Добавил:
Sekretar
kiopkiopkiop18@yandex.ru
t.me/Prokururor I Вовсе не секретарь, но почту проверяю
Опубликованный материал нарушает ваши авторские права? Сообщите нам.
Вуз:
Предмет:
Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5525_Библиотеки_им_академика_М_И_Перельмана.pdf
Artificial Intelligence in Adaptive Radiation Therapy
merging method has been proposed to leverage the attention mechanism of deep
learning neural networks to integrate both deep learning features and radiomics
features, achieving enhanced classification accuracy [41].
Disease staging is part of diagnosis assessing the severity and progression of
disease. Specifically, cancer staging determines the extent of tumor growth and
spread within the body, for example using the TNM staging system. It helps predict
patient prognosis and guide treatment decisions. AI has been actively explored in
quantitative analysis of imaging and anatomical information for improved staging.
For example, an improved N staging system with better survival stratification was
proposed using quantitative spatial characterizations of lymph node tumor anatomy
for NPC patients [10]. In this study, new angle and distance descriptors were
designed for precise geometric localizations of the lymph node tumor relative to the
surrounding organs and selected using machine learning techniques for targeted
prediction of patient survival.
Disease phenotyping refers to the process of identifying and characterizing the
observable characteristics or traits of a disease, which is crucial in understanding the
manifestation and progression of diseases, as well as their susceptibility to treatment.
AI can be applied to predict the existing disease phenotypes from the routinely
generated medical data with major contributions in efficiency, low-cost, and noninvasiveness. For example, radiomics has been successfully applied in predicting the
status of HER2 and HR using ADC in invasive breast cancer [42]. The developed
radiomics signatures retained similar predictive values in treatment response
evaluation and therefore could serve as non-invasive surrogates of existing biomarkers. Another study discovered a high correlation between the inherent lung
texture information extracted by radiomics from CT images with pulmonary
function measurements, suggesting a potential fast and low-cost approach to
generate pulmonary function phenotyping for diagnosis and treatment guidance
[34, 43].
3.3.2 Personalized treatment
Personalized treatment has emerged as a promising approach to improve patient
outcomes in various cancers. It aims to tailor therapy based on specific patient
characteristics to achieve optimal results. The current progress in personalized
treatment has been driven by advancements in imaging analysis, such as radiomics
that provides valuable information about tumor characteristics, such as size and
texture, aiding in treatment planning and response assessment. One example of the
use of radiomics in NPC is the identification of patients who might benefit from
adjuvant chemotherapy for local-regionally advanced disease [ 33]. Radiomic-based
features extracted from pre-treatment imaging scans can be used to develop
predictive models and stratify patients based on their likelihood of treatment
response. This approach holds promise in optimizing treatment selection, minimizing unnecessary interventions, and improving outcomes for patients with NPC.
Adaptive radiation therapy (ART) is a dynamic treatment approach that enables
real-time adjustments to radiation therapy plans based on changes in a patient’s
3-9
Artificial Intelligence in Adaptive Radiation Therapy
anatomy, or other relevant factors during treatment. It takes into account the
dynamic nature of tumors and surrounding tissues, enhancing treatment accuracy
and improving patient outcomes. An integral component of ART involves the
integration of imaging techniques, such as cone-beam CT, throughout treatment to
provide information on changes in tumor size and location. The acquired imaging
data are then utilized to redefine treatment plans. Recent advancements in AI,
particularly in the field of radiomics, have shown promise in identifying patients who
may require ART prior to the initiation of RT. Radiomics features extracted from
pre-treatment T1-weighted (T1-w) and T2-weighted (T2-w) MR images within the
primary target for RT, along with a logistic regression model, can help identify
potential patients who may benefit from ART during radical concurrent chemoradiation therapy [44]. Additionally, radiomics features extracted from radiation
dose maps and contours depicting the relative position between the target and
organs at risk (OARs) have further confirmed the pivotal role of radiomics in
determining the need for ART [45]. Furthermore, studies have been conducted to
predict the suitability of thermoplastic masks, which may become ill-fitted due
to lymph node shrinkage [46]. Radiomics provides clinicians with an opportunity to
identify patients at risk of requiring ART due to tumor shrinkage, allowing for
increased attention and appropriate intervention. And the developed model can
benefit low-risk patients by potentially omitting weekly cone-beam CT scans,
thereby minimizing unnecessary radiation exposure.
