Artfcial Intelligence in Radiaton Oncology

Seong K Mun Virginia Tech, USA

Sonja Dieterich University of California, Davis, USA

Key Features

• ridges the gap between basic didactcs and rontline research, responding to the growing amount o literature or in radiaton oncolog

• Practcal while being broad enough to provide enough overview to develop an implementaton strateg

• Presents a possible ground breaking pathwa to improve the precision and scope o radiaton oncolog , especiall in treatment planning, personal therap , natural language processing, error mitgaton and productvit improvements

• nterdisciplinar collaboraton brings a rich combinaton o contributons, with plent o cross pollinaton among di erent felds o research bringing a nuanced perspectve

Descripton

The clinical use of Artfcial Intelligence (AI) in radiaton oncology is in its infancy. However, it is certain that is capable o making radiaton oncolog more precise and personali ed with improved outcomes. adiaton oncolog deplo s an arra o state o the art technologies or imaging, treatment, planning, simulaton, targetng, and ualit assurance while managing the massive amount o data involving therapists, dosimetrists, ph sicists, nurses, technologists, and managers. consists o man power ul tools which can process a huge amount o inter related data to improve accurac , productvit , and automaton in comple operatons such as radiaton oncolog .

his book o ers an arra o scientfc concepts, and technolog tools with selected e amples o current applicatons to serve as a one stop resource or the radiaton oncolog communit . he clinical adopton, be ond research, will re uire ethical consideratons and a ramework or an overall assessment o as a set o power ul tools.

30 renowned e perts contributed to si teen chapters organi ed into si sectons efne the uture, Strateg , ools, pplicatons, and ssessment and utcomes. he uture is defned rom a clinical and a technical perspectve and the strateg discusses lessons learned rom radiolog e perience in and the role o open access data to enhance the per ormance o tools. he tools include radiomics, segmentaton, knowledge representaton, and natural language processing. he applicatons discuss knowledge based treatment planning and automaton, based treatment planning, predicton o radiotherap to icit , radiomics in cancer prognostcaton and treatment response, and the use o or mitgaton o error propagaton. he si th secton elucidates two critcal issues in the clinical adopton ethical issues and the evaluaton o as a trans ormatve technolog .

January 2023

Imprint: World Scientfc Publishing Compan

Extent: 400pp

Type: e erence ook

Main Subject: Ph sics

Sub-Subjects: ioph sics, iological nd edical Ph sics pplied nd echnical Ph sics rtfcial ntelligence achine Learning ncolog Cancer esearch

Keywords: rtfcial ntelligence achine Learning edical Ph sics adiaton ncolog atural Language Processing reatment Planning edical maging mage Segmentaton

Readership: edical ph sicists, biomedical engineers, developers and engineers, radiaton oncologists, hospital managers in radiaton oncolog departments, medical technolog enthusiasts

• Defne the Future:

Clinical Radiaton Oncology in 2040 — Vision for Future RO from Clinical Perspectve

A Vision for Radiaton Therapy in 2030

• Strategy:

Lessons from the Artfcial Intelligence in Radiology for Radiaton Oncology

Open Access Data to Enable AI Applicatons in Radiaton Therapy

• AI Tools:

Science and Tools of Radiomics for Radiaton Oncology

Artfcial Intelligence for Image Segmentaton in Radiaton Oncology

Knowledge Representaton for Radiaton Oncology

Natural Language Processing for Radiaton Oncology

• AI Applicatons:

Knowledge-Based Treatment Planning: An Efcient and Reliable Planning Technique towards Treatment Planning

Automaton

Artfcial Intelligence in Radiaton Therapy Treatment Planning

Clinical Applicaton of AI for Radiaton Therapy Treatment Planning

Using AI to Predict Radiotherapy Toxicity Risk Based on Patent Germline Genotyping: Opportunites and Obstacles

Utlizaton of Radiomics in Cancer Prognostcaton and Treatment Response

How AI Can Help Us Understand and Mitgate Error Propagaton in Radiaton Oncology

• Assessment and Outcomes:

Ethics and rtfcial ntelligence in adiaton ncolog

Evaluaton o rtfcial ntelligence in adiaton ncolog