Automated Intensity Modulated Radiation Therapy Treatment Planning for Cervical Cancer Based on Convolution Neural Network

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Abstract

Purpose:

To develop and evaluate an automatic intensity-modulated radiation therapy (IMRT) program for cervical cancer, including a Convolution Neural Network (CNN)-based prediction model and an automated optimization strategy.

Methods:

A CNN deep learning model was trained to predict a patient-specify set of IMRT objectives based on overlap volume histograms (OVH) and high-quality plan of previous patients. A total of 140 cervical cancer patients were enrolled in this study, including 100 patients in the training set, 20 patients in the validation set and 20 patients in the testing set. The input of this model was OVH data and the output were values of IMRT plan objectives. For patients in the testing set, the set of planning objectives were predicted by the CNN model and used to automatically generate IMRT plans. Meanwhile, manual plans of these patients were generated by 1 beginner planner and 1 senior planner respectively. Finally, dose distribution, dosimetric parameters and planning time were analyzed. In addition, the 3 types of plans were blinded compared and ranked by an experienced oncologist.

Results:

There were almost no statistically differences among these 3 types of plans in target coverage and dose conformity. Dose homogeneity were slightly decreased while the average dose and parameters for most organs-at-risk (OARs) were decreased in automatic plans. Especially in comparison with manual plans by the beginner planner, V ₄₀ of bladder and rectum decreased 6.3% and 12.3%, while mean dose of rectum and marrow were 1.1 Gy and 1.8 Gy lower with automatic plans (either P < 0.017). In the blinded comparison, automatic plans were chosen as best plan in 14 cases.

Conclusions:

For cervical cancer, automatic IMRT plans optimized from the CNN generated objectives have superior dose sparing without compromising of target dose. It significantly reduced the planning time.

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Most cited references 41

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Dermatologist-level classification of skin cancer with deep neural networks

Andre Esteva, Brett Kuprel, Roberto Chacón-Novoa … (2017)

Skin cancer, the most common human malignancy, is primarily diagnosed visually, beginning with an initial clinical screening and followed potentially by dermoscopic analysis, a biopsy and histopathological examination. Automated classification of skin lesions using images is a challenging task owing to the fine-grained variability in the appearance of skin lesions. Deep convolutional neural networks (CNNs) show potential for general and highly variable tasks across many fine-grained object categories. Here we demonstrate classification of skin lesions using a single CNN, trained end-to-end from images directly, using only pixels and disease labels as inputs. We train a CNN using a dataset of 129,450 clinical images—two orders of magnitude larger than previous datasets—consisting of 2,032 different diseases. We test its performance against 21 board-certified dermatologists on biopsy-proven clinical images with two critical binary classification use cases: keratinocyte carcinomas versus benign seborrheic keratoses; and malignant melanomas versus benign nevi. The first case represents the identification of the most common cancers, the second represents the identification of the deadliest skin cancer. The CNN achieves performance on par with all tested experts across both tasks, demonstrating an artificial intelligence capable of classifying skin cancer with a level of competence comparable to dermatologists. Outfitted with deep neural networks, mobile devices can potentially extend the reach of dermatologists outside of the clinic. It is projected that 6.3 billion smartphone subscriptions will exist by the year 2021 (ref. 13) and can therefore potentially provide low-cost universal access to vital diagnostic care.

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A survey on deep learning in medical image analysis

Geert Litjens, Arnaud Arindra Adiyoso Setio, Thijs Kooi … (2017)

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks. Concise overviews are provided of studies per application area: neuro, retinal, pulmonary, digital pathology, breast, cardiac, abdominal, musculoskeletal. We end with a summary of the current state-of-the-art, a critical discussion of open challenges and directions for future research.

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Representation learning: a review and new perspectives.

Y Bengio, A. Courville, P. Vincent (2013)

The success of machine learning algorithms generally depends on data representation, and we hypothesize that this is because different representations can entangle and hide more or less the different explanatory factors of variation behind the data. Although specific domain knowledge can be used to help design representations, learning with generic priors can also be used, and the quest for AI is motivating the design of more powerful representation-learning algorithms implementing such priors. This paper reviews recent work in the area of unsupervised feature learning and deep learning, covering advances in probabilistic models, autoencoders, manifold learning, and deep networks. This motivates longer term unanswered questions about the appropriate objectives for learning good representations, for computing representations (i.e., inference), and the geometrical connections between representation learning, density estimation, and manifold learning.

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Author and article information

Journal

Journal ID (nlm-ta): Technol Cancer Res Treat

Journal ID (iso-abbrev): Technol Cancer Res Treat

Journal ID (publisher-id): TCT

Journal ID (hwp): sptct

Title: Technology in Cancer Research & Treatment

Publisher: SAGE Publications (Sage CA: Los Angeles, CA )

ISSN (Print): 1533-0346

ISSN (Electronic): 1533-0338

Publication date (Electronic): 5 October 2020

Publication date Collection: 2020

Volume: 19

Electronic Location Identifier: 1533033820957002

Affiliations

[1 ]Department of Radiation Oncology, Fujian Cancer Hospital & Fujian Medical University Cancer Hospital, Fuzhou, Fujian, China

[2 ]School of Nuclear Science and Technology, University of South China, Hengyang, China

[3 ]Department of Radiology, Fujian Cancer Hospital & Fujian Medical University Cancer Hospital, Fuzhou, Fujian, China

[4 ]Department of Radiation and Cellular Oncology, The University of Chicago Medicine, IL, USA

Author notes

[*]Bai Penggang, Department of Radiation Oncology, Fujian Cancer Hospital & Fujian Medical University Cancer Hospital, Fuzhou, Fujian 350014, China. Email: littwind@ 123456sina.cn

Author information

Chen Jihong https://orcid.org/0000-0002-4496-507X

Article

Publisher ID: 10.1177_1533033820957002

DOI: 10.1177/1533033820957002

PMC ID: 7543127

PubMed ID: 33016230

SO-VID: 3a65fc56-2d81-4f53-98e5-c2b56308ad7a

License:

This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License ( https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages ( https://us.sagepub.com/en-us/nam/open-access-at-sage).

History

Date received : 5 May 2020

Date revision received : 24 June 2020

Date accepted : 9 July 2020

Funding

Funded by: Science and Technology program of Fujian Province, https://doi.org/10.13039/501100005270;

Award ID: 2018Y2003

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cover-date January-December 2020

typesetter ts3

Keywords: cervical cancer,deep learning,automatic plan,intensity-modulated radiation therapy

Data availability:

Keywords: cervical cancer, deep learning, automatic plan, intensity-modulated radiation therapy

Automated Intensity Modulated Radiation Therapy Treatment Planning for Cervical Cancer Based on Convolution Neural Network

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Computer Vision, Deep Learning, Deep Reinforcement Learning, IoT

Most cited references 41

Dermatologist-level classification of skin cancer with deep neural networks

A survey on deep learning in medical image analysis

Representation learning: a review and new perspectives.

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