3.3.3 Treatment outcome and prognosis prediction
Treatment response, as an important measure of the effectiveness of a given treatment,
helps clinicians determine whether a particular therapy is working and whether any
adjustments need to be made to the treatment plan. Common treatment response
indicators are tumor shrinkage, pathological response, and survival. Pathological
response to the novel sequential trans-arterial chemoembolization (TACE)–stereotactic
bodyradiotherapy(SBRT)–immunotherapy for unresectablehepatocellularcarcinoma
patientsweresuccessfullypredictedby the individualpre-treatmentradiomicsand deltaradiomics features from multi-phase contrast-enhanced MRI [47]. A recent study fused
multi-modal data fusion with label-softening technique alongside a multi-kernel-based
radial basis function (RBF) neural network, to mitigate the inherent disparity of
different data modalities for effective predictions of distant metastasis of NPC patients
[48]. Treatment outcome prediction often encounters the challenge of sample imbalance. Zhang et al addressed the challenge in survival predictionby a high-generalizable
classifier, multi-kernel regression with graph embedding (MERGE), and an imbalance
framework, sensitivity-based under-sampling (SUS), and achieved a promising performance in similar tasks [49].
Treatment side effects are another important consideration during treatment
planning and disease management. By leveraging diverse quantitative medical data,
AI models have shown great potential in predicting various side effects for radiation
oncology. Radiotherapy-induced acute oral mucositis is the most prevalent side
effect among NPC patients, which may compromise the quality of life or even
3-10
Artificial Intelligence in Adaptive Radiation Therapy
survival of NPC patients. Various factors have been discovered to be correlated with
the incidence and severity of oral mucositis, such as pre-treatment body mass index
(BMI) [50], nutrition status [51], and mean oral cavity dose [52]. One recent study
attempted the integration of radiomics features and dosiomics features and achieved
the best validation performance (mean AUC = 0.81) among the existing models [29].
Esophageal fi stula (EF) is a severe complication that occurs in 4%–25% of
patients undergoing radiotherapy treatment for esophageal cancer, which is correlated with clinical factors such as age and tumor grade [53, 54]. Recently, AI models
based on radiomics, and deep learning analysis of medical images have been applied
for personalized prediction of EF [55, 56]. The integration of dosiomics and
radiomics features from both the esophagus and gross tumor volume further
improved EF prediction [57].
Radiation pneumonitis is a type of lung injury caused by radiation and acute
radiation pneumonitis is one of the most severe complications that can occur within
six months after commencing radiotherapy treatment [58]. Dose–volume statistics
have been investigated but contain limited predictability, possibly due to the interpatient and intra-patient heterogeneity in response to radiation. One novel study by
Li et al attempted to integrate the dosimetric and radiomic information by
extracting radiomics-based lung texture information from different dose-interval
subregions and built a model with better performance than the whole lung model
[59]. Further integration of lung function information with a radiomics–dosiomics
model established from different lung function subregions demonstrated enhanced
performance in predicting radiation pneumonitis [60]. Acute radiation esophagitis is
another common complication during and after lung cancer treatment. One previous
study discovered that radiomics features from the CT images resulted in an AI
model with satisfactory performance in acute radiation esophagitis prediction [61].
3.4 Challenges and future directions of AI in clinical decision making
3.4.1 Challenges and concerns
3.4.1.1 Data quality and privacy
One of the prerequisites of training an accurate and generalizable model is the use of
high-quality data. Data with too much noise from either poor image quality or
mislabeled clinical outcomes often contain minimum information to be learnt by the
AI models. Highly biased data due to poorly standardized data acquisition protocols
will significantly degrade model generalizability. In addition to purposely generating
and acquiring high-quality data from a carefully designed experiment, such as a
clinical trial, several techniques have been proposed and applied to identify poorquality data. The direct uncertainty prediction model and Bayesian technique can be
used to evaluate the uncertainty of the prediction by training on samples with
multiple labels [62, 63]. Another technique called untrainable data cleansing can
identify potentially incorrect and noisy samples by inferencing the existing singlelabel samples, which is more practical for datasets with limited annotations [64].
