Evidence map›Paper›PMID 30387364›Full record

ArticleThe British journal of radiology2019

Development and validation of a decision support tool to select IMRT as radiotherapy treatment planning modality for patients with locoregionally advanced non-small cell lung cancers (NSCLC).

Raj Kumar Shrimali, Santam Chakraborty, Tapesh Bhattacharyya, Indranil Mallick, Rimpa Basu Achari, Sriram Prasath, B Arun, Anurupa Mahata, M Vidhya Shree, E Vishnupriya and 1 more

Abstract readValidation Study
In one paragraph

Article in The British journal of radiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. Article
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

11 authors.

Raj Kumar Shrimali1 Department of Radiation Oncology, Tata Medical Center , Rajarhat, Kolkata , India.
Santam Chakraborty1 Department of Radiation Oncology, Tata Medical Center , Rajarhat, Kolkata , India.ORCID http://orcid.org/0000-0003-3580-5979
Tapesh Bhattacharyya1 Department of Radiation Oncology, Tata Medical Center , Rajarhat, Kolkata , India.
Indranil Mallick1 Department of Radiation Oncology, Tata Medical Center , Rajarhat, Kolkata , India.
Rimpa Basu Achari1 Department of Radiation Oncology, Tata Medical Center , Rajarhat, Kolkata , India.
Sriram Prasath1 Department of Radiation Oncology, Tata Medical Center , Rajarhat, Kolkata , India.
B Arun1 Department of Radiation Oncology, Tata Medical Center , Rajarhat, Kolkata , India.
Anurupa Mahata1 Department of Radiation Oncology, Tata Medical Center , Rajarhat, Kolkata , India.
M Vidhya Shree1 Department of Radiation Oncology, Tata Medical Center , Rajarhat, Kolkata , India.
E Vishnupriya1 Department of Radiation Oncology, Tata Medical Center , Rajarhat, Kolkata , India.
Sanjoy Chatterjee1 Department of Radiation Oncology, Tata Medical Center , Rajarhat, Kolkata , India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objective: Radiation planning for locally-advanced non-small cell lung cancer (NSCLC) can be time-consuming and iterative. Many cases cannot be planned satisfactorily using multisegment three-dimensional conformal radiotherapy (3DCRT). We sought to develop and validate a predictive model which could estimate the probability that acceptable target volume coverage would need intensity modulated radiotherapy (IMRT).

methods: Variables related to the planning target volume (PTV) and topography were identified heuristically. These included the PTV, it's craniocaudal extent, the ratio of PTV to total lung volume, distance of the centroid of the PTV from the spinal canal, and the extent PTV crossed the midline. Metrics were chosen such that they could be measured objectively, quickly and reproducibly. A logistic regression model was trained and validated on 202 patients with NSCLC. A group of patients who had both complex 3DCRT and IMRT planned was then used to derive the utility of the use of such a model in the clinic based on the time taken for planning such complex 3DCRT.

results: Of the 202 patients, 93 received IMRT, as they had larger volumes crossing midline. The final model showed a good rank discrimination (Harrell's C-index 0.84) and low calibration error (mean absolute error of 0.014). Predictive accuracy in an external dataset was 92%. The final model was presented as a nomogram. Using this model, the dosimetrist can save a median planning time of 168 min per case.

conclusion: We developed and validated a data-driven, decision aid which can reproducibly determine the best planning technique for locally-advanced NSCLC. ADVANCES IN KNOWLEDGE:: Our validated, data-driven decision aid can help the planner to determine the need for IMRT in locally advanced NSCLC saving significant planning time in the process.

Indexed as

Decision Support TechniquesRadiotherapy, Intensity-ModulatedCarcinoma, Non-Small-Cell LungDecision Support Systems, ClinicalHumansLung NeoplasmsRadiotherapy, ConformalRadiotherapy Planning, Computer-Assisted

Identifiers

PMID30387364
PMCPMC6404834

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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.