Hidetaka Arimura Laboratory, Kyushu University

In our AI-based oncology researches, we are making use of an eclipse radiation treatment planning system (Varian Medical Systems) for delineation of tumor contours, treatment planning, and so on.

Papers 2023-5-Egashira

Magnetic Resonance-Based Imaging Biopsy with Signatures Including Topological Betti Number Features for Prediction of Primary Brain Metastatic Sites

Mai Egashira, Hidetada Arimura, Kazuma Kobayashi, Kazutoshi Moriyama, Takumi Kodama, Tomoki Tokuda, Kenta Ninomiya, Hiroyuki Okamoto, Hiroshi Igaki
Physical and Engineering Sciences in Medicine (Published: 21 August 2023)
https://doi.org/10.1007/s13246-023-01308-6

Abstract:This study incorporated topology Betti number (BN) features into the prediction of primary sites of brain metastases and the construction of magnetic resonance (MR)-based imaging biopsy (MRB) models. The significant features of the MRB model were selected from those obtained from gray-scale and three-dimensional wavelet-filtered images, BN and inverted BN (iBN) maps, and clinical variables (age and gender). The results suggest that the BN signature boosted the performance of MRB for the identification of primary sites of brain metastases including small tumors.We investigated an approach for predicting recurrence after radiation therapy using local binary pattern (LBP)-bas
Keywords: Imaging Biopsy, Betti number,Topology

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Papers 2023-4-Cui

Deep learning model fusion improves lung tumor segmentation accuracy across variable training-to-test dataset ratios

Yunhao Cui, Hidetaka Arimura, Tadamasa Yoshitake, Yoshiyuki Shioyama, Hidetake Yabuuchi
Physical and Engineering Sciences in Medicine (Published: 07 August 2023)
https://doi.org/10.1007/s13246-023-01295-8

Abstract:This study aimed to investigate the robustness of a deep learning (DL) fusion model for low training-to-test ratio (TTR) datasets in the segmentation of gross tumor volumes (GTVs) in three-dimensional planning computed tomography (CT) images for lung cancer stereotactic body radiotherapy (SBRT). Three DL models, 3D U-Net, V-Net, and dense V-Net, were trained to segment the GTV regions. Nine fusion models were constructed with logical AND, logical OR, and voting of the two or three outputs of the three DL models. TTR was defined as the ratio of the number of cases in a training dataset to that in a test dataset. The voting fusion model achieved the highest DSCs of 0.829 to 0.798 for all TTRs among the 12 models. The findings suggest that the proposed voting fusion model is a robust approach for low TTR datasets in segmenting GTVs in planning CT images of lung cancer SBRT.

Keywords: deep learning, lung cancer

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Papers 2023-3-Jin

CT image-based biopsy to aid prediction of HOPX expression status and prognosis for non-small cell lung cancer patients

Yu Jin, Hidetaka Arimura, YunHao Cui, Takumi Kodama, Shinichi Mizuno, Satoshi Ansai
Cancers 2023, 15(8), 2220, Published: 10 April 2023
https://doi.org/10.3390/cancers15082220

Abstract: Recent studies have found that the HOPX gene functions as a tumor suppressor, and its expression status influences patients’ survival in NSCLC. This study established an imaging biopsy with the radiogenomic signatures that links HOPX expression status and CT images to aid the prediction of HOPX expression status and the prognosis for lung cancer patients. Detecting gene expression status from CT images might be helpful to improve the accuracy of wet biopsy.
Keywords:HOPX; CT image features; imaging biopsy; non-small cell lung cancer; radiogenomics

Papers 2023-2-Ninomiya

Three-dimensional topological radiogenomics of epidermal growth factor receptor Del19 and L858R mutation subtypes on computed tomography images of lung cancer patients

Kenta Ninomiya, Hidetaka Arimura, Kentaro Tanaka, Wai Yee Chan, Yutaro Kabata, Shinichi Mizuno, Nadia Fareeda Muhammad Gowdh, Nur Adura Yaakup, Chong-Kin Liam, Chee-Shee Chai, Kwan Hoong Ng

