China Oncology ›› 2023, Vol. 33 ›› Issue (4): 377-387.doi: 10.19401/j.cnki.1007-3639.2023.04.008
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LIU Yang1(), HU Yiyang1, LIU Yueping2, NIU Shuyao2, DING Pingan1, TIAN Yuan1, GUO Honghai1, YANG Peigang1, ZHANG Ze1, ZHENG Tao1, TAN Bibo1, FAN Liqiao1, LI Yong1, ZHAO Qun1()
Received:
2022-05-27
Revised:
2023-03-13
Online:
2023-04-30
Published:
2023-05-15
Contact:
ZHAO Qun
CLC Number:
LIU Yang, HU Yiyang, LIU Yueping, NIU Shuyao, DING Pingan, TIAN Yuan, GUO Honghai, YANG Peigang, ZHANG Ze, ZHENG Tao, TAN Bibo, FAN Liqiao, LI Yong, ZHAO Qun. The value of artificial intelligence-assisted technology in HER2 assessmentof gastric cancer patients receiving neoadjuvant chemotherapy[J]. China Oncology, 2023, 33(4): 377-387.
Fig. 1
AI-assisted microscope assessment A: Selected the area; B: Completed convolution network located tumor cells, delineated the stained membranes in different color; C: Shielded the signal of non-tumor cells and highlighted the stained cell membrane. The results showed that the complete and incomplete strong staining rates were 89.3% and 91.7%, the complete and incomplete weak staining rates were 0.0% and 0.0%, the no staining rate was 0.0%, suggested rating +++."
Fig. 4
Comparison of consistency between visual assessment, AI-assisted assessment and gold standard assessment A: Consistency of pre-NAC HER2 between visual assessment with gold standard (κ = 0.766); B: Consistency of pre-NAC HER2 between AI assessment with gold standard (κ = 0.853); C: Consistency of post-NAC HER2 between visual assessment with gold standard (κ = 0.773); D: Consistency of post-NAC HER2 between AI assessment with gold standard (κ = 0.876)."
Tab. 1
Changes in HER2 status after NAC assessed by AI and clinicopathologic factors"
Clinicopathological parameter | HER2 expression changes/% | χ2 | P value | |
---|---|---|---|---|
Down-regulated expression (n=97) | Up-regulated expression (n=27) | |||
Gender | 0.033 | 0.856 | ||
Male | 77 (79.4) | 21 (77.8) | ||
Female | 20 (20.6) | 6 (22.2) | ||
Age/year | 0.808 | 0.369 | ||
<60 | 48 (49.5) | 16 (59.3) | ||
≥60 | 49 (50.5) | 11 (40.7) | ||
NEC regimen | 0.201 | 0.654 | ||
XELOX | 55 (56.7) | 14 (51.9) | ||
SOX | 42 (43.3) | 13 (48.1) | ||
Tumor location | 1.050 | 0.592 | ||
Esophagogastric junction | 31 (32.0) | 6 (22.2) | ||
Stomach body | 37 (38.1) | 11 (40.7) | ||
Antrum | 29 (29.9) | 10 (37.0) | ||
Tumor length D/cm | 1.323 | 0.250 | ||
≤5 | 72 (74.2) | 17 (63.0) | ||
>5 | 25 (25.8) | 10 (37.0) | ||
Differentiation | 0.473 | 0.491 | ||
Moderate | 37 (44.6) | 10 (37.0) | ||
Poor | 46 (55.4) | 17 (63.0) | ||
Lauren type | 3.248 | 0.197 | ||
Intestine | 33 (39.8) | 10 (37.0) | ||
Diffused | 30 (36.1) | 6 (22.2) | ||
Mixed | 20 (24.1) | 11 (40.7) | ||
Tumor regression grade | 8.557 | 0.003 | ||
≤2 | 66 (68.0) | 10 (37.0) | ||
3 | 31 (32.0) | 17 (63.0) | ||
Nerve invasion | 2.630 | 0.105 | ||
Yes | 37 (38.1) | 15 (55.6) | ||
No | 60 (61.9) | 12 (44.4) | ||
Vascular tumor embolus | 1.563 | 0.211 | ||
Yes | 18 (18.6) | 8 (29.6) | ||
No | 79 (81.4) | 19 (70.4) | ||
ypT | 1.591 | 0.207 | ||
T1-3 | 34 (35.1) | 6 (22.2) | ||
T4 | 63 (64.9) | 21 (77.8) | ||
ypN | 5.509 | 0.019 | ||
N0 | 46 (47.4) | 6 (22.2) | ||
N+ | 51 (52.6) | 21 (77.8) | ||
ypM | 1.151 | 0.576 | ||
M0 | 93 (95.9) | 27 (100.0) | ||
M1 | 4 (4.1) | 0 (0.0) |
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