ArticleScientific reports2020
SHIFT: speedy histological-to-immunofluorescent translation of a tumor signature enabled by deep learning.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 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
34 citing papers in PubMed, 68 citations in OpenAlex.
- Breast Cancer Hormone Receptor Status Determination from H&E-Stained Biopsy Images Using Pixel-Level Classifiers.Cancers · 2026Article
- Dual-Modal Autofluorescence Microscopy for CycleGAN-Based H&E-like Image Generation in Liver Tissue: A Preliminary Proof-of-Concept Study.Diagnostics (Basel, Switzerland) · 2026Article
- DGAT: a dual-graph attention network for inferring spatial protein landscapes from transcriptomics.Nature communications · 2026Article
- miniMTI: minimal multiplex tissue imaging enhances biomarker expression prediction from histology.Research square · 2026Article
- Artificial intelligence in digital pathology diagnosis and analysis: technologies, challenges, and future prospects.Military Medical Research · 2026Review
- A robust and scalable framework for hallucination detection in virtual tissue staining and digital pathology.Nature biomedical engineering · 2025Article
- Spatial proteomics for investigating solid tumor resistance mechanisms.Cancer metastasis reviews · 2025Review
- Scalable, trustworthy generative model for virtual multi-staining from H&E whole slide images.PLoS computational biology · 2025Article
- Toward universal immunofluorescence normalization for multiplex tissue imaging with UniFORM.Cell reports methods · 2025Article
- ROSIE: AI generation of multiplex immunofluorescence staining from histopathology images.Nature communications · 2025Article
- COEXIST: Coordinated single-cell integration of serial multiplexed tissue images.PLoS computational biology · 2025Article
- Addressing persistent challenges in digital image analysis of cancer tissue: resources developed from a hackathon.Molecular oncology · 2025Review
- Histopathology-based protein multiplex generation using deep learning.Nature machine intelligence · 2025Article
- Autofluorescence Virtual Staining System for H&E Histology and Multiplex Immunofluorescence Applied to Immuno-Oncology Biomarkers in Lung Cancer.Cancer research communications · 2025Article
- Virtual multiplex immunofluorescence identifies lymphocyte subsets predictive of response to neoadjuvant therapy.Therapeutic advances in medical oncology · 2025Article
- VS-FPM: Large-Format, Label-Free Virtual Histopathology Microscopy.BME frontiers · 2025Article
- Single color digital H&E staining with In-and-Out Net.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2024Article
- Defining precancer: a grand challenge for the cancer community.Nature reviews. Cancer · 2024Review
- A Pan-Cancer Patient-Derived Xenograft Histology Image Repository with Genomic and Pathologic Annotations Enables Deep Learning Analysis.Cancer research · 2024Article
- MYC Deregulation and PTEN Loss Model Tumor and Stromal Heterogeneity of Aggressive Triple-Negative Breast Cancer.Nature communications · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 1 institution in 1 country.
Funding
Abstract
Spatially-resolved molecular profiling by immunostaining tissue sections is a key feature in cancer diagnosis, subtyping, and treatment, where it complements routine histopathological evaluation by clarifying tumor phenotypes. In this work, we present a deep learning-based method called speedy histological-to-immunofluorescent translation (SHIFT) which takes histologic images of hematoxylin and eosin (H&E)-stained tissue as input, then in near-real time returns inferred virtual immunofluorescence (IF) images that estimate the underlying distribution of the tumor cell marker pan-cytokeratin (panCK). To build a dataset suitable for learning this task, we developed a serial staining protocol which allows IF and H&E images from the same tissue to be spatially registered. We show that deep learning-extracted morphological feature representations of histological images can guide representative sample selection, which improved SHIFT generalizability in a small but heterogenous set of human pancreatic cancer samples. With validation in larger cohorts, SHIFT could serve as an efficient preliminary, auxiliary, or substitute for panCK IF by delivering virtual panCK IF images for a fraction of the cost and in a fraction of the time required by traditional IF.
Indexed as
Identifiers
What OpenQuestion holds
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.