Solid pulmonary nodule risk assessment and decision analysis: comparison of four prediction models in 285 cases

Perandini, Simone; Soardi, Gian Alberto; Motton, Massimiliano; Rossi, Arianna; Signorini, M.; Montemezzi, Stefania

doi:10.1007/s00330-015-4138-9

Cited by 19 publications

(11 citation statements)

References 22 publications

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“…Finally, there are additional nodule prediction models that incorporate PET information (Herder) and volumetric measurements (Bayesian Inference Malignancy Calculator) that may be more accurate than the prediction models used here; however, we did not have PET data on all patients, and volumetric measurements were not provided. [16][17][18][19][20] The strengths of the study include the fact that it is a geographically diverse sample with prospective data collected from patients with pulmonary nodules. In addition, an important attribute of this study was that physician assessment of pCA prior to diagnostic testing was provided.…”

Section: Discussionmentioning

confidence: 99%

Physician Assessment of Pretest Probability of Malignancy and Adherence With Guidelines for Pulmonary Nodule Evaluation

Tanner

Porter²,

Gould

et al. 2017

Chest

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Physician assessment as a means for predicting malignancy in pulmonary nodules is more accurate than previously validated nodule prediction calculators. Despite the accuracy of clinical intuition, physicians did not follow guideline-based recommendations when selecting the next diagnostic test. To provide optimal patient care, focus in the areas of guideline refinement, implementation, and dissemination is needed.

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Section: Discussionmentioning

confidence: 99%

Physician Assessment of Pretest Probability of Malignancy and Adherence With Guidelines for Pulmonary Nodule Evaluation

Tanner

Porter²,

Gould

et al. 2017

Chest

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“…The topic of the LUNGx Challenge is especially timely given the recent development of lung nodule risk models and attention toward the clinical deployment of such models, [20][21][22][23][24] which benefit from the inclusion of imaging findings. Cancer risk models, however, require clinical and demographic information beyond that provided by the LUNGx Challenge.…”

Section: Discussionmentioning

confidence: 99%

LUNGx Challenge for computerized lung nodule classification

Armato

Drukker

et al. 2016

J. Med. Imag

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The purpose of this work is to describe the LUNGx Challenge for the computerized classification of lung nodules on diagnostic computed tomography (CT) scans as benign or malignant and report the performance of participants' computerized methods along with that of six radiologists who participated in an observer study performing the same Challenge task on the same dataset. The Challenge provided sets of calibration and testing scans, established a performance assessment process, and created an infrastructure for case dissemination and result submission. Ten groups applied their own methods to 73 lung nodules (37 benign and 36 malignant) that were selected to achieve approximate size matching between the two cohorts. Area under the receiver operating characteristic curve (AUC) values for these methods ranged from 0.50 to 0.68; only three methods performed statistically better than random guessing. The radiologists' AUC values ranged from 0.70 to 0.85; three radiologists performed statistically better than the best-performing computer method. The LUNGx Challenge compared the performance of computerized methods in the task of differentiating benign from malignant lung nodules on CT scans, placed in the context of the performance of radiologists on the same task. The continued public availability of the Challenge cases will provide a valuable resource for the medical imaging research community.

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“…Our results emphasize the need to specifically note the presence of pulmonary nodules with irregular and ill-defined margins in patients with metastatic RCC to the lung in CT reports as these may represent primary lung neoplasms. Computer-aided detection and characterization of pulmonary nodules with prediction models have been shown to be helpful in the diagnosis and pre-surgical evaluation of pulmonary nodules 18,19 and may help identify these lesions.…”

Section: Discussionmentioning

confidence: 99%

Renal Cell Carcinoma With Pulmonary Metastasis and Metachronous Non-Small Cell Lung Cancer

Bowman

Pedrosa²,

Kapur

et al. 2017

Clinical Genitourinary Cancer

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Introduction The development of a second primary malignancy in a patient with a preexisting diagnosis of metastatic cancer may be easily overlooked or misattributed to progression of disease. We report 3 patients with clear cell renal cell carcinoma (ccRCC) metastatic to the lungs who were subsequently diagnosed with non-small cell lung cancer (NSCLC). We examined the frequency of this occurrence within our institution and report on the radiographic findings that may help distinguish between metastatic and primary lung cancers. Methods Patients who received systemic targeted therapy for metastatic RCC at our institution between January 2006 and October 2013 were identified and the proportion and incidence rate for developing NSCLC with pre-existing metastatic RCC were calculated. Results 2% of patients (3/151; 95% CI 0.68 – 5.68%) treated for metastatic RCC with systemic targeted therapies at our institution were subsequently diagnosed with NSCLC, increasing to 3.5% (3/85; 95% CI 1.21 – 9.87%) among patients with known RCC pulmonary metastasis. The incident rate for development of NSCLC in metastatic RCC patients was 0.87 per 100 person-years (95% CI 0.22 – 2.4). Conclusion The subsequent development of a primary lung cancer in metastatic RCC patients, which occurred in 2% of patients at our institution, appears underreported in the literature. Primary NSCLC may be underdiagnosed in patients with metastatic RCC. Both the radiographic appearance and clinical behavior of a lesion may hold clues that can help distinguish between a new primary and progression of metastatic disease.

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Solid pulmonary nodule risk assessment and decision analysis: comparison of four prediction models in 285 cases

Abstract: • The BIMC and PKUPH models offer better characterization than older prediction models • Both the PKUPH and BIMC models completely avoided false negative results • The Mayo model suffers from a large number of indeterminate results.

Cited by 19 publications

References 22 publications

Physician Assessment of Pretest Probability of Malignancy and Adherence With Guidelines for Pulmonary Nodule Evaluation

Physician Assessment of Pretest Probability of Malignancy and Adherence With Guidelines for Pulmonary Nodule Evaluation

LUNGx Challenge for computerized lung nodule classification

Renal Cell Carcinoma With Pulmonary Metastasis and Metachronous Non-Small Cell Lung Cancer

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