Concerns regarding patient data privacy have long been raised throughout the
development of medical AI. Patient data are originally produced and managed by
3-11
Artificial Intelligence in Adaptive Radiation Therapy
healthcare institutions under the supervision of government or other public bodies.
However, collaborations between public and private sectors are often encouraged
for healthcare innovations, which may compromise patient privacy due to poor data
management on the private side or data leakage from the black-box models [65].
Regulations for the responsible use and management of patient data for medical AI
development are crucial for data privacy protection, but a balance should be
maintained so as not to slow down medical AI development.
3.4.1.2 Integration with existing healthcare systems
While many studies focus on using AI alone to perform a specific task, in reality, it is
more feasible for humans and AI to collaborate for improved efficiency while
minimizing risk. The impact of AI on the behavior and decision making of
healthcare providers still remains unclear, as it can vary according to their
experience and specific tasks [66]. On the other hand, integrating medical AI into
existing clinical routines poses significant challenges. Clinical professionals have
established a routine workflow without AI through decades of clinical practice,
leaving no room for AI to play its role without changing the current protocol. The
existing picture archiving and communication system (PACS) is mostly a closed
system, which forces many commercial medical AI applications to be developed as
separate systems such as web applications or widgets to interact with radiologists
and show interpretation results [67]. This makes the use of AI assistance a tedious
and repetitive process from the perspective of manual data transmission and results
input. Standards for data transition and the framework of integrated workflow are
needed to realize seamless AI integration.
3.4.2 Future directions
3.4.2.1 Emerging trends and technologies
The cross-disciplinary integration is a hot research topic in the current application of
AI in clinical settings. However, challenges related to data integration and feature
selection must be overcome to effectively merge multiple domains. Moreover, AI is
driving the transformation of the traditional healthcare model. Intelligent wearable
devices and the medical Internet of Things are advancing rapidly, allowing for realtime monitoring of patients’ physiological indicators. This progress is paving the
way for personalized healthcare decision making in the near future.
3.4.2.2 Reliability and interpretability
The development of trustworthy and reliable AI models is of paramount importance
for successful and responsible clinical deployment. Efforts should be made at
different stages of the AI model development to improve reliability, including
data preprocessing, feature selection, and model development. Proper data processing could reduce data bias and enhance the signal-to-noise ratio, thus helping with
model convergence and avoiding overfitting. Feature repeatability based on perturbation or test–retest should be considered during feature selection to ensure reliable
3-12
Artificial Intelligence in Adaptive Radiation Therapy
predictions. A more rigorous and standardized process is warranted for reliable AI
model development.
Model interpretability is another important aspect of model reliability as it can
help identify and correct biases, errors, or unwanted behaviors in the model, as well
as provide insights into the data and the problem domain. However, there is
currently no single best way to interpret a model, and different methods may have
different advantages and limitations depending on the context and the goal of the
interpretation. Section 3.2 has mentioned several interpretable methods that can be
explored further.
3.4.2.3 Closing the gap between research and practice
Despite the boom in medical AI, most of the approaches have been developed and
evaluated in a laboratory setting. Only less than one hundred of over 17 000 medical
AI studies were implemented in a real-life clinical setting [66]. While the research
setting is homogeneous, real clinical practice often encounters various situations. AI
models trained in a specific patient cohort or under a specific scenario may not be
adaptive to the diverse conditions in clinical practice. Continuous efforts in
validating the effectiveness of AI and promoting better study design will contribute
to bridging the gap between laboratory research and real clinical practice.
In addition, there remains the problem of how AI can be seamlessly integrated
into current clinical workflow. At the RSNA 2020 Annual Meeting, an Imaging AI
in Practice (IAIP) demonstration showed an AI-integrated workflow in a simulated
clinical environment, where AI functioned in several critical clinical decision-making
steps including imaging examination ordering, protocoling, acquisition, display,
interpretation, reporting, and follow-up [68]. It highlights the crucial role of
consistent interoperability standards and effective human–AI interaction in an
integrated circumstance. The demonstration shows a successful integration where
AI is no longer working as an additional unit in disease detection or treatment
selection only, but rather is fully integrated into and dramatically changes the
overall workflow of current clinical practice.
3.5 Summary
In this chapter, we introduced the major techniques and applications of AI in clinical
decision making. Clinical decision making happens in the entire spectrum of health
management, including disease diagnosis, treatment planning, and prognosis.
