tree (CART) model | Sensitivity: 94.0%
Specificity: 93.4%
Accuracy: 93.7% | [61] |
|
| Gastric* | 71 (1102 spectra acquired) | Partial least squares and linear discriminant analysis (PLS-DA) | Sensitivities: 93.8, 84.7, 82.1%
Specificities: 93.8, 94.5, 95.3% (normal, benign, malignant) | [62] |
|
| Gastric* | 67 (238 total tissue sites) | Ant colony optimization integrated with linear discriminant analysis (ACO-LDA) | Sensitivity: 94.6%, 89.3%
Specificity: 94.6%, 97.8%
Accuracy: 94.6%, 96.7% (diagnostic, predictive) | [63] |
|
| Esophgeal | 27 (75 total tissue sites) | Nonnegative constrained least squares minimization (NNCLSM) and linear discriminate analysis (LDA) | Sensitivity: 97.0%
Specificity: 95.2%
Accuracy: 96.0% | [65] |
|
| Upper GI tract | 107 (1189 spectra acquired) | Nonnegative constrained least squares minimization (NNCLSM) with reference database for biomolecular modeling | Sensitivities: 92.6%, 90.9%
Specificities: 88.6%, 93.9%
Accuracy: 89.3%, 94.7% (gastric, esophagus) | [66] |
|
| Cervical | 66* (172 tissue sites) (11 patients excluded) | Logistic regression discrimination algorithms | Sensitivity: 89%
Specificity: 81% | [82] |
|
Cervical (effect of hormonal
variation on
cervical disease) | 122 | Nonlinear maximum representation and discrimination (MRDF) and Sparse linear multinomial logistic regression (SMLR) | Incorporation of the hormonal variation information into a previous model improved model accuracy from 74% to 97% for diagnosis of LGSIL | [83] |
|
| Lung | 26 (129 spectra acquired) | Principal component analysis and linear discriminant analysis with a databased biomolecular model | Sensitivity: 96%
Specificity: 91% | [110] |
|
