Evidence map›Paper›PMID 29292031›Full record

ArticleEBioMedicine2018

Deep Convolutional Neural Networks Enable Discrimination of Heterogeneous Digital Pathology Images.

Pegah Khosravi, Ehsan Kazemi, Marcin Imielinski, Olivier Elemento, Iman Hajirasouliha

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in EBioMedicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 144 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
144citing papers in PubMed, 1 pooled it
22.6field-weighted citation impact, top 1% of its field
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

144 citing papers in PubMed, 1 synthesis or guideline pooled it, 331 citations in OpenAlex.

  1. Pooled it
  2. MRE11 as a Predictive Biomarker of Outcome After Radiation Therapy in Bladder Cancer.International journal of radiation oncology, biology, physics · 2019
    Trial
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  5. Current trends and future directions of artificial intelligence in lung cancer diagnosis.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2026
    Article
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84 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 3 institutions in 2 countries.

Pegah KhosraviInstitute for Computational Biomedicine, Weill Cornell Medical College, NY, USA; Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA.
Ehsan KazemiYale Institute for Network Science, Yale University, New Haven, CT, USA.
Marcin ImielinskiCaryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medical College, NY, USA; Department of Pathology and Laboratory Medicine, Weill Cornell Medical College, NY, USA; The New York Genome Center, NY, USA; The Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA.
Olivier ElementoInstitute for Computational Biomedicine, Weill Cornell Medical College, NY, USA; Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA; Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medical College, NY, USA; The Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA.
Iman HajirasoulihaInstitute for Computational Biomedicine, Weill Cornell Medical College, NY, USA; Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA; Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medical College, NY, USA; The Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA.
Lander Institute · ILWeill Cornell Medicine · USYale University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Pathological evaluation of tumor tissue is pivotal for diagnosis in cancer patients and automated image analysis approaches have great potential to increase precision of diagnosis and help reduce human error. In this study, we utilize several computational methods based on convolutional neural networks (CNN) and build a stand-alone pipeline to effectively classify different histopathology images across different types of cancer. In particular, we demonstrate the utility of our pipeline to discriminate between two subtypes of lung cancer, four biomarkers of bladder cancer, and five biomarkers of breast cancer. In addition, we apply our pipeline to discriminate among four immunohistochemistry (IHC) staining scores of bladder and breast cancers. Our classification pipeline includes a basic CNN architecture, Google's Inceptions with three training strategies, and an ensemble of two state-of-the-art algorithms, Inception and ResNet. Training strategies include training the last layer of Google's Inceptions, training the network from scratch, and fine-tunning the parameters for our data using two pre-trained version of Google's Inception architectures, Inception-V1 and Inception-V3. We demonstrate the power of deep learning approaches for identifying cancer subtypes, and the robustness of Google's Inceptions even in presence of extensive tumor heterogeneity. On average, our pipeline achieved accuracies of 100%, 92%, 95%, and 69% for discrimination of various cancer tissues, subtypes, biomarkers, and scores, respectively. Our pipeline and related documentation is freely available at https://github.com/ih-_lab/CNN_Smoothie.

Indexed as

AlgorithmsImage Processing, Computer-AssistedNeural Networks, ComputerHumansNeoplasmsROC CurveBiomarkersClassificationConvolutional Neural NetworkDeep learningDigital pathology imagingTumor heterogeneity

Identifiers

PMID29292031
PMCPMC5828543
OpenAlexW2949648625

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.