Introduction: Lymph node metastasis is one of the most common ways of tumour metastasis. The presence or absence of lymph node involvement influences the cancer’s stage, therapy, and prognosis. The integration of artificial intelligence systems in the histopathological diagnosis of lymph nodes after surgery is urgent. Methods: Here, we propose a pan-origin lymph node cancer metastasis detection system. The system is trained by over 700 whole-slide images (WSIs) and is composed of two deep learning models to locate the lymph nodes and detect cancers. Results: It achieved an area under the receiver operating characteristic curve (AUC) of 0.958, with a 95.2% sensitivity and 72.2% specificity, on 1,402 WSIs from 49 organs at the National Cancer Center, China. Moreover, we demonstrated that the system could perform robustly with 1,051 WSIs from 52 organs from another medical centre, with an AUC of 0.925. Conclusion: Our research represents a step forward in a pan-origin lymph node metastasis detection system, providing accurate pathological guidance by reducing the probability of missed diagnosis in routine clinical practice.

1.
Onder
L
,
Ludewig
B
.
A fresh view on lymph node organogenesis
.
Trends Immunol
.
2018
;
39
(
10
):
775
87
.
2.
Fidler
IJ
.
The pathogenesis of cancer metastasis: the “seed and soil” hypothesis revisited
.
Nat Rev Cancer
.
2003
;
3
(
6
):
453
8
.
3.
Hur
K
,
Han
TS
,
Jung
EJ
,
Yu
J
,
Lee
HJ
,
Kim
WH
, et al
.
Up-regulated expression of sulfatases (SULF1 and SULF2) as prognostic and metastasis predictive markers in human gastric cancer
.
J Pathol
.
2012
;
228
(
1
):
88
98
.
4.
Bhattacharya
P
,
Mukherjee
R
.
Lymph node extracapsular extension as a marker of aggressive phenotype: classification, prognosis and associated molecular biomarkers
.
Eur J Surg Oncol
.
2021
;
47
(
4
):
721
31
.
5.
Griebling
TL
,
Ozkutlu
D
,
See
WA
,
Cohen
MB
.
Prognostic implications of extracapsular extension of lymph node metastases in prostate cancer
.
Mod Pathol
.
1997
;
10
(
8
):
804
9
.
6.
Veronese
N
,
Nottegar
A
,
Pea
A
,
Solmi
M
,
Stubbs
B
,
Capelli
P
, et al
.
Prognostic impact and implications of extracapsular lymph node involvement in colorectal cancer: a systematic review with meta-analysis
.
Ann Oncol
.
2016
;
27
(
1
):
42
8
.
7.
Beroukhim
R
,
Mermel
CH
,
Porter
D
,
Wei
G
,
Raychaudhuri
S
,
Donovan
J
, et al
.
The landscape of somatic copy-number alteration across human cancers
.
Nature
.
2010
;
463
(
7283
):
899
905
.
8.
Ehteshami Bejnordi
B
,
Veta
M
,
Johannes van Diest
P
,
Van Ginneken
B
,
Karssemeijer
N
,
Litjens
G
, et al
.
Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer
.
JAMA
.
2017
;
318
(
22
):
2199
210
.
9.
Amin
MB
,
Edge
SB
,
Greene
FL
.
AJCC cancer staging manual
. 8th ed.
Springer Science & Business Media
;
2017
.
10.
LeCun
Y
,
Bengio
Y
,
Hinton
G
.
Deep learning
.
Nature
.
2015
;
521
(
7553
):
436
44
.
11.
Coudray
N
,
Ocampo
PS
,
Sakellaropoulos
T
,
Narula
N
,
Snuderl
M
,
Fenyö
D
, et al
.
Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning
.
Nat Med
.
2018
;
24
(
10
):
1559
67
.
12.
Song
Z
,
Zou
S
,
Zhou
W
,
Huang
Y
,
Shao
L
,
Yuan
J
, et al
.
Clinically applicable histopathological diagnosis system for gastric cancer detection using deep learning
.
Nat Commun
.
2020
;
11
(
1
):
4294
9
.
13.
Zheng
X
,
Wang
R
,
Zhang
X
,
Sun
Y
,
Zhang
H
,
Zhao
Z
, et al
.
A deep learning model and human-machine fusion for prediction of EBV-associated gastric cancer from histopathology
.
Nat Commun
.
2022
;
13
(
1
):
2790
12
.
14.
Yu
G
,
Sun
K
,
Xu
C
,
Shi
XH
,
Wu
C
,
Xie
T
, et al
.
Accurate recognition of colorectal cancer with semi-supervised deep learning on pathological images
.
Nat Commun
.
2021
;
12
(
1
):
6311
3
.
15.
