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Depicting and predicting changes of lung after lobectomy for cancer by using CT images

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Abstract

Lobectomy is an effective and well-established therapy for localized lung cancer. This study aimed to assess the lung and lobe change after lobectomy and predict the postoperative lung volume. The study included 135 lung cancer patients from two hospitals who underwent lobectomy (32, right upper lobectomy (RUL); 31, right middle lobectomy (RML); 24, right lower lobectomy (RLL); 26, left upper lobectomy (LUL); 22, left lower lobectomy (LLL)). We initially employ a convolutional neural network model (nnU-Net) for automatically segmenting pulmonary lobes. Subsequently, we assess the volume, effective lung volume (ELV), and attenuation distribution for each lobe as well as the entire lung, before and after lobectomy. Ultimately, we formulate a machine learning model, incorporating linear regression (LR) and multi-layer perceptron (MLP) methods, to predict the postoperative lung volume. Due to the physiological compensation, the decreased TLV is about 10.73%, 8.12%, 13.46%, 11.47%, and 12.03% for the RUL, RML, RLL, LUL, and LLL, respectively. The attenuation distribution in each lobe changed little for all types of lobectomy. LR and MLP models achieved a mean absolute percentage error of 9.8% and 14.2%, respectively. Radiological findings and a predictive model of postoperative lung volume might help plan the lobectomy and improve the prognosis.

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Data availability

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

CT:

Computed tomography

ELV:

Effective lung volume

FEV1:

Forced expiratory volume in one second

FVC:

Forced vital capacity

HAV:

High attenuation volume

LAV:

Low attenuation volume

LLL:

Left lower lobectomy

LUL:

Left upper lobectomy

MAV:

Middle attenuation volume

PFT:

Pulmonary function test

RLL:

Right lower lobectomy

RML:

Right middle lobectomy

RUL:

Right upper lobectomy

TLV:

Total volume change

VATS:

Video-assisted thoracoscopic surgery

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Funding

This work was partly supported by the National Natural Science Foundation of China under Grant (Nos. 82072008, 62271131), the Liaoning Natural Science Foundation (2021-YGJC-21, 2020-BS-049), and the Fundamental Research Funds for the Central Universities (N2119010, N2224001-10).

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Author notes

  1. Yanan Wu, Haowen Pang and Jing Shen contributed equally to this work.

Authors and Affiliations

  1. College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China

    Yanan Wu, Haowen Pang, Shouliang Qi & Wei Qian

  2. Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China

    Yanan Wu, Haowen Pang & Shouliang Qi

  3. Graduate School, Tianjin Medical University, Tianjin, China

    Jing Shen

  4. Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, Dalian, China

    Jing Shen & Jianlin Wu

  5. School of Chemical Equipment, Shenyang University of Technology, Liaoyang, China

    Jie Feng

  6. Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China

    Yong Yue

Authors
  1. Yanan Wu
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Contributions

YW, HP, and JS contributed equally to the data collection, processing and analysis, and writing – original draft preparation. JF contributed to the software and investigation. YY contributed to the data curation and validation. SQ, WQ, and JW contributed to the conceptualization, supervision, writing – reviewing and editing, funding acquisition. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Shouliang Qi or Jianlin Wu.

Ethics declarations

Ethics approval and consent to participate

The study was approved by the Medical Ethics Committee of the Affiliated Zhongshan Hospital of Dalian University and Shengjing Hospital of China Medical University. All procedures involving human participants were performed according to the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Formal consent was not required for this retrospective study.

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Not applicable.

Competing interests

The authors declare that they have no competing interests.

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Wu, Y., Pang, H., Shen, J. et al. Depicting and predicting changes of lung after lobectomy for cancer by using CT images. Med Biol Eng Comput 61, 3049–3066 (2023). https://doi.org/10.1007/s11517-023-02907-x

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  • DOI: https://doi.org/10.1007/s11517-023-02907-x

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