Making fast and accurate clinical decisions is critical for effective disease management and thus improving patients’ quality of life. In the era of AI, clinical decisions
can be more intelligent with more accurate information of patients’ conditions and
predictions of treatment outcomes. Numerous efforts have been made to cope with
the special characteristics of clinical data and develop advanced AI techniques for
model construction. Some well-developed AI models have been applied to different
clinical decision-making scenarios and obtained promising achievements, which
demonstrates AI’s ability to guide clinical practice and assist in clinical decision
making. In summary, it is believed that AI has the potential to be an efficient and
3-13
Artificial Intelligence in Adaptive Radiation Therapy
reliable assistant for healthcare providers in making clinical decisions. Further
research is needed to address the existing challenges and concerns. Only after that
can we harness the full potential of AI in clinical decision making and pave the way
for better healthcare outcome and personalized patient care.
References
[1] Patton D D 1978 Introduction to clinical decision making Semin. Nucl. Med. 8 273–82
[2] Chamberlin J et al 2021 Automated detection of lung nodules and coronary artery calcium
using artificial intelligence on low-dose CT scans for lung cancer screening: accuracy and
prognostic value BMC Med.
[3] Bhuiyan A, Wong T Y, Ting D S W, Govindaiah A, Souied E H and Smith R T 2020
Artificial intelligence to stratify severity of age-related macular degeneration (AMD) and
predict risk of progression to late AMD Transl. Vis. Sci. Technol.
[4] Schork N J 2019 Artificial intelligence and personalized medicine Precision Medicine in
Cancer Therapy
[5] Huang S, Yang J, Fong S and Zhao Q 2020 Artificial intelligence in cancer diagnosis and
prognosis: opportunities and challenges Cancer Lett.
[6] Titano J J et al 2018 Automated deep-neural-network surveillance of cranial images for acute
neurologic events Nat. Med.
[7] Nicolae A et al 2020 Conventional vs machine learning-based treatment planning in prostate
brachytherapy: results of a phase I randomized controlled trial Brachytherapy
[8] Dembrower K et al 2020 Effect of artificial intelligence-based triaging of breast cancer
screening mammograms on cancer detection and radiologist workload: a retrospective
simulation study Lancet Digit. Health
[9] Yoo Y J, Ha E J, Cho Y J, Kim H L, Han M and Kang S Y 2018 Computer-aided diagnosis
of thyroid nodules via ultrasonography: initial clinical experience Korean J. Radiol.
[10] Zhang J et al 2023 Quantitative spatial characterization of lymph node tumor for N stage
improvement of nasopharyngeal carcinoma patients Cancers
[11] Sevakula R K, Au-Yeung W M, Singh J P, Heist E K, Isselbacher E M and Armoundas A A
2020 State-of-the-art machine learning techniques aiming to improve patient outcomes
pertaining to the cardiovascular system J. Am. Heart Assoc.
[12] Nam J G et al 2019 Development and validation of deep learning-based automatic detection
algorithm for malignant pulmonary nodules on chest radiographs Radiology
[13] Cheng J Z et al 2016 Computer-aided diagnosis with deep learning architecture: applications
to breast lesions in US images and pulmonary nodules in CT scans Sci. Rep.
[14] Liu H et al 2021 A deep learning model integrating mammography and clinical factors
facilitates the malignancy prediction of BI-RADS 4 microcalcifications in breast cancer
screening Eur. Radiol.
[15] Graffy P M, Liu J, O’Connor S, Summers R M and Pickhardt P J 2019 Automated
segmentation and quantification of aortic calcification at abdominal CT: application of a
deep learning-based algorithm to a longitudinal screening cohort Abdom. Radiol.
[16] Joo B et al 2020 A deep learning algorithm may automate intracranial aneurysm detection
on MR angiography with high diagnostic performance Eur. Radiol.
