Skip to main content

Table 1 Demographic characteristics and disease categories of the study subjects in different datasets

From: Development and validation of an endoscopic images-based deep learning model for detection with nasopharyngeal malignancies

Characteristics

All

Training set

Validation set

Test set

Prospective test set

Subjects, n

8306

5557

807

1587

355

Mean (± SD), years

45.9 ± 12.7

45.8 ± 12.7

45.9 ± 12.7

45.7 ± 12.7

47.8 ± 13.0

Sex, n(%)

 Female

2562 (30.9)

1681 (30.3)

250 (31.0)

507 (32.0)

124 (34.9)

 Male

5612 (67.6)

3783 (68.1)

540 (66.9)

1058 (66.7)

231 (65.1)

 N/A

132 (1.6)

93 (1.7)

17 (2.1)

22 (1.4)

0 (0.0)

Disease category, n(%)

Normal

5713 (19.7)

3763 (19.2)

584 (21.7)

961 (18.2)

405 (28.3)

Malignancies

 NPC

19,107 (66.0)

13,061 (66.7)

1749 (65.0)

3564 (67.6)

731(51.1)

 Othersa

335 (1.2)

252 (1.3)

22 (0.8)

54 (1.0)

7 (0.4)

Benign diseasesb

3811 (13.2)

2500 (12.8)

335 (12.5)

691 (13.1)

287(20.1)

Images, n(%)

28,966

19,576 (67.6)

2690 (9.3)

5270 (18.2)

1430 (4.9)

  1. N/A not available, NPC nasopharyngeal carcinoma
  2. aLymphoma, rhabdomyosarcoma, olfactory neuroblastoma, malignant melanoma and plasmacytoma
  3. bPrecancerous/atypical hyperplasia, fibroangioma, leiomyoma, meningioma, minor salivary gland tumour, fungal infection, tuberculosis, chronic inflammation, adenoids/lymphoid hyperplasia, nasopharyngeal cyst and foreign bodies