AI-guided histopathology predicts brain metastasis in lung cancer patients.

Brain metastases can occur in nearly half of patients with early and locally advanced (stage I-III) non-small cell lung cancer (NSCLC). There are no reliable histopathologic or molecular means to identify those who are likely to develop brain metastases. We sought to determine if deep learning (DL) could be applied to routine H&E-stained primary tumor tissue sections from stage I-III NSCLC patients to predict the development of brain metastasis. Diagnostic slides from 158 patients with stage I-III NSCLC followed for at least 5 years for the development of brain metastases (Met+, 65 patients) versus no progression (Met-, 93 patients) were subjected to whole-slide imaging. Three separate iterations were performed by first selecting 118 cases (45 Met+, 73 Met-) to train and validate the DL algorithm, while 40 separate cases (20 Met+, 20 Met-) were used as the test set. The DL algorithm results were compared to a blinded review by four expert pathologists. The DL-based algorithm was able to distinguish the eventual development of brain metastases with an accuracy of 87% (p < 0.0001) compared with an average of 57.3% by the four pathologists and appears to be particularly useful in predicting brain metastases in stage I patients. The DL algorithm appears to focus on a complex set of histologic features. DL-based algorithms using routine H&E-stained slides may identify patients who are likely to develop brain metastases from those who will remain disease free over extended (>5 year) follow-up and may thus be spared systemic therapy. © 2024 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Zhou H ，Watson M ，Bernadt CT ，Lin SS ，Lin CY ，Ritter JH ，Wein A ，Mahler S ，Rawal S ，Govindan R ，Yang C ，Cote RJ ... -  《-》

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 ... -  《-》

Independent real-world application of a clinical-grade automated prostate cancer detection system.

Artificial intelligence (AI)-based systems applied to histopathology whole-slide images have the potential to improve patient care through mitigation of challenges posed by diagnostic variability, histopathology caseload, and shortage of pathologists. We sought to define the performance of an AI-based automated prostate cancer detection system, Paige Prostate, when applied to independent real-world data. The algorithm was employed to classify slides into two categories: benign (no further review needed) or suspicious (additional histologic and/or immunohistochemical analysis required). We assessed the sensitivity, specificity, positive predictive values (PPVs), and negative predictive values (NPVs) of a local pathologist, two central pathologists, and Paige Prostate in the diagnosis of 600 transrectal ultrasound-guided prostate needle core biopsy regions ('part-specimens') from 100 consecutive patients, and to ascertain the impact of Paige Prostate on diagnostic accuracy and efficiency. Paige Prostate displayed high sensitivity (0.99; CI 0.96-1.0), NPV (1.0; CI 0.98-1.0), and specificity (0.93; CI 0.90-0.96) at the part-specimen level. At the patient level, Paige Prostate displayed optimal sensitivity (1.0; CI 0.93-1.0) and NPV (1.0; CI 0.91-1.0) at a specificity of 0.78 (CI 0.64-0.89). The 27 part-specimens considered by Paige Prostate as suspicious, whose final diagnosis was benign, were found to comprise atrophy (n = 14), atrophy and apical prostate tissue (n = 1), apical/benign prostate tissue (n = 9), adenosis (n = 2), and post-atrophic hyperplasia (n = 1). Paige Prostate resulted in the identification of four additional patients whose diagnoses were upgraded from benign/suspicious to malignant. Additionally, this AI-based test provided an estimated 65.5% reduction of the diagnostic time for the material analyzed. Given its optimal sensitivity and NPV, Paige Prostate has the potential to be employed for the automated identification of patients whose histologic slides could forgo full histopathologic review. In addition to providing incremental improvements in diagnostic accuracy and efficiency, this AI-based system identified patients whose prostate cancers were not initially diagnosed by three experienced histopathologists. © 2021 The Authors. The Journal of Pathology published by John Wiley & Sons, Ltd. on behalf of The Pathological Society of Great Britain and Ireland.

da Silva LM ，Pereira EM ，Salles PG ，Godrich R ，Ceballos R ，Kunz JD ，Casson A ，Viret J ，Chandarlapaty S ，Ferreira CG ，Ferrari B ，Rothrock B ，Raciti P ，Reuter V ，Dogdas B ，DeMuth G ，Sue J ，Kanan C ，Grady L ，Fuchs TJ ，Reis-Filho JS ... -  《-》

Development and validation of a supervised deep learning algorithm for automated whole-slide programmed death-ligand 1 tumour proportion score assessment in non-small cell lung cancer.

Immunohistochemical programmed death-ligand 1 (PD-L1) staining to predict responsiveness to immunotherapy in patients with advanced non-small cell lung cancer (NSCLC) has several drawbacks: a robust gold standard is lacking, and there is substantial interobserver and intraobserver variance, with up to 20% discordance around cutoff points. The aim of this study was to develop a new deep learning-based PD-L1 tumour proportion score (TPS) algorithm, trained and validated on a routine diagnostic dataset of digitised PD-L1 (22C3, laboratory-developed test)-stained samples. We designed a fully supervised deep learning algorithm for whole-slide PD-L1 assessment, consisting of four sequential convolutional neural networks (CNNs), using aiforia create software. We included 199 whole slide images (WSIs) of 'routine diagnostic' histology samples from stage IV NSCLC patients, and trained the algorithm by using a training set of 60 representative cases. We validated the algorithm by comparing the algorithm TPS with the reference score in a held-out validation set. The algorithm had similar concordance with the reference score (79%) as the pathologists had with one another (75%). The intraclass coefficient was 0.96 and Cohen's κ coefficient was 0.69 for the algorithm. Around the 1% and 50% cutoff points, concordance was also similar between pathologists and the algorithm. We designed a new, deep learning-based PD-L1 TPS algorithm that is similarly able to assess PD-L1 expression in daily routine diagnostic cases as pathologists. Successful validation on routine diagnostic WSIs and detailed visual feedback show that this algorithm meets the requirements for functioning as a 'scoring assistant'.

Hondelink LM ，Hüyük M ，Postmus PE ，Smit VTHBM ，Blom S ，von der Thüsen JH ，Cohen D ... -  《-》

Nonsmall cell lung cancer presenting with synchronous solitary brain metastasis.

Hu C ，Chang EL ，Hassenbusch SJ 3rd ，Allen PK ，Woo SY ，Mahajan A ，Komaki R ，Liao Z ... -  《CANCER》