ReviewTransplantation2019
Enhancing the Value of Histopathological Assessment of Allograft Biopsy Monitoring.
Review in Transplantation, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed.
- Multidimensional deep learning for grading and prognostic assessment of intrahepatic mass-forming cholangiocarcinoma.Insights into imaging · 2026Article
- Artificial intelligence in small tissue biopsies: diagnostic applications, histochemical integration, and methodological challenges in surgical pathology.Histochemistry and cell biology · 2026Review
- MRI-based quantification of intratumoral heterogeneity for intrahepatic mass-forming cholangiocarcinoma grading: a multicenter study.Insights into imaging · 2025Article
- Transitioning of renal transplant pathology from allograft to xenograft and tissue engineering pathology: Are we prepared?World journal of transplantation · 2023Review
- Not only a small liver - The pathologist's perspective in the pediatric liver transplant setting.Pathologica · 2022Review
- The role of immunosuppression in long-term graft hepatitis and fibrosis after paediatric liver transplant - comparison of two treatment protocols.Frontiers in transplantation · 2022Article
- Adaptation of Imaging Mass Cytometry to Explore the Single Cell Alloimmune Landscape of Liver Transplant Rejection.Frontiers in immunology · 2022Article
- Artificial intelligence and algorithmic computational pathology: an introduction with renal allograft examples.Histopathology · 2021Review
- Commentary: The Digital Fate of Glomeruli in Renal Biopsy.Journal of pathology informatics · 2021Article
- Advantages of Using a Web-based Digital Platform for Kidney Preimplantation Biopsies.Journal of pathology informatics · 2021Article
- Machine learning to predict transplant outcomes: helpful or hype? A national cohort study.Transplant international : official journal of the European Society for Organ Transplantation · 2020Article
- Banff Digital Pathology Working Group: Going digital in transplant pathology.American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons · 2020Article
- Precision transplant pathology.Current opinion in organ transplantation · 2020Review
- The Landscape of Digital Pathology in Transplantation: From the Beginning to the Virtual E-Slide.Journal of pathology informatics · 2019Review
- Whole Slide Imaging and Its Applications to Histopathological Studies of Liver Disorders.Frontiers in medicine · 2019Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Traditional histopathological allograft biopsy evaluation provides, within hours, diagnoses, prognostic information, and mechanistic insights into disease processes. However, proponents of an array of alternative monitoring platforms, broadly classified as "invasive" or "noninvasive" depending on whether allograft tissue is needed, question the value proposition of tissue histopathology. The authors explore the pros and cons of current analytical methods relative to the value of traditional and illustrate advancements of next-generation histopathological evaluation of tissue biopsies. We describe the continuing value of traditional histopathological tissue assessment and "next-generation pathology (NGP)," broadly defined as staining/labeling techniques coupled with digital imaging and automated image analysis. Noninvasive imaging and fluid (blood and urine) analyses promote low-risk, global organ assessment, and "molecular" data output, respectively; invasive alternatives promote objective, "mechanistic" insights by creating gene lists with variably increased/decreased expression compared with steady state/baseline. Proponents of alternative approaches contrast their preferred methods with traditional histopathology and: (1) fail to cite the main value of traditional and NGP-retention of spatial and inferred temporal context available for innumerable objective analyses and (2) belie an unfamiliarity with the impact of advances in imaging and software-guided analytics on emerging histopathology practices. Illustrative NGP examples demonstrate the value of multidimensional data that preserve tissue-based spatial and temporal contexts. We outline a path forward for clinical NGP implementation where "software-assisted sign-out" will enable pathologists to conduct objective analyses that can be incorporated into their final reports and improve patient care.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.