Disagreement of diameter and volume measurements for pulmonary nodule size estimation in CT lung cancer screening

Heuvelmans, Marjolein A; Walter, Joan; Vliegenthart, Rozemarijn; Ooijen, Peter M. A. van; Bock, Geertruida H. de; Koning, Harry J. de; Oudkerk, Matthijs

doi:10.1136/thoraxjnl-2017-210770

Cited by 69 publications

(47 citation statements)

References 7 publications

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“…Nodule volumetry provides a more accurate assessment for baseline size and subsequent growth than diameter measurements. 12 Nodule volume is the preferred method for evaluation in the BTS guidelines and recommended as a more accurate method in the latest Fleischner Society guideline. 13 It appears in several diagnostic algorithms but is insufficient in isolation.…”

Section: Introductionmentioning

confidence: 99%

Probability of cancer in lung nodules using sequential volumetric screening up to 12 months: the UKLS trial

Marcus

Duffy²,

Devaraj

et al. 2019

Thorax

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Word count Abstract: 249 2 Word count manuscript: 3378 Abstract Background Estimation of the clinical probability of malignancy in patients with pulmonary nodules will facilitate early diagnosis, determine optimum patient management strategies and reduce overall costs.Method Data from the UK Lung Cancer Screening (UKLS) trial were analysed. Multivariable logistic regression models were utilised to identify independent predictors and to develop a parsimonious model to estimate the probability of lung cancer in lung nodules detected at baseline, three month and twelve month repeat screening. ResultsOf 1994 participants that underwent CT scan, 1013 participants had a total of 5063 lung nodules and 52 (2.6%) of the participants developed lung cancer during a median followup of 4 years. Covariates that predict lung cancer in our model included female gender, asthma, bronchitis, asbestos exposure, history of previous cancer, early and late onset of family history of lung cancer, smoking duration, forced vital capacity, nodule type (pure ground glass and part solid) and volume as measured by semi-automated volumetry. The final model incorporating all predictors had excellent discrimination; area under the receiver-operating characteristic curve (AUC [95% CI] = 0.885 [0.880 to 0.889]). Internal validation suggested that the model will discriminate well when applied to new data (optimism-corrected AUC = 0.882 [0.848-.907]). The risk model had a good calibration (goodness-of-fit χ(8) 8.13, P = 0.42).Conclusions Our model may be utilised in estimating the probability of lung cancer in nodules detected at baseline, and three months and twelve months from baseline, allowing more efficient stratification of follow-up in population-based lung cancer screening programs.3 Key Messages• What is the key question?To develop a lung cancer pulmonary nodule risk model which incorporates volumetric measurements.• What is the bottom line?The UKLS pulmonary risk model has excellent discrimination; area under the receiveroperating characteristic curve (AUC [95% CI] = 0.885 [0.880 to 0.889] and has good calibration (goodness-of-fit χ(8) 8.13, P = 0.42).• Why read on?The potential for the UKLS Nodule Risk Model (UKLS-NRM) is that it may be utilised in future national CT screening programmes, which incorporates volumetric measurements to identify malignant pulmonary nodules.

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Section: Introductionmentioning

confidence: 99%

Probability of cancer in lung nodules using sequential volumetric screening up to 12 months: the UKLS trial

Marcus

Duffy²,

Devaraj

et al. 2019

Thorax

Self Cite

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“…Another study also looked at semantic features, proposing that pulmonary nodules with a larger diameter, located in the upper lobe, spiculation, and PSN (part-solid nodule) had a higher probability to be malignant [27]. However, it has been shown that semi-automated volume analysis is a more robust method than a simple measurement of the diameter to measure the size of the pulmonary nodule [28], and spiculation is an uncommon feature in early-stage lung cancer [8]. Our study finds that nodule diameter and nodule type are significantly different between cohorts diagnosed with IA and PM, with nodules with smaller diameter and pure GGN types increasing the probability of PM diagnosis.…”

Section: Discussionmentioning

confidence: 99%

Preoperative CT-based radiomics combined with intraoperative frozen section is predictive of invasive adenocarcinoma in pulmonary nodules: a multicenter study

Woodruff

Sanduleanu

et al. 2020

Eur Radiol

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Objectives Develop a CT-based radiomics model and combine it with frozen section (FS) and clinical data to distinguish invasive adenocarcinomas (IA) from preinvasive lesions/minimally invasive adenocarcinomas (PM). Methods This multicenter study cohort of 623 lung adenocarcinomas was split into training (n = 331), testing (n = 143), and external validation dataset (n = 149). Random forest models were built using selected radiomics features, results from FS, lesion volume, clinical and semantic features, and combinations thereof. The area under the receiver operator characteristic curves (AUC) was used to evaluate model performances. The diagnosis accuracy, calibration, and decision curves of models were tested. ResultsThe radiomics-based model shows good predictive performance and diagnostic accuracy for distinguishing IA from PM, with AUCs of 0.89, 0.89, and 0.88, in the training, testing, and validation datasets, respectively, and with corresponding accuracies of 0.82, 0.79, and 0.85. Adding lesion volume and FS significantly increases the performance of the model with AUCs of 0.96, 0.97, and 0.96, and with accuracies of 0.91, 0.94, and 0.93 in the three datasets. There is no significant difference in AUC between the FS model enriched with radiomics and volume against an FS model enriched with volume alone, while the former has higher accuracy. The model combining all available information shows minor non-significant improvements in AUC and accuracy compared with an FS model enriched with radiomics and volume. Conclusions Radiomics signatures are potential biomarkers for the risk of IA, especially in combination with FS, and could help guide surgical strategy for pulmonary nodules patients. Key Points • A CT-based radiomics model may be a valuable tool for preoperative prediction of invasive adenocarcinoma for patients with pulmonary nodules. • Radiomics combined with frozen sections could help in guiding surgery strategy for patients with pulmonary nodules.

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“…In the NELSON trial, nodule growth was defined as a change in the volume of at least 25% between two subsequent examinations based on validation studies with repeated low-dose CT examinations performed on the same days, in which the measurement error was maximally 25% (3). However, optimization of the VDT cutoff for fast-growing nodules in lung cancer screening revealed that lowering the VDT cutoff could reduce false-positive referrals (28). D Not all CT screening facilities can use the software that was used in the NELSON trial.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of the 95% limits of agreement of the volumes of 5-year clinically stable solid nodules for the development of a follow-up system for indeterminate solid nodules in CT lung cancer screening

Kakinuma¹,

Muramatsu²,

Yamamichi

et al. 2018

J. Thorac. Dis.

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Disagreement of diameter and volume measurements for pulmonary nodule size estimation in CT lung cancer screening

Abstract: Pre-results, ISRCTN63545820.

Cited by 69 publications

References 7 publications

Probability of cancer in lung nodules using sequential volumetric screening up to 12 months: the UKLS trial

Probability of cancer in lung nodules using sequential volumetric screening up to 12 months: the UKLS trial

Preoperative CT-based radiomics combined with intraoperative frozen section is predictive of invasive adenocarcinoma in pulmonary nodules: a multicenter study

Evaluation of the 95% limits of agreement of the volumes of 5-year clinically stable solid nodules for the development of a follow-up system for indeterminate solid nodules in CT lung cancer screening

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