[17] Jin L et al 2020 Deep-learning-assisted detection and segmentation of rib fractures from CT
scans: development and validation of FracNet eBioMedicine
ed D D Von Hoff and H Han (Cham: Springer International) pp 265–83
19 55
9 25
471 61–71
24 1337–41
19 470–6
2 e468–74
19 665
15 230
9 e013924
290 218–28
6 24454
31 5902–12
44 2921–8
30 5785–93
62 103106
3-14
Artificial Intelligence in Adaptive Radiation Therapy
[18] Yoon A P, Lee Y L, Kane R L, Kuo C F, Lin C and Chung K C 2021 Development and
validation of a deep learning model using convolutional neural networks to identify scaphoid
fractures in radiographs JAMA Netw. Open
[19] Wolf D M et al 2022 Redefining breast cancer subtypes to guide treatment prioritization
and maximize response: predictive biomarkers across 10 cancer therapies Cancer Cell.
609–623.e6
[20] Baxi V, Edwards R, Montalto M and Saha S 2022 Digital pathology and artificial
intelligence in translational medicine and clinical practice Mod. Pathol.
[21] Lamberti M J et al 2019 A study on the application and use of artificial intelligence to
support drug development Clin. Ther.
[22] Geady C, Shultz D B, Razak A R A, Schuetze S and Haibe-Kains B 2022 Radiomics in
sarcoma trials: a complement to RECIST for patient assessment J. Cancer Metastasis Treat.
8 45
[23] Tan D, Mohamad Salleh S A, Manan H A and Yahya N 2023 Delta-radiomics-based
models for toxicity prediction in radiotherapy: a systematic review and meta-analysis J. Med.
Imaging. Radiat. Oncol.
[24] Cousin F et al 2023 Radiomics and delta-radiomics signatures to predict response and
survival in patients with non-small-cell lung cancer treated with immune checkpoint
inhibitors Cancers
[25] Jeon S H et al 2019 Delta-radiomics signature predicts treatment outcomes after preoper-
ative chemoradiotherapy and surgery in rectal cancer Radiat. Oncol.
[26] Fave X, Zhang L, Yang J et al 2017 Using pretreatment radiomics and delta-radiomics
features to predict non-small cell lung cancer patient outcomes Int. J. Radiat. Oncol. Biol.
Phys.
98 249
[27] Braman N M et al 2017 Intratumoral and peritumoral radiomics for the pretreatment
prediction of pathological complete response to neoadjuvant chemotherapy based on breast
DCE-MRI Breast Cancer Res.
[28] van Griethuysen J J M et al 2017 Computational radiomics system to decode the
radiographic phenotype Cancer Res.
[29] Dong Y et al 2023 Multimodal data integration to predict severe acute oral mucositis of
nasopharyngeal carcinoma patients following radiation therapy Cancers
[30] Teng X et al 2022 Building reliable radiomic models using image perturbation Sci. Rep. 12
10035
[31] Teng X et al 2022 Improving radiomic model reliability using robust features from
perturbations for head-and-neck carcinoma Front. Oncol.
[32] Zhang Y P et al 2023 Artificial intelligence-driven radiomics study in cancer: the role of
feature engineering and modeling Mil. Med. Res.
[33] Teng X et al 2023 Explainable machine learning via intra-tumoral radiomics feature
mapping for patient stratification in adjuvant chemotherapy for locoregionally advanced
nasopharyngeal carcinoma Radiol. Med.
[34] Huang Y H et al 2023 Respiratory invariant textures from static computed tomography
scans for explainable lung function characterization J. Thorac. Imaging
[35] Xu C, Tao D and Xu C 2013 A survey on multi-view learning arXiv: 1304.5634v1
[36] Baltrušaitis T, Ahuja C and Morency L P 2019 Multimodal machine learning: a survey and
taxonomy IEEE Trans. Pattern Anal. Mach. Intell.
[37] Molnar, C., 2020. Interpretable Machine Learning (Victoria: LeanPub)
67 564–79
15 1968
19 57
4 e216096
40
35 23–32
41 1414–26
14 43
77 e104–7
15 2032
12 974467
10 22
128 828–38
38 286–96
41 423–43
3-15
Artificial Intelligence in Adaptive Radiation Therapy
[38] Hanif A, Zhang X and Wood S 2021 A survey on explainable artificial intelligence
techniques and challenges 2021 IEEE 25th Int. Enterprise Distributed Object Computing
Workshop (EDOCW) pp 81–9
[39] Takagi T and Sugeno M 1985 Fuzzy identification of systems and its applications to
modeling and control IEEE Trans. Syst. Man Cybern.