Computer Methods and Programs in Biomedicine (accepted on Apr 7, 2023)
https://doi.org/10.1016/j.cmpb.2023.107544

Abstract: The objective of this study was to elucidate a novel radiogenomics approach using three-dimensional (3D) topologically invariant Betti numbers (BNs) for topological characterization of epidermal growth factor receptor (EGFR) Del19 and L858R mutation subtypes. 3DBN features, which showed a radiogenomic association with the characteristics of the EGFR Del19/L858R mutation subtypes, yielded higher accuracy for subtype classifications in comparison with conventional features.
Keywords: radiogenomics, computational topology, molecularly targeted drugs, precision medicine

Papers 2023-1-Ikushima

Topology-based radiomic features for prediction of parotid gland cancer malignancy grade in magnetic resonance images

Kojiro Ikushima, Hidetaka Arimura, Ryuji Yasumatsu, Hidemi Kamezawa, Kenta Ninomiya

Magnetic Resonance Materials in Physics, Biology and Medicine, (Published: 20 April 2023)
https://doi.org/10.1007/s10334-023-01084-0

Abstract:The malignancy grades of parotid gland cancer (PGC) have been assessed for decision of treatment policies. Therefore, we have investigated the feasibility of a topology-based radiomic features for prediction of parotid gland cancer (PGC) malignancy grade in magnetic resonance (MR) images.This study indicated that topology-based radiomic features could be feasible for the noninvasive prediction of the malignancy grade of PGCs.
Keywords: Radiomic features, Topology, Parotid gland cancer, Malignancy grade

Papers 2022-6-Kamezawa

Recurrence prediction with local binary pattern-based dosiomics in patients with head and neck squamous cell carcinoma

Kamezawa Hidemi, Arimura Hidetaka, et al.
Physical and Engineering Sciences in Medicine (Published: 05 December 2022)
https://doi.org/10.1007/s13246-022-01201-8

Abstract:We investigated an approach for predicting recurrence after radiation therapy using local binary pattern (LBP)-based dosiomics in patients with head and neck squamous cell carcinoma (HNSCC). LBP-based dosiomics models may be more accurate in predicting recurrence after radiation therapy in patients with HNSCC.
Keywords: recurrence prediction, head and neck carcinoma, local binary pattern, dosiomics

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Papers 2022-5-Nagami

Dual segmentation models for poorly and well-differentiated hepatocellular carcinoma using two-step transfer deep learning on dynamic contrast-enhanced CT images

Noriyuki Nagami, Hidetaka Arimura, Junichi Nojiri, Cui Yunhao, Kenta Ninomiya, Manabu Ogata, Mitsutoshi Oishi, Keiichi Ohira, Shigetoshi Kitamura, Hiroyuki Irie.

Physical and Engineering Sciences in Medicine (Published: 05 December 2022)
https://doi.org/10.1007/s13246-022-01202-7

Abstract: The aim of this study was to develop dual segmentation models for poorly and well-differentiated hepatocellular carcinoma (HCC), using two-step transfer learning (TSTL) based on dynamic contrast-enhanced (DCE) computed tomography (CT) images. The proposed model using TSTL from the lung cancer dataset showed the potential to segment poorly and well-differentiated HCC regions on DCE-CT images.
Keywords:  deep learning, hepatocellular carcinoma, dual segmentation, poorly differentiated, well-differentiated, transfer learning

Papers 2022-4-Moriyama

Feasibility for prediction of primary cancer sites of brain metastases based on Hessian index images

Kazutoshi MORIYAMA, Hidetaka ARIMURA, Kazuma KOBAYASHI, Quoc CUONG-LE, Akimasa URAKAMI, Kenta NINOMIYA, Takumi KODAMA, Hiroyuki OKAMOTO, Hiroshi IGAKI

Medical Imaging and Information Sciences 2022;39(3):57-67.(English abstract, Japanese body text)
https://doi.org/10.11318/mii.39.57

