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Observational Study
. 2022 Jun;127(6):609-615.
doi: 10.1007/s11547-022-01494-5. Epub 2022 May 10.

Radiomics in pulmonary neuroendocrine tumours (NETs)

Diletta Cozzi  1   2 , Eleonora Bicci  3 , Edoardo Cavigli  3 , Ginevra Danti  3 , Silvia Bettarini  4 , Paolo Tortoli  4 , Lorenzo Nicola Mazzoni  5 , Simone Busoni  4 , Silvia Pradella  3   6 , Vittorio Miele  3
Affiliations
Observational Study

Radiomics in pulmonary neuroendocrine tumours (NETs)

Diletta Cozzi et al. Radiol Med. 2022 Jun.

Abstract

Objectives: The aim of this single-centre, observational, retrospective study is to find a correlation using Radiomics between the analysis of CT texture features of primary lesion of neuroendocrine (NET) lung cancer subtypes (typical and atypical carcinoids, large and small cell neuroendocrine carcinoma), Ki-67 index and the presence of lymph nodal mediastinal metastases.

Methods: Twenty-seven patients (11 males and 16 females, aged between 48 and 81 years old-average age of 70,4 years) with histological diagnosis of pulmonary NET with known Ki-67 status and metastases who have performed pre-treatment CT in our department were included. All examinations were performed with the same CT scan (Sensation 16-slice, Siemens). The study protocol was a baseline scan followed by 70 s delay acquisition after administration of intravenous contrast medium. After segmentation of primary lesions, quantitative texture parameters of first and higher orders were extracted. Statistics nonparametric tests and linear correlation tests were conducted to evaluate the relationship between different textural characteristics and tumour subtypes.

Results: Statistically significant (p < 0.05) differences were seen in post-contrast enhanced CT in multiple first and higher-order extracted parameters regarding the correlation with classes of Ki-67 index values. Statistical analysis for direct acquisitions was not significant. Concerning the correlation with the presence of metastases, one histogram feature (Skewness) and one feature included in the Gray-Level Co-occurrence Matrix (ClusterShade) were significant on contrast-enhanced CT only.

Conclusions: CT texture analysis may be used as a valid tool for predicting the subtype of lung NET and its aggressiveness.

Keywords: Computed tomography; Ki-67; Lung carcinoids; Radiomics.

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Conflict of interest statement

The authors declare that they have no conflict of interest.

Figures

Fig. 1
Fig. 1
Workflow of patients’ selection
Fig. 2
Fig. 2
Computed tomography imaging in a 67-year-old male patient with a smooth-edged pulmonary lesion with histology of a typical carcinoid in the left lower lobe at baseline (A), with vivid enhancement in the venous phase (B) and lung parenchyma window (C). Large cell carcinoma in a 79-year-old male patient closely connected to the right posterior costal pleura; it shows polylobulated edges at baseline and lung parenchyma window (D, F) with contrast enhancement (E). Small cell carcinoma in a 76-year-old female patient: large left hilar lesion on direct examination (F), with inhomogeneous enhancement (G) and in parenchyma window (H). Notice the multiple confluent mediastinal lymphadenopathies (asterisk)
Fig. 3
Fig. 3
a Boxplot of Skewness feature vs Ki-67 classes; b boxplot of ClusterShade feature vs Ki-67 classes; c boxplot of Skewness feature vs absence (0) or presence (1) of metastases; d boxplot of ClusterShade feature vs absence (0) or presence (1) of metastasis
See this image and copyright information in PMC

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