[40] Zhang Y et al 2022 Radiomics-based detection of COVID-19 from chest x-ray using
interpretable soft label-driven TSK fuzzy classifier Diagnostics
[41] Yang D et al 2023 Deep learning attention-guided radiomics for COVID-19 chest radio-
graph classification Quant. Imaging. Med. Surg.
[42] Teng X et al 2023 Noninvasive imaging signatures of HER2 and HR using ADC in invasive
breast cancer: repeatability, reproducibility, and association with pathological complete
response to neoadjuvant chemotherapy Breast. Cancer. Res.
[43] Faught A M et al 2017 Evaluating the toxicity reduction with computed tomographic
ventilation functional avoidance radiation therapy Int. J. Radiat. Oncol.
[44] Yu T T et al 2019 Pretreatment prediction of adaptive radiation therapy eligibility using
MRI-based radiomics for advanced nasopharyngeal carcinoma patients Front. Oncol.
[45] Lam S K et al 2021 Multi-organ omics-based prediction for adaptive radiation therapy
eligibility in nasopharyngeal carcinoma patients undergoing concurrent chemoradiotherapy
Front. Oncol.
[46] Lam S K et al 2022 A multi-center study of CT-based neck nodal radiomics for predicting an
adaptive radiotherapy trigger of ill-fitted thermoplastic masks in patients with nasophar-
yngeal carcinoma Life
[47] Ho L M, Lam S K, Zhang J, Chiang C L, Chan A C Y and Cai J 2023 Association of multi-
phasic MR-based radiomic and dosimetric features with treatment response in
unresectable hepatocellular carcinoma patients following novel sequential TACE-SBRT-
immunotherapy Cancers
[48] Sheng J, Lam S, Zhang J, Zhang Y and Cai J 2023 Multi-omics fusion with soft labeling for
enhanced prediction of distant metastasis in nasopharyngeal carcinoma patients after
radiotherapy Comput. Biol. Med.
[49] Zhang Y et al 2022 Integration of an imbalance framework with novel high-generalizable
classifiers for radiomics-based distant metastases prediction of advanced nasopharyngeal
carcinoma Knowl.-Based Syst.
[50] Saito N, Imai Y, Muto T and Sairenchi T 2012 Low body mass index as a risk factor of
moderate to severe oral mucositis in oral cancer patients with radiotherapy Support Care
Cancer Off. J. Multinatl. Assoc. Support Care Cancer
[51] Shu Z et al 2020 Nutritional status and its association with radiation-induced oral mucositis
in patients with nasopharyngeal carcinoma during radiotherapy: a prospective study Front.
Oncol.
[52] Orlandi E et al 2018 Multivariable model for predicting acute oral mucositis during
combined IMRT and chemotherapy for locally advanced nasopharyngeal cancer patients
Oral Oncol.
[53] Wang X et al 2022 Risk factors of esophageal fistula induced by re-radiotherapy for
recurrent esophageal cancer with local primary site BMC Cancer
[54] Zhu C, Wang S, You Y, Nie K and Ji Y 2019 Risk factors for esophageal fistula in
esophageal cancer patients treated with radiotherapy: a systematic review and meta-analysis
Oncol. Res. Treat.
11 792024
12 241
15 1105
168 107684
235 107649
10 594687
86 266–72
43 34–41
SMC-15 116–32
12 2613
13 572–84
25 77
99 325–33
9 1050
20 3373–7
22 207
3-16
Artificial Intelligence in Adaptive Radiation Therapy
[55] Xu Y et al 2021 Integrating clinical data and attentional CT imaging features for esophageal
fistula prediction in esophageal cancer Front. Oncol.
[56] Li Z, Gong J, Shi L, Li J, Yang Z, Chai G, Lv B, Xiang G, Wang B, Carr SR and Fiorelli A
2024 Clinical-radiomics nomogram for the risk prediction of esophageal fistula in patients
with esophageal squamous cell carcinoma treated with intensity-modulated radiation
therapy or volumetric-modulated arc therapy J. Thorac. Dis.
[57] Guo W, Li B, Xu W, Cheng C, Qiu C, Sam S K, Zhang J, Teng X, Meng L, Zheng X and
Wang Y 2024 Multi-omics and multi-VOIs to predict esophageal fistula in esophageal cancer
patients treated with radiotherapy J. Cancer Res. Clin. Oncol.