Abstract: The primary cancer sites for the brain metastasis site (BM) should be identified for selection of optimal treatment approaches. The proposed approach could have a potential for identifying primary cancer sites, but it should be improved.
Keywords: radiomics, Brain metastases, Machine learning, Hessian index

Papers 2022-3-Yamanouchi

Prediction of Intracranial Aneurysm Rupture Risk Using Non-Invasive Radiomics Analysis Based on Follow-Up Magnetic Resonance Angiography Images: A Preliminary Study

Yamanouchi Masayuki, Arimura Hidetaka, Kodama Takumi, Urakami Akimasa
Applied Sciences, 2022, 12(17), 8615
https://www.mdpi.com/2076-3417/12/17/8615

This study This is the first preliminary study to develop prediction models for aneurysm rupture risk using radiomics analysis based on follow-up magnetic resonance angiography (MRA) images. We selected 103 follow-up images from 18 unruptured aneurysm (UA) cases and 10 follow-up images from 10 ruptured aneurysm (RA) cases to build the prediction models. This prediction model with non-invasive MRA images could predict aneurysm rupture risk for SAH prevention.
Keywords: intracranial aneurysms; rupture risk; prediction model; radiomics; magnetic resonance angiography

Papers 2022-2-Kodama

Relapse predictability of topological signature on pretreatment planning CT images of stage I non-small cell lung cancer patients before treatment with stereotactic ablative radiotherapy

Kodama Takumi, Arimura Hidetaka, Shirakawa Yumi, Ninomiya Kenta, Yoshitake Tadamasa, Shioyama Yoshiyuki
Thoracic Cancer, 16 June, 2022
https://doi.org/10.1111/1759-7714.14483

This study aimed to explore the predictability of topological signatures linked to the locoregional relapse (LRR) and distant metastasis (DM) on pretreatment planning computed tomography images of stage I non-small cell lung cancer (NSCLC) patients before treatment with stereotactic ablative radiotherapy (SABR)
Keywords: lung cancer, topology, stereotactic ablative radiotherapy

Papers 2022-1-Ninomiya

Synergistic combination of a topologically invariant imaging signature and a biomarker for the accurate prediction of symptomatic radiation pneumonitis before stereotactic ablative radiotherapy for lung cancer: A retrospective analysis

Kenta Ninomiya, Hidetaka Arimura, Tadamasa Yoshitake, Taka-aki Hirose, Yoshiyuki Shioyama
PLOS ONE, January 31, 2022
https://doi.org/10.1371/journal.pone.0263292

We aimed to explore the synergistic combination of a topologically invariant Betti number (BN)-based signature and a biomarker for the accurate prediction of symptomatic (grade ≥ 2) radiation-induced pneumonitis (RP+) before stereotactic ablative radiotherapy (SABR) for lung cancer.
Keywords: Deep learning, Segmentation, Dense V-Networks, Lung stereotactic, Body radiation therapy

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Papers 2021-3-Urakami

Stratification of prostate cancer patients into low- and high-grade groups using multiparametric magnetic resonance radiomics with dynamic contrast-enhanced image joint histograms

Akimasa Urakami, Hidetaka Arimura, Yukihisa Takayama, Fumio Kinoshita, Kenta Ninomiya, Kenjiro Imada, Sumiko Watanabe, Akihiro Nishie, Yoshinao Oda, Kousei Ishigami
The Prostate, Published December /08/2021
DOI:https://doi.org/10.1002/pros.24278

This study aimed to investigate the potential of stratification of prostate cancer patients into low- and high-grade groups (GGs) using multiparametric magnetic resonance (mpMR) radiomics in conjunction with two-dimensional (2D) joint histograms computed with dynamic contrast-enhanced (DCE) images. This study suggests that the proposed approach could have the potential to stratify prostate cancer patients into low- and high-GGs.
Keywords: prostate cancer, grade group, multiparametric MR, dynamic contrast-enhanced images, joint histogram

