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Deep learning approach of diffusion-weighted imaging as an outcome predictor in laryngeal and hypopharyngeal cancer patients with radiotherapy-related curative treatment: a preliminary study

  • Head and Neck
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Abstract

Objectives

This preliminary study aimed to develop a deep learning (DL) model using diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) maps to predict local recurrence and 2-year progression-free survival (PFS) in laryngeal and hypopharyngeal cancer patients treated with various forms of radiotherapy-related curative therapy.

Methods

Seventy patients with laryngeal and hypopharyngeal cancers treated by radiotherapy, chemoradiotherapy, or induction-(chemo)radiotherapy were enrolled and divided into training (N = 49) and test (N = 21) groups based on presentation timeline. All patients underwent MR before and 4 weeks after the start of radiotherapy. The DL models that extracted imaging features on pre- and intra-treatment DWI and ADC maps were trained to predict the local recurrence within a 2-year follow-up. In the test group, each DL model was analyzed for recurrence prediction. Additionally, the Kaplan-Meier and multivariable Cox regression analyses were performed to evaluate the prognostic significance of the DL models and clinical variables.

Results

The highest area under the receiver operating characteristics curve and accuracy for predicting the local recurrence in the DL model were 0.767 and 81.0%, respectively, using intra-treatment DWI (DWIintra). The log-rank test showed that DWIintra was significantly associated with PFS (p = 0.013). DWIintra was an independent prognostic factor for PFS in multivariate analysis (p = 0.023).

Conclusion

DL models using DWIintra may have prognostic value in patients with laryngeal and hypopharyngeal cancers treated by curative radiotherapy. The model-related findings may contribute to determining the therapeutic strategy in the early stage of the treatment.

Key Points

• Deep learning models using intra-treatment diffusion-weighted imaging have prognostic value in patients with laryngeal and hypopharyngeal cancers treated by curative radiotherapy.

• The findings from these models may contribute to determining the therapeutic strategy at the early stage of the treatment.

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Abbreviations

ADC:

Apparent diffusion coefficient

ADCintra :

Intra-treatment ADC

ADCpre :

Pretreatment ADC

AUC:

Area under the receiver operating characteristics curve

CNN:

Convolutional neural network

DL:

Deep learning

DWI:

Diffusion-weighted imaging

DWIintra :

Intra-treatment DWI

DWIpre :

Pretreatment DWI

MR:

Magnetic resonance

PFS:

Progression-free survival

ROC:

Receiver operating characteristics.

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Acknowledgements

This retrospective study was supported by a grant from the Japanese Ministry of Education, Culture, Sports, Science and Technology (Grant-in-Aid for Young Scientists KAKEN; No. KAKEN No. 21K15814).

Funding

This retrospective study was supported by a grant from the Japanese Ministry of Education, Culture, Sports, Science and Technology (Grant-in-Aid for Young Scientists KAKEN; No. KAKEN No. 21K15814).

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Authors and Affiliations

  1. Department of Radiology, St. Marianna University School of Medicine, 2–16-1 Sugao, Miyamae-ku, Kawasaki, Kanagawa, 216-8511, Japan

    Hayato Tomita, Atsuko Fujikawa & Hidefumi Mimura

  2. Department of Advanced Biomedical Imaging Informatics, St. Marianna University School of Medicine, 2–16-1 Sugao, Miyamae-ku, Kawasaki, Kanagawa, 216-8511, Japan

    Tatsuaki Kobayashi & Yasuyuki Kobayashi

  3. School of Science for Open and Environmental Systems, Graduate School of Science and Technology, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama, Kanagawa, 223-8522, Japan

    Eichi Takaya

  4. Department of AI Research Lab, Harada Academy, 2-54-4, Higashitaniyama, Kagoshima, Kagoshima, 891-0113, Japan

    Sono Mishiro & Daisuke Hirahara

  5. Department of Radiology, Machida Municipal Hospital, 2-15-41 Asahi-cho, Machida, Tokyo, 194-0023, Japan

    Yoshiko Kurihara

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  1. Hayato Tomita
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Correspondence to Hayato Tomita.

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Tomita, H., Kobayashi, T., Takaya, E. et al. Deep learning approach of diffusion-weighted imaging as an outcome predictor in laryngeal and hypopharyngeal cancer patients with radiotherapy-related curative treatment: a preliminary study. Eur Radiol 32, 5353–5361 (2022). https://doi.org/10.1007/s00330-022-08630-9

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