Background
Accurate lymph node staging is important for the diagnosis and treatment of patients
with bladder cancer. We aimed to develop a lymph node metastases diagnostic model
(LNMDM) on whole slide images and to assess the clinical effect of an artificial intelligence-assisted
(AI) workflow.
Methods
In this retrospective, multicentre, diagnostic study in China, we included consecutive
patients with bladder cancer who had radical cystectomy and pelvic lymph node dissection,
and from whom whole slide images of lymph node sections were available, for model
development. We excluded patients with non-bladder cancer and concurrent surgery,
or low-quality images. Patients from two hospitals (Sun Yat-sen Memorial Hospital
of Sun Yat-sen University and Zhujiang Hospital of Southern Medical University, Guangzhou,
Guangdong, China) were assigned before a cutoff date to a training set and after the
date to internal validation sets for each hospital. Patients from three other hospitals
(the Third Affiliated Hospital of Sun Yat-sen University, Nanfang Hospital of Southern
Medical University, and the Third Affiliated Hospital of Southern Medical University,
Guangzhou, Guangdong, China) were included as external validation sets. A validation
subset of challenging cases from the five validation sets was used to compare performance
between the LNMDM and pathologists, and two other datasets (breast cancer from the
CAMELYON16 dataset and prostate cancer from the Sun Yat-sen Memorial Hospital of Sun
Yat-sen University) were collected for a multi-cancer test. The primary endpoint was
diagnostic sensitivity in the four prespecified groups (ie, the five validation sets,
a single-lymph-node test set, the multi-cancer test set, and the subset for a performance
comparison between the LNMDM and pathologists).
Findings
Between Jan 1, 2013 and Dec 31, 2021, 1012 patients with bladder cancer had radical
cystectomy and pelvic lymph node dissection and were included (8177 images and 20 954
lymph nodes). We excluded 14 patients (165 images) with concurrent non-bladder cancer
and also excluded 21 low-quality images. We included 998 patients and 7991 images
(881 [88%] men; 117 [12%] women; median age 64 years [IQR 56–72]; ethnicity data not
available; 268 [27%] with lymph node metastases) to develop the LNMDM. The area under
the curve (AUC) for accurate diagnosis of the LNMDM ranged from 0·978 (95% CI 0·960–0·996)
to 0·998 (0·996–1·000) in the five validation sets. Performance comparisons between
the LNMDM and pathologists showed that the diagnostic sensitivity of the model (0·983
[95% CI 0·941–0·998]) substantially exceeded that of both junior pathologists (0·906
[0·871–0·934]) and senior pathologists (0·947 [0·919–0·968]), and that AI assistance
improved sensitivity for both junior (from 0·906 without AI to 0·953 with AI) and
senior (from 0·947 to 0·986) pathologists. In the multi-cancer test, the LNMDM maintained
an AUC of 0·943 (95% CI 0·918–0·969) in breast cancer images and 0·922 (0·884–0·960)
in prostate cancer images. In 13 patients, the LNMDM detected tumour micrometastases
that had been missed by pathologists who had previously classified these patients’
results as negative. Receiver operating characteristic curves showed that the LNMDM
would enable pathologists to exclude 80–92% of negative slides while maintaining 100%
sensitivity in clinical application.
Interpretation
We developed an AI-based diagnostic model that did well in detecting lymph node metastases,
particularly micrometastases. The LNMDM showed substantial potential for clinical
applications in improving the accuracy and efficiency of pathologists’ work.
Funding
National Natural Science Foundation of China, the Science and Technology Planning
Project of Guangdong Province, the National Key Research and Development Programme
of China, and the Guangdong Provincial Clinical Research Centre for Urological Diseases.
