Creating a More Welcoming Home for Your Work at The American Journal of Pathology

When Martha Furie became Editor-in-Chief of The American Journal of Pathology (AJP) in 2018, her target was for AJP to achieve an Impact Factor of greater than 5.0 by the end of her five-year term. We were delighted to learn this past June that we made it! The most recent Impact Factor is 5.77, and AJP remains the most highly cited journal in the field of pathology. This goal was attained through the dedicated efforts of the AJP team; the contribution of high-quality, original research by AJP's authors; and an emphasis on increasing the number of timely review articles and theme issues. Moreover, a new topic category called “Machine Learning, Computational Pathology, and Biophysical Imaging” was added to AJP in 20191Cohen S. Furie M.B. Artificial intelligence and pathobiology join forces in The American Journal of Pathology.Am J Pathol. 2019; 189: 4-5Abstract Full Text Full Text PDF PubMed Scopus (8) Google Scholar at the suggestion of Dr. Stanley Cohen, a past President of the American Society for Investigative Pathology (ASIP). Under Dr. Cohen's guidance as an Associate Editor, this has become the Journal's most rapidly growing category. In the January 2023 issue alone, four articles present cutting-edge research applying artificial intelligence to the classification of osteosarcoma,2Patkar S. Beck J. Harmon S. Mazcko C. Turkbey B. Choyke P. Brown G.T. LeBlanc A. Deep domain adversarial learning for species-agnostic classification of histologic subtypes of osteosarcoma.Am J Pathol. 2023; 193: 60-72Abstract Full Text Full Text PDF Scopus (2) Google Scholar analysis of kidney histopathology,3Bouteldja N. Hölscher D.L. Klinkhammer B.M. Buelow R.D. Lotz J. Weiss N. Daniel C. Amann K. Boor P. Stain-independent deep learning–based analysis of digital kidney histopathology.Am J Pathol. 2023; 193: 73-83Abstract Full Text Full Text PDF PubMed Scopus (3) Google Scholar and diagnosis of urothelial lesions4Nishikawa T. Matsuzaki I. Ryuta I. Musangile F.Y. Sagan K. Nishikawa M. Mikasa Y. Takahashi Y. Kojima F. Murata S. Use of artificial intelligence for the interpretable prediction of the pathological diagnosis and molecular abnormalities of flat urothelial lesions.Am J Pathol. 2023; 193: 39-50Abstract Full Text Full Text PDF Scopus (1) Google Scholar and a spectrum of cancers.5Zhang H. Qureshi M.A. Wahid M. Charifa A. Ehsan A. Ip A. De Dois I. Ma W. Sharma I. McCloskey J. Donato M. Siegel D. Gutierrez M. Pecora A. Goy A. Albitar M. Differential diagnosis of hematologic and solid tumors using targeted transcriptome and artificial intelligence.Am J Pathol. 2023; 193: 51-59Abstract Full Text Full Text PDF Scopus (5) Google Scholar The editors and staff of AJP, along with ASIP's Publications Committee, also meet periodically to discuss ways to keep the Journal on an upward trajectory and best meet the needs of its authors. One of these brain-storming sessions sparked a review of AJP's formatting requirements, which originate from the days when readers were likely to pick up a printed issue and browse the contents. Back then, it made sense to have a uniform look to the articles across the full issue. But it is less important in this digital age, since content utilization has become much more article-based, rather than issue-based. A thorough review of AJP's stylistic requirements was therefore undertaken, which culminated in the elimination of many cosmetic parameters that are not important to understanding the science being conveyed. Authors now have discretion regarding how statistics are presented, figures and images are labeled, and tables are annotated, providing them with much more flexibility. The updated Instructions for Authors were posted online in October 2022 (https://ajp.amjpathol.org/content/authorinfo), and a copy is included here in the January 2023 print issue. Simplifying these publication requirements will make both the submission and production processes easier and smoother for all parties. In addition to relaxing formatting restrictions, AJP has expanded to offer authors the opportunity to provide a graphical abstract along with their submission. These summary figures appear at the start of the online version of the article. Prospective authors also are reminded that AJP welcomes initial submissions in any format, as long as all of the information needed for expert evaluation is supplied. To further enhance the experience for authors, AJP's staff, Editor-in-Chief, and Associate Editors are continuing their efforts to expedite the review process as much as possible. In 2022, the time for initial editorial screening averaged 2.7 days, and average time from submission to first decision after external review was 33.8 days. Research in diseases of the eye has always been well-represented in the pages of AJP. Consequently, the Journal will be incorporating a new, dedicated topic category entitled “Ocular Pathobiology.” To mark this addition, an exciting theme issue, “Microbiome and Ocular Health: Insights and Perspectives,” is under development. This special issue will contain several review articles by leaders in the field, and an accompanying call for relevant original research papers was open from July to November 2022. The Editors look forward to bringing this excellent content to AJP's