[58] Zander D S and Rassaei N 2018 Drug reactions and other iatrogenic pulmonary diseases
Pulmonary Pathology
C F Farver (Amsterdam: Elsevier) pp 396 –408
[59] Li B et al 2022 Lung subregion partitioning by incremental dose intervals improves omics-
based prediction for acute radiation pneumonitis in non-small-cell lung cancer patients
Cancers
[60] Li B et al 2022 Function-wise dual-omics analysis for radiation pneumonitis prediction in
lung cancer patients Front. Pharmacol.
[61] Zheng X et al 2023 Multi-omics to predict acute radiation esophagitis in patients with lung
cancer treated with intensity-modulated radiation therapy Eur. J. Med. Res
[62] Raghu M et al 2019 Direct uncertainty prediction for medical second opinions Proc. Mach.
Learn. Res. 97 5281–90
[63] Kendall A and Gal Y 2017 What uncertainties do we need in Bayesian deep learning for
computer vision? Advances in Neural Information Processing Systems vol 30 (Red Hook, NY:
Curran Associates)
[64] Dakka M A et al 2021 Automated detection of poor-quality data: case studies in healthcare
Sci. Rep.
[65] Murdoch B 2021 Privacy and artificial intelligence: challenges for protecting health
information in a new era BMC Med. Ethics
[66] Yin J, Ngiam K Y and Teo H H 2021 Role of artificial intelligence applications in real-life
clinical practice: systematic review J. Med. Internet Res.
[67] Blezek D J, Olson-Williams L, Missert A and Korfiatis P 2021 AI integration in the clinical
workflow J. Digit. Imaging
[68] Wiggins W F et al 2021 Imaging AI in practice: a demonstration of future workflow using
integration standards Radiol. Artif. Intell.
14 4889
11 18005
Foundations in Diagnostic Pathology 2nd edn ed D S Zander and
13 971849
https://proceedings.mlr.press/v97/raghu19a.html
22 122
34 1435–46
3 e210152
11 688706
16 2032–48
150 39
28 126
23 e25759
3-17
IOP Publishing
Artificial Intelligence in Adaptive Radiation Therapy
Yi Wang and X. Sharon Qi
Chapter 4
Imaging technologies in radiation therapy
Xinru Chen, Cenji Yu, Gregory Sharp and Jinzhong Yang
Imaging technologies play a pivotal role in the evolution of radiation therapy,
enabling the precise delivery of treatment while minimizing damage to surrounding
healthy tissues. In adaptive radiotherapy, the integration of advanced imaging
modalities is essential for treatment adjustments based on changes in tumor size,
position, and biological characteristics. The ability to dynamically assess and
respond to these changes enhances treatment accuracy, ensuring that radiation is
delivered optimally throughout the course of therapy. A variety of imaging
techniques, each with distinct capabilities, are currently available for use in radiation
therapy. These include computed tomography (CT) and magnetic resonance
imaging (MRI), positron emission tomography (PET), four-dimensional (4D)-CT
and 4D-MRI, and so on. This chapter focuses on the unique contribution of each
imaging modality to different stages of radiation therapy, from treatment planning
to treatment guidance, motion management, and post-treatment assessment. The
advancement of imaging technologies supports the growing need for personalized
and precise radiation therapy.
4.1 Introduction
Imaging is a central component of adaptive radiotherapy (ART), because it defines
the anatomic and functional changes to be addressed. The earliest proposal for ART
in 1997 already provided for the use of imaging to measure and inform geometric
changes in anatomy [1], and included a phase II study on off-line ART for prostate
cancer [2]. Off-line ART using cone beam computed tomography (CBCT), magnetic
resonance imaging (MRI), and functional imaging have been conducted as early as
the late 2000s [3, 4]. Pre-treatment online adaptive radiotherapy using MRI has been
used since 2014 [5], and using CBCT since at least 2020 [6].
In this chapter, we present the current state-of-the-art in imaging for radiation
therapy, with a focus on the technologies and methods used in adaptive therapy. Offline adaptive therapy is commonly performed when pre-treatment imaging using
doi:10.1088/978-0-7503-6119-4ch4 4-1 ª IOP Publishing Ltd 2025. All rights,
including for text and data mining (TDM), artificial intelligence (AI) training, and similar technologies, are reserved.