2021
DOI: 10.1200/cci.21.00021
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Machine Learning Radiomics Model for Early Identification of Small-Cell Lung Cancer on Computed Tomography Scans

Abstract: PURPOSE Small-cell lung cancer (SCLC) is the deadliest form of lung cancer, partly because of its short doubling time. Delays in imaging identification and diagnosis of nodules create a risk for stage migration. The purpose of our study was to determine if a machine learning radiomics model can detect SCLC on computed tomography (CT) among all nodules at least 1 cm in size. MATERIALS AND METHODS Computed tomography scans from a single institution were selected and resampled to 1 × 1 × 1 mm. Studies were divide… Show more

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Cited by 7 publications
(10 citation statements)
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“…Beyond the traditional tasks of radiologists, radiomic features coupled with AI can extract and analyze quantitative data to provide tumor characterizations to guide patient treatment, including predicting histologic and molecular subtypes. For example, the ML radiomics model has shown promise in differentiating small cell lung cancer from other lung lesions on CT for pulmonary nodules at least 1 cm in size [36] . Recently, Ma et al used ML to differentiate between breast cancer molecular subtypes based on mammography and ultrasound.…”
Section: Imaging Acquisition Optimizationmentioning
confidence: 99%
“…Beyond the traditional tasks of radiologists, radiomic features coupled with AI can extract and analyze quantitative data to provide tumor characterizations to guide patient treatment, including predicting histologic and molecular subtypes. For example, the ML radiomics model has shown promise in differentiating small cell lung cancer from other lung lesions on CT for pulmonary nodules at least 1 cm in size [36] . Recently, Ma et al used ML to differentiate between breast cancer molecular subtypes based on mammography and ultrasound.…”
Section: Imaging Acquisition Optimizationmentioning
confidence: 99%
“…(3) The number of finite lifespans. (4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19) The mean, standard deviation, skewness, kurtosis, first quartile, median, third quartile, and interquartile range of the finite lifespans and finite midlifes. (20) The entropy of the finite lifespans [36].…”
Section: Supplemental Informationmentioning
confidence: 99%
“…In this paper we study the use of TDA for lung tumor histology prediction from thoracic radiographic images. Our main goal is to study the added value of TDA to all of the prominent learning problems on lung tumor CT scan images compared to state of the art quantitative imaging tools [12, 13, 14, 15, 16].…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
“…Our main goal is to study the added value of TDA to all of the prominent learning problems on lung tumor CT scan images compared with state-of-the-art quantitative imaging tools. 12 , 13 , 14 , 15 , 16 …”
Section: Introductionmentioning
confidence: 99%