PPapers 2021-2-Cui

Automated approach for segmenting gross tumor volumes for lung cancer stereotactic body radiation therapy using CT-based dense V-networks

Cui YunHao, Hidetaka Arimura, Risa Nakano, Tadamasa Yoshitake, Yoshiyuki Shioyama, Hidetake Yabuuchi
Journal of Radiation Research, Volume 62, Issue 2, March 2021, Pages 346-355
Published : 22 January 2021
DOI:https://doi.org/10.1093/jrr/rraa132

Abstract:The aim of this study was to develop an automated segmentation approach for small gross tumor volumes (GTVs) in 3D planning CT images using dense V-networks (DVNs) that offer more advantages in segmenting smaller structures than conventional V-networks. Regions of interest (ROI) with dimensions of 50 × 50 × 6-72 pixels in the planning CT images were cropped based on the GTV centroids when applying stereotactic body radiotherapy (SBRT) to patients.
Keywords: Deep learning, Segmentation, Dense V-Networks, Lung stereotactic, Body radiation therapy

HPapers 2021-1-Ninomiya

Robust identification of EGFR mutated NSCLC patients from three countries using Betti numbers

Kenta Ninomiya, Hidetaka Arimura, Wai Yee Chan, Kentaro Tanaka, Shinichi Mizuno, Nadia Fareeda Muhammad Gowdh, Nur Adura Yaakup, Chong-Kin Liam, Chee-Shee Chai, Kwan Hoong Ng
Published by PLOS ONE, 11 January 2021
DOI:https://doi.org/10.1371/journal.pone.0244354

Abstract:We have proposed a novel robust radiogenomics approach to the identification of epidermal growth factor receptor (EGFR) mutations among patients with non-small cell lung cancer (NSCLC) using Betti numbers (BNs).
The proposed model showed higher robustness than the conventional models in the identification of EGFR mutations among NSCLC patients.
The results suggested the robustness of the BN-based approach against
Keywords:Homology, Radiogenomics, EGFR driver oncogene, Molecularly, Targeted therapy, Imaging biopsy

Papers 2020-8-Le

Radiomic features based on Hessian index for prediction of prognosis in head-and-neck cancer patients

Quoc Le, Hidetaka Arimura, Kenta Ninomiya, Yutaro Kabata
Scientific Reports 10, Article number: 21301 (2020)
Published: 04 December 2020
DOI: https://www.nature.com/articles/s41598-020-78338-7

Purpose:This study proposed novel radiomic features based on the Hessian index
of differential topology for the prediction of prognosis prior to treatment in head-and-neck (HN) cancer patients. The Hessian index, which can indicate tumor heterogeneity with convex, concave, and other points (saddle points), was calculated as the number of negative eigenvalues of the Hessian matrix at each voxel on computed tomography(CT)images.
Result:This result indicates that index features could provide more prognostic information than conventional features and further increase the prognostic value of clinical variables in HN cancer patients
Keywords:Head-and-neck cancer, Novel radiomics, CT images, Hessian index, Survival analysis

Papers 2020-7-HiroseH

Radiomic prediction of radiation pneumonitis on pretreatment planning computed tomography images prior to lung cancer stereotactic body radiation therapy

Taka-aki Hirose, Hidetaka Arimura, Kenta Ninomiya, Tadamasa Yoshitake, Jun-ichi Fukunaga, Yoshiyuki Shioyama
Scientific Reports, 10, Article number: 20424(2020)
Published :24 November 2020
DOI:https://doi.org/10.1038/s41598-020-77552-7

Abstract:This study developed a radiomics-based predictive model for radiation-induced pneumonitis (RP) after lung cancer stereotactic body radiation therapy (SBRT) on pretreatment planning computed tomography (CT) images. The radiomic features calculated on pretreatment planning CT images could be predictive imaging biomarkers for RP after lung cancer SBRT
Keywords:Radiomics-based predictive model, Radiation-induced pneumonitis, Lung cancer