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. 2023 Jun 1;13(6):3747-3759.
doi: 10.21037/qims-22-1101. Epub 2023 Apr 6.

Using 2-dimensional hand photographs to predict postoperative biochemical remission in acromegaly patients: a transfer learning approach

Mengqi Wang #  1 , Chengbin Duan #  1 , Shun Yao  1 , Jinping Chen  1 , Shaolin Zhang  1 , Zongming Wang  1 , Bin Hu  1 , Zhigang Mao  1 , Haijun Wang  1 , Yonghong Zhu  2 , Wenli Chen  1
Affiliations

Using 2-dimensional hand photographs to predict postoperative biochemical remission in acromegaly patients: a transfer learning approach

Mengqi Wang et al. Quant Imaging Med Surg. .

Abstract

Background: The primary treatment goals in acromegaly patients are complete surgical removal of underlying pituitary tumors and biochemical remission. One of the challenges in developing countries is the difficulty in monitoring postoperative biochemical levels in acromegaly patients, particularly those who live in remote areas or regions with limited medical resources.

Methods: In an attempt to overcome the abovementioned challenges, we conducted a retrospective study and established a mobile and low-cost method to predict biochemical remission in acromegaly patients after surgery, the efficacy of which was assessed retrospectively using the China Acromegaly Patient Association (CAPA) database. A total of 368 surgical patients from the CAPA database were successfully followed up to obtain their hand photographs. Demographics, baseline clinical characteristics, pituitary tumor features, and treatment details were collated. Postoperative outcome, defined as biochemical remission at the last follow-up timepoint, was assessed. Transfer learning with a new mobile tailored neurocomputing architecture, MobileNetv2, was used to explore the identical features that could be used as predictors of long-term biochemical remission after surgery.

Results: As expected, the MobileNetv2-based transfer learning algorithm was shown to predict biochemical remission with statistical accuracies of 0.96 and 0.76 in the training cohort (n=803) and validation cohort (n=200), respectively, and the loss function value was 0.82.

Conclusions: Our findings demonstrate the potential of the MobileNetv2-based transfer learning algorithm in predicting biochemical remission for postoperative patients who are at home or live far away from a pituitary or neuroendocrinological treatment center.

Keywords: Acromegaly; MobileNetv2; biochemical remission; transfer learning.

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Conflict of interest statement

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-22-1101/coif). SY was supported by the Guangdong Basic and Applied Basic Research Foundation (No. 2023A1515011644) and the Chinese Postdoctoral Science Foundation (No. 2019M663271). WC was supported by the Clinical Research Project of The East Division (the First Affiliated Hospital, Sun Yat-sen University, No. 2019004) and the National Natural Science Foundation of China (No. 82203179). The other authors have no conflicts of interest to declare.

Figures

Figure 1
Figure 1
Illustration of background removal in hand photographs using Otsu’s thresholding method.
Figure 2
Figure 2
Illustration of the residual bottleneck block structure used in the MobileNetv2 algorithm.
Figure 3
Figure 3
Illustrative framework of our proposed MobileNetv2-based transfer learning algorithm to automatically predict postoperative remission for acromegaly patients using hand photographs. s, stride; p, padding; inc, input channel; t, expansion factor.
Figure 4
Figure 4
A diagram showing the transfer learning algorithm.
Figure 5
Figure 5
Epoch plots of the transfer learning algorithm-based predicting model performance over each epoch round.
See this image and copyright information in PMC

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