Ström
P
,
Kartasalo
K
,
Olsson
H
,
Solorzano
L
,
Delahunt
B
,
Berney
DM
, et al
.
Artificial intelligence for diagnosis and grading of prostate cancer in biopsies: a population-based, diagnostic study
.
Lancet Oncol
.
2020
;
21
(
2
):
222
32
.
16.
Bulten
W
,
Pinckaers
H
,
van Boven
H
,
Vink
R
,
de Bel
T
,
van Ginneken
B
, et al
.
Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study
.
Lancet Oncol
.
2020
;
21
(
2
):
233
41
.
17.
Tolkach
Y
,
Dohmgörgen
T
,
Toma
M
,
Kristiansen
G
.
High-accuracy prostate cancer pathology using deep learning
.
Nat Mach Intell
.
2020
;
2
(
7
):
411
8
.
18.
Nagpal
K
,
Foote
D
,
Tan
F
,
Liu
Y
,
Chen
PHC
,
Steiner
DF
, et al
.
Development and validation of a deep learning algorithm for Gleason grading of prostate cancer from biopsy specimens
.
JAMA Oncol
.
2020
;
6
(
9
):
1372
80
.
19.
Bulten
W
,
Balkenhol
M
,
Belinga
JJA
,
Brilhante
A
,
Çakır
A
,
Egevad
L
, et al
.
Artificial intelligence assistance significantly improves Gleason grading of prostate biopsies by pathologists
.
Mod Pathol
.
2021
;
34
(
3
):
660
71
.
20.
Nagpal
K
,
Foote
D
,
Liu
Y
,
Chen
PHC
,
Wulczyn
E
,
Tan
F
, et al
.
Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer
.
NPJ Digit Med
.
2019
;
2
(
1
):
48
10
.
21.
Van der Laak
J
,
Litjens
G
,
Ciompi
F
.
Deep learning in histopathology: the path to the clinic
.
Nat Med
.
2021
;
27
(
5
):
775
84
.
22.
Litjens
G
,
Bandi
P
,
Ehteshami Bejnordi
B
,
Geessink
O
,
Balkenhol
M
,
Bult
P
, et al
.
1399 H&E-stained sentinel lymph node sections of breast cancer patients: the CAMELYON dataset
.
GigaScience
.
2018
;
7
(
6
):
giy065
.
23.
Steiner
DF
,
MacDonald
R
,
Liu
Y
,
Truszkowski
P
,
Hipp
JD
,
Gammage
C
, et al
.
Impact of deep learning assistance on the histopathologic review of lymph nodes for metastatic breast cancer
.
Am J Surg Pathol
.
2018
;
42
(
12
):
1636
46
.
24.
Pham
HHN
,
Futakuchi
M
,
Bychkov
A
,
Furukawa
T
,
Kuroda
K
,
Fukuoka
J
.
Detection of lung cancer lymph node metastases from whole-slide histopathologic images using a two-step deep learning approach
.
Am J Pathol
.
2019
;
189
(
12
):
2428
39
.
25.
Matsushima
J
,
Sato
T
,
Ohnishi
T
,
Yoshimura
Y
,
Mizutani
H
,
Koto
S
, et al
.
The use of deep learning-based computer diagnostic algorithm for detection of lymph node metastases of gastric adenocarcinoma
.
Int J Surg Pathol
.
2023
;
31
(
6
):
975
81
.
26.
Brockmoeller
S
,
Echle
A
,
Ghaffari Laleh
N
,
Eiholm
S
,
Malmstrøm
ML
,
Plato Kuhlmann
T
, et al
.
Deep learning identifies inflamed fat as a risk factor for lymph node metastasis in early colorectal cancer
.
J Pathol
.
2022
;
256
(
3
):
269
81
.
27.
Song
JH
,
Hong
Y
,
Kim
ER
,
Kim
SH
,
Sohn
I
.
Utility of artificial intelligence with deep learning of hematoxylin and eosin-stained whole slide images to predict lymph node metastasis in T1 colorectal cancer using endoscopically resected specimens
.
J Gastroenterol
.
2022
;
57
(
9
):
654
6
.
28.
Pan
Y
,
Sun
Z
,
Wang
W
,
Yang
Z
,
Jia
J
,
Feng
X
, et al
.
Automatic detection of squamous cell carcinoma metastasis in esophageal lymph nodes using semantic segmentation
.
Clin Transl Med
.
2020
;
10
(
3
):
e129
.
29.
Shaish
H
,
Mutasa
S
,
Makkar
J
,
Chang
P
,
Schwartz
L
,
Ahmed
F
.
Prediction of lymph node maximum standardized uptake value in patients with cancer using a 3D convolutional neural network: a proof-of-concept study
.
AJR Am J Roentgenol
.
2019
;
212
(
2
):
238
44
.
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