Artificial Intelligence Helps Pathologists Increase Diagnostic Accuracy and Efficiency in the Detection of Breast Cancer Lymph Node Metastases.

The detection of lymph node metastases is essential for breast cancer staging, although it is a tedious and time-consuming task where the sensitivity of pathologists is suboptimal. Artificial intelligence (AI) can help pathologists detect lymph node metastases, which could help alleviate workload issues. We studied how pathologists' performance varied when aided by AI. An AI algorithm was trained using more than 32 000 breast sentinel lymph node whole slide images (WSIs) matched with their corresponding pathology reports from more than 8000 patients. The algorithm highlighted areas suspicious of harboring metastasis. Three pathologists were asked to review a dataset comprising 167 breast sentinel lymph node WSIs, of which 69 harbored cancer metastases of different sizes, enriched for challenging cases. Ninety-eight slides were benign. The pathologists read the dataset twice, both digitally, with and without AI assistance, randomized for slide and reading orders to reduce bias, separated by a 3-week washout period. Their slide-level diagnosis was recorded, and they were timed during their reads. The average reading time per slide was 129 seconds during the unassisted phase versus 58 seconds during the AI-assisted phase, resulting in an overall efficiency gain of 55% ( P <0.001). These efficiency gains are applied to both benign and malignant WSIs. Two of the 3 reading pathologists experienced significant sensitivity improvements, from 74.5% to 93.5% ( P ≤0.006). This study highlights that AI can help pathologists shorten their reading times by more than half and also improve their metastasis detection rate.

Retamero JA ，Gulturk E ，Bozkurt A ，Liu S ，Gorgan M ，Moral L ，Horton M ，Parke A ，Malfroid K ，Sue J ，Rothrock B ，Oakley G ，DeMuth G ，Millar E ，Fuchs TJ ，Klimstra DS ... -  《-》

Artificial Intelligence-Aided Diagnosis of Breast Cancer Lymph Node Metastasis on Histologic Slides in a Digital Workflow.

Identifying lymph node (LN) metastasis in invasive breast carcinoma can be tedious and time-consuming. We investigated an artificial intelligence (AI) algorithm to detect LN metastasis by screening hematoxylin and eosin (H&E) slides in a clinical digital workflow. The study included 2 sentinel LN (SLN) cohorts (a validation cohort with 234 SLNs and a consensus cohort with 102 SLNs) and 1 nonsentinel LN cohort (258 LNs enriched with lobular carcinoma and postneoadjuvant therapy cases). All H&E slides were scanned into whole slide images in a clinical digital workflow, and whole slide images were automatically batch-analyzed using the Visiopharm Integrator System (VIS) metastasis AI algorithm. For the SLN validation cohort, the VIS metastasis AI algorithm detected all 46 metastases, including 19 macrometastases, 26 micrometastases, and 1 with isolated tumor cells with a sensitivity of 100%, specificity of 41.5%, positive predictive value of 29.5%, and negative predictive value (NPV) of 100%. The false positivity was caused by histiocytes (52.7%), crushed lymphocytes (18.2%), and others (29.1%), which were readily recognized during pathologists' reviews. For the SLN consensus cohort, 3 pathologists examined all VIS AI annotated H&E slides and cytokeratin immunohistochemistry slides with similar average concordance rates (99% for both modalities). However, the average time consumed by pathologists using VIS AI annotated slides was significantly less than using immunohistochemistry slides (0.6 vs 1.0 minutes, P = .0377). For the nonsentinel LN cohort, the AI algorithm detected all 81 metastases, including 23 from lobular carcinoma and 31 from postneoadjuvant chemotherapy cases, with a sensitivity of 100%, specificity of 78.5%, positive predictive value of 68.1%, and NPV of 100%. The VIS AI algorithm showed perfect sensitivity and NPV in detecting LN metastasis and less time consumed, suggesting its potential utility as a screening modality in routine clinical digital pathology workflow to improve efficiency.

Challa B ，Tahir M ，Hu Y ，Kellough D ，Lujan G ，Sun S ，Parwani AV ，Li Z ... -  《-》

Artificial intelligence-based model for lymph node metastases detection on whole slide images in bladder cancer: a retrospective, multicentre, diagnostic study.

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. 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). 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. 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. 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.

Wu S ，Hong G ，Xu A ，Zeng H ，Chen X ，Wang Y ，Luo Y ，Wu P ，Liu C ，Jiang N ，Dang Q ，Yang C ，Liu B ，Shen R ，Chen Z ，Liao C ，Lin Z ，Wang J ，Lin T ... -  《-》

Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer.

Ehteshami Bejnordi B ，Veta M ，Johannes van Diest P ，van Ginneken B ，Karssemeijer N ，Litjens G ，van der Laak JAWM ，the CAMELYON16 Consortium ，Hermsen M ，Manson QF ，Balkenhol M ，Geessink O ，Stathonikos N ，van Dijk MC ，Bult P ，Beca F ，Beck AH ，Wang D ，Khosla A ，Gargeya R ，Irshad H ，Zhong A ，Dou Q ，Li Q ，Chen H ，Lin HJ ，Heng PA ，Haß C ，Bruni E ，Wong Q ，Halici U ，Öner MÜ ，Cetin-Atalay R ，Berseth M ，Khvatkov V ，Vylegzhanin A ，Kraus O ，Shaban M ，Rajpoot N ，Awan R ，Sirinukunwattana K ，Qaiser T ，Tsang YW ，Tellez D ，Annuscheit J ，Hufnagl P ，Valkonen M ，Kartasalo K ，Latonen L ，Ruusuvuori P ，Liimatainen K ，Albarqouni S ，Mungal B ，George A ，Demirci S ，Navab N ，Watanabe S ，Seno S ，Takenaka Y ，Matsuda H ，Ahmady Phoulady H ，Kovalev V ，Kalinovsky A ，Liauchuk V ，Bueno G ，Fernandez-Carrobles MM ，Serrano I ，Deniz O ，Racoceanu D ，Venâncio R ... -  《-》

Clinical implementation of artificial-intelligence-assisted detection of breast cancer metastases in sentinel lymph nodes: the CONFIDENT-B single-center, non-randomized clinical trial.

Pathologists' assessment of sentinel lymph nodes (SNs) for breast cancer (BC) metastases is a treatment-guiding yet labor-intensive and costly task because of the performance of immunohistochemistry (IHC) in morphologically negative cases. This non-randomized, single-center clinical trial (International Standard Randomized Controlled Trial Number:14323711) assessed the efficacy of an artificial intelligence (AI)-assisted workflow for detecting BC metastases in SNs while maintaining diagnostic safety standards. From September 2022 to May 2023, 190 SN specimens were consecutively enrolled and allocated biweekly to the intervention arm (n = 100) or control arm (n = 90). In both arms, digital whole-slide images of hematoxylin-eosin sections of SN specimens were assessed by an expert pathologist, who was assisted by the 'Metastasis Detection' app (Visiopharm) in the intervention arm. Our primary endpoint showed a significantly reduced adjusted relative risk of IHC use (0.680, 95% confidence interval: 0.347-0.878) for AI-assisted pathologists, with subsequent cost savings of ~3,000 €. Secondary endpoints showed significant time reductions and up to 30% improved sensitivity for AI-assisted pathologists. This trial demonstrates the safety and potential for cost and time savings of AI assistance.

van Dooijeweert C ，Flach RN ，Ter Hoeve ND ，Vreuls CPH ，Goldschmeding R ，Freund JE ，Pham P ，Nguyen TQ ，van der Wall E ，Frederix GWJ ，Stathonikos N ，van Diest PJ ... -  《Nature Cancer》