Rapid pathologic grading-based diagnosis of esophageal squamous cell carcinoma via Raman spectroscopy and a deep learning algorithm

Table 1 Summaries of the fundamental information of enrolled subjects and the corresponding number of collected Raman spectra

Patient information		Number of patients	Number of spectra
Age	< 60 years	24
	≥ 60 years	66
Gender	Male	59
	Female	31
Test site	Neck	2
	Upper	24
	Middle	40
	Lower	16
Histological grading	Health	30	120
	Low grade	30	120
	High grade	30	120

Table 2 Analysis of peak positions and assignment in Raman spectra during low-health and high-low progressions

Progression type	Change direction	Raman shift, cm-1	Band assignments
Low-health	Increase	891	Lauric acid
		1078	v(C-C) of lipids
		1095	DNA, symmetric PO, stretching vibration
		1132	Cytochrome c
		1171	Acetoacetate
		1216	Amide III and CH2 wagging
		1295	Amide III delta (CH)2
		1369	Thymine
		1417	CH2 stretch
		1440	Lipin
		1596-1616	Phenylalanine C = C
		1618	v(C = C) of porphyrins
High-low	Decrease	891	Lauric acid
		1004	vs(C-C) ring breathing of phenylalanine
		1078	v(C-C) of lipids
		1095	DNA, symmetric PO, stretching vibration
		1132	Cytochrome c
		1171	Acetoacetate
		1295	Amide III delta (CH)2
		1315	Glycerol
		1369	Thymine
		1417	CH2 stretch
		1448-1468	CH3 deformation
		1618	v(C = C) of porphyrins

Table 3 Results for the specificity, sensitivity, and F1 score achieved by the 1D-transformer model in the pathology grading task

Classification	Category	Specificity	Sensitivity	F1 score
Histologic grading	Health	95.0	99.15	94.74
	Low	98.33	83.19	89.19
	High	96.62	97.52	95.55

Table 4 Results for the accuracy, specificity, sensitivity, and F1 score produced by each of the ten models in the pathology grading task

Classification	Model	Accuracy	Specificity	Sensitivity	F1 score
Histologic grading	Model 1	94.12	97.04	93.94	93.89
	Model 2	91.67	95.83	91.68	91.77
	Model 3	97.22	98.61	97.22	97.22
	Model 4	88.89	94.44	88.89	88.57
	Model 5	94.44	97.22	94.44	94.41
	Model 6	94.29	97.10	94.44	94.41
	Model 7	94.44	97.22	94.44	94.30
	Model 8	88.89	94.44	88.89	88.30
	Model 9	94.44	97.22	94.44	94.30
	Model 10	94.59	97.33	94.66	94.56

Table 5 The performance metrics

Algorithm types	Accuracy	Specificity	Sensitivity	F1 score
PLS-Transformer	93.30 ± 2.53	96.65 ± 1.27	93.30 ± 2.54	93.17 ± 2.67
PLS-ResNet18	88.52 ± 5.44	94.28 ± 2.72	88.51 ± 5.50	88.57 ± 5.38
PLS-LSTM	80.18 ± 5.60	90.06 ± 3.11	80.09 ± 6.79	78.80 ± 6.43
PLS-GRU	88.58 ± 7.39	94.22 ± 4.02	88.47 ± 7.63	87.87 ± 7.46
PLS-EfficientNet	81.01 ± 6.10	90.64 ± 3.32	81.94 ± 5.91	80.72 ± 5.64
PLS-DenseNet	90.79 ± 3.94	95.29 ± 2.15	90.73 ± 4.09	90.62 ± 3.68
PLS-SVM	87.46 ± 7.51	94.04 ± 2.89	87.20 ± 7.16	87.30 ± 7.20
PLS-XGB	74.27 ± 5.86	85.00 ± 4.03	74.36 ± 5.09	74.4 ± 5.5

PLS: Partial least-squares; LSTM: Long Short-Term Memory; GRU: Gated Recurrent Units; SVM: Support vector machine; XGB: Extreme gradient boosting.

Table 6 Analysis of peak positions and assignment in Raman spectra of health, low, and high tissues

Raman shift, cm-1	Intensity	Band assignment
891	w	Lauric acid
1004	w	vs(C-C) ring breathing of phenylalanine
1078	w	v(C-C) of lipids
1095	w, m	DNA; symmetric PO, stretching vibration
1132	m	Cytochrome c
1171	w, m	Acetoacetate
1216	m	Amide III and CH2 wagging
1295	w	Amide III delta (CH)2
1315	m	Glycerol
1369	w	Thymine
1440	w	Lipin
1448-1468	w	CH3 deformation

Intensity: “w” means weak (0.4-0.6) and “m” means mid (0.6-0.8).