readers later in 2023. AJP is the flagship publication of ASIP, and we want the relationship between the Journal and the Society to be a strong, mutually beneficial one. To that end, publication charges for corresponding authors who are Regular Members of ASIP are discounted by nearly 50%. We hope that this very substantial benefit underscores how much AJP values its ASIP member authors and makes it a more affordable option as members consider homes for their research. And notably, all review articles are published without charge to the authors, regardless of membership status. Dr. Furie is honored to have been appointed to a second five-year term as Editor-in-Chief, beginning with this January 2023 issue. She looks forward to remaining part of the stellar AJP team, for it truly is a collective, collaborative endeavor. The accomplishments of the past five years would have been impossible without the contributions of ASIP's Publications Committee; Senior Associate Editors Bill Coleman and Rick Mitchell; AJP's dedicated Associate Editors and Editorial Board members; its expert external reviewers; and the tireless, devoted Editorial Office staff—Emily Essex (Director of Scientific Publications); Chhavi Chauhan (Director of Scientific Outreach); Henry Carter (Assistant Managing Editor); and Michael Dustin (Senior Editorial Assistant). Of course, the key to any journal's success is the strength of the papers that it publishes. The major objective for Dr. Furie's second term is to expand efforts to make AJP a more attractive, user-friendly place for the Journal's authors to showcase their work. To that end, we invite our readers and prospective authors to reach out to us at ajp@asip.org with questions; ideas for review articles or theme issues; or suggestions for improving our processes of submission, evaluation, and preparation of manuscripts for publication. The AJP team has worked hard to ensure that the Journal remains a top-cited, desirable destination for discoveries in basic and translational pathobiology. But there is always more to be done, and we will continue to strive to provide the best possible experience for AJP's authors. Deep Domain Adversarial Learning for Species-Agnostic Classification of Histologic Subtypes of OsteosarcomaThe American Journal of PathologyVol. 193Issue 1PreviewOsteosarcomas (OSs) are aggressive bone tumors with many divergent histologic patterns. During pathology review, OSs are subtyped based on the predominant histologic pattern; however, tumors often demonstrate multiple patterns. This high tumor heterogeneity coupled with scarcity of samples compared with other tumor types render histology-based prognosis of OSs challenging. To combat lower case numbers in humans, dogs with spontaneous OSs have been suggested as a model species. Herein, a convolutional neural network was adversarially trained to classify distinct histologic patterns of OS in humans using mostly canine OS data during training. Full-Text PDF Open AccessDifferential Diagnosis of Hematologic and Solid Tumors Using Targeted Transcriptome and Artificial IntelligenceThe American Journal of PathologyVol. 193Issue 1PreviewDiagnosis and classification of tumors is increasingly dependent on biomarkers. RNA expression profiling using next-generation sequencing provides reliable and reproducible information on the biology of cancer. This study investigated targeted transcriptome and artificial intelligence for differential diagnosis of hematologic and solid tumors. RNA samples from hematologic neoplasms (N = 2606), solid tumors (N = 2038), normal bone marrow (N = 782), and lymph node control (N = 24) were sequenced using next-generation sequencing using a targeted 1408-gene panel. Full-Text PDF Open AccessStain-Independent Deep Learning–Based Analysis of Digital Kidney HistopathologyThe American Journal of PathologyVol. 193Issue 1PreviewConvolutional neural network (CNN)-based image analysis applications in digital pathology (eg, tissue segmentation) require a large amount of annotated data and are mostly trained and applicable on a single stain. Here, a novel concept based on stain augmentation is proposed to develop stain-independent CNNs requiring only one annotated stain. In this benchmark study on stain independence in digital pathology, this approach is comprehensively compared with state-of-the-art techniques including image registration and stain translation, and several modifications thereof. Full-Text PDF Use of Artificial Intelligence for the Interpretable Prediction of the Pathologic Diagnosis and Molecular Abnormalities of Flat Urothelial LesionsThe American Journal of PathologyVol. 193Issue 1PreviewFlat urothelial lesions are important because of their potential for carcinogenesis and development into invasive urothelial carcinomas. However, it is difficult for pathologists to detect early flat urothelial changes and accurately diagnose flat urothelial lesions. To predict the pathologic diagnosis and molecular abnormalities of flat urothelial lesions from pathologic images, artificial intelligence with an interpretable method was used. Next-generation sequencing on 110 hematoxylin and eosin–stained slides of normal urothelium and flat urothelial lesions, including atypical urothelium, dysplasia, and carcinoma in situ, detected 17 types of molecular abnormalities. Full-Text PDF