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Toward better prediction of recurrence for Cushing’s disease: a factorization-machine based neural approach

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Abstract

Cushing's disease (CD) is a rare disease that occurs in 1.2–1.4 persons per million population per year. Recurrence prediction after transsphenoidal surgery (TSS) is important for determining individual treatment and follow-up strategies. Between 2000 and 2017, 354 CD patients with initial postoperative remission and long-term follow-up data were enrolled from Peking union medical college hospital (PUMCH) to predict recurrence, and PUMCH is one of the largest CD treatment centers in the world. We first investigated the effect of a factorization machine (FM)-based neural network to predict recurrence after TSS for CD. This method could automatically reduce a portion of the cross-feature selection work with acceptable parameters. We conducted a performance comparison of various algorithms on the collected dataset. To address the lack of interpretability of neural network models, we also used the local interpretable model-agnostic explanations approach, which provides an explanation in the form of relevant features of the predicted results by approximating the model behavior of the variables in a local manner. Compared with existing methods, the DeepFM model obtained the highest AUC value (0.869) and the lowest log loss value (0.256). According to the importance of each feature, three top features for the DeepFM model were postoperative morning adrenocorticotropic hormone level, age, and postoperative morning serum cortisol nadir. In the post hoc explanation phase, the above-mentioned importance-leading features made a great contribution to the prediction probability. The results showed that deep learning-based models could better aid neurosurgeons in recurrence prediction after TTS for patients with CD, and could contribute to determining individual treatment strategies.

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Abbreviations

CD:

Cushing’s disease

UFC:

Urine free cortisol

ACTH:

Adrenocorticotropin

BMI:

Body mass index

PrC:

Preoperative morning serum cortisol level

PrUFC:

Preoperative 24 h UFC level

PrACTH:

Preoperative morning ACTH level

PoACTH:

Postoperative morning ACTH level

PoC:

Postoperative morning serum cortisol nadir

PoUFC:

Postoperative 24 h UFC nadir level

CSI:

Cavernous sinus invasion

MLP:

Multilayer perceptron

LR:

Logistic regression

DT:

Decision tree

RF:

Random forest

GaussianNB:

Gaussian Naive Bayes

GBDT:

Gradient tree boosting

PNN:

Product-based neural network

DeepFM:

Factorization-Machine neural network

LIME:

Local interpretable model-agnostic explanations

TSS:

Transsphenoidal surgery

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Acknowledgements

The authors of this paper are grateful to the Graduate Innovation Fund of Peking Union Medical College (2018-1002-01-10), Natural Science Foundation of Beijing Municipality (Grant No. 7182137), Capital Characteristic Clinic Project (Grant No. Z16100000516092), Chinese Academy of Medical Sciences (Grant No. 2017-I2M-3-014), and Natural Science Foundation of China (Grant No.61872113), and the joint project with Baidu Inc.

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Author notes

  1. Yanghua Fan and Dongfang Li contributed equally to this work.

Authors and Affiliations

  1. Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100032, China

    Yanghua Fan, Yifan Liu, Ming Feng & Renzhi Wang

  2. Harbin Institute of Technology (Shenzhen), Shenzhen, 518055, China

    Dongfang Li & Qingcai Chen

  3. Peng Cheng Laboratory, Shenzhen, China

    Qingcai Chen

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  1. Yanghua Fan
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Correspondence to Qingcai Chen or Renzhi Wang.

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Fan, Y., Li, D., Liu, Y. et al. Toward better prediction of recurrence for Cushing’s disease: a factorization-machine based neural approach. Int. J. Mach. Learn. & Cyber. 12, 625–633 (2021). https://doi.org/10.1007/s13042-020-01192-6

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