Evidence map›Paper›PMID 29940810›Full record

ReviewTechnology in cancer research & treatment2018

Radiomics for Response and Outcome Assessment for Non-Small Cell Lung Cancer.

Liting Shi, Yaoyao He, Zilong Yuan, Stanley Benedict, Richard Valicenti, Jianfeng Qiu, Yi Rong

Open access · goldAbstract readReview
In one paragraph

Review in Technology in cancer research & treatment, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
64citing papers in PubMed, 1 pooled it
7.7field-weighted citation impact, top 2% 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

64 citing papers in PubMed, 1 synthesis or guideline pooled it, 95 citations in OpenAlex.

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4 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

7 authors at 3 institutions in 2 countries.

Liting Shi1 Department of Radiology, Taishan Medical University, Tai'an, China.
Yaoyao He1 Department of Radiology, Taishan Medical University, Tai'an, China.
Zilong Yuan2 Department of Radiology, Hubei Cancer Hospital, Wuhan, China.
Stanley Benedict3 Department of Radiation Oncology, University of California Davis Comprehensive Cancer Center, Sacramento, CA, USA.
Richard Valicenti3 Department of Radiation Oncology, University of California Davis Comprehensive Cancer Center, Sacramento, CA, USA.
Jianfeng Qiu1 Department of Radiology, Taishan Medical University, Tai'an, China.
Yi Rong3 Department of Radiation Oncology, University of California Davis Comprehensive Cancer Center, Sacramento, CA, USA.ORCID 0000-0002-2620-1893
Taishan Medical University · CNUniversity of California Davis Medical Center · USHubei Cancer Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Routine follow-up visits and radiographic imaging are required for outcome evaluation and tumor recurrence monitoring. Yet more personalized surveillance is required in order to sufficiently address the nature of heterogeneity in nonsmall cell lung cancer and possible recurrences upon completion of treatment. Radiomics, an emerging noninvasive technology using medical imaging analysis and data mining methodology, has been adopted to the area of cancer diagnostics in recent years. Its potential application in response assessment for cancer treatment has also drawn considerable attention. Radiomics seeks to extract a large amount of valuable information from patients' medical images (both pretreatment and follow-up images) and quantitatively correlate image features with diagnostic and therapeutic outcomes. Radiomics relies on computers to identify and analyze vast amounts of quantitative image features that were previously overlooked, unmanageable, or failed to be identified (and recorded) by human eyes. The research area has been focusing on the predictive accuracy of pretreatment features for outcome and response and the early discovery of signs of tumor response, recurrence, distant metastasis, radiation-induced lung injury, death, and other outcomes, respectively. This review summarized the application of radiomics in response assessments in radiotherapy and chemotherapy for non-small cell lung cancer, including image acquisition/reconstruction, region of interest definition/segmentation, feature extraction, and feature selection and classification. The literature search for references of this article includes PubMed peer-reviewed publications over the last 10 years on the topics of radiomics, textural features, radiotherapy, chemotherapy, lung cancer, and response assessment. Summary tables of radiomics in response assessment and treatment outcome prediction in radiation oncology have been developed based on the comprehensive review of the literature.

Indexed as

Carcinoma, Non-Small-Cell LungData MiningDiagnostic ImagingHumansImage Interpretation, Computer-AssistedLung NeoplasmsOutcome Assessment, Health CarePrecision MedicineRadiation OncologychemotherapyNSCLCradiomicsradiotherapyresponse assessmentsystemic therapy

Identifiers

PMID29940810
PMCPMC6048673
OpenAlexW2809785120

What OpenQuestion holds

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LicenceCC BY-NC
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.