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A spatio-temporal image analysis for growth of indeterminate pulmonary nodules detected by CT scan

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Abstract

The objective is to evaluate the performance of computational image classification for indeterminate pulmonary nodules (IPN) chronologically detected by CT scan. Total 483 patients with 670 abnormal pulmonary nodules, who were taken chest thin-section CT (TSCT) images at least twice and resected as suspicious nodules in our hospital, were enrolled in this study. Nodular regions from the initial and the latest TSCT images were cut manually for each case, and approached by Python development environment, using the open-source cv2 library, to measure the nodular change rate (NCR). These NCRs were statistically compared with clinico-pathological factors, and then, this discriminator was evaluated for clinical performance. NCR showed significant differences among the nodular consistencies. In terms of histological subtypes, NCR of invasive adenocarcinoma (ADC) were significantly distinguishable from other lesions, but not from minimally invasive ADC. Only for cancers, NCR was significantly associated with loco-regional invasivity, p53-immunoreactivity, and Ki67-immunoreactivity. Regarding Epidermal Growth Factor Receptor gene mutation of ADC-related nodules, NCR showed a significant negative correlation. On staging of lung cancer cases, NCR was significantly increased with progression from pTis-stage 0 up to pT1b-stage IA2. For clinical shared decision-making (SDM) whether urgent resection or watchful-waiting, receiver operating characteristic (ROC) analysis showed that area under the ROC curve was 0.686. For small-sized IPN detected by CT scan, this approach shows promise as a potential navigator to improve work-up for life-threatening cancer screening and assist SDM before surgery.

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Acknowledgements

We would like to express our sincere gratitude to Mr. Noriyasu Kobayashi and Ms. Haruka Takemura, Department of Laboratory Medicine, JA North Alps Medical Center Azumi Hospital, Japan, for the preparation of the pathological specimen. This work was facilitated by the open source contributions of Python and OpenCV [20].

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Authors and Affiliations

  1. Department of Thoracic Surgery, JA Nagano North Alps Medical Center Azumi Hospital, 3207-1 Ikeda, Ikeda-machi, Kita-azumi-gun, Nagano, 399-8605, Japan

    Takaomi Hanaoka & Shotaro Ono

  2. Department of Molecular Pathology, Shinshu University School of Medicine, 3-1-1 Asahi, Matsumoto, Nagano, 390-8621, Japan

    Hisanori Matoba & Jun Nakayama

  3. Department of Pathology, JA Nagano North Alps Medical Center Azumi Hospital, 3207-1 Ikeda, Ikeda-Machi, Kita-azumi-gun, Nagano, 399-8605, Japan

    Jun Nakayama

  4. Department of Respirology, JA Nagano North Alps Medical Center Azumi Hospital, 3207-1 Ikeda, Ikeda-machi, Kita-azumi-gun, Nagano, 399-8605, Japan

    Kayoko Ikegawa & Mitsuyo Okada

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  1. Takaomi Hanaoka
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  2. Hisanori Matoba
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  3. Jun Nakayama
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  4. Shotaro Ono
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Contributions

All authors meet the ICMJE authorship criteria. TH: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Project Administration, Resources, Supervision, Validation, Visualization, Writing-original draft, Writing-review and editing. HM: Investigation, Data Curation, Formal Analysis, Methodology, Resources, Validation, Writing-review and editing. JN: Investigation, Data Curation, Formal Analysis, Methodology, Resources, Supervision, Validation, Writing-review and editing. SO: Validation, Writing—review and editing. KI: Validation, Writing—review and editing. MO: Validation, Writing—review and editing.

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Correspondence to Takaomi Hanaoka.

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All authors have no financial/commercial conflicts of interest.

Ethical approval

This investigation was approved by the institutional ethics committees (No. 29–06-01), with waiver of patient informed consent.

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Hanaoka, T., Matoba, H., Nakayama, J. et al. A spatio-temporal image analysis for growth of indeterminate pulmonary nodules detected by CT scan. Radiol Phys Technol 17, 71–82 (2024). https://doi.org/10.1007/s12194-023-00750-1

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  • DOI: https://doi.org/10.1007/s12194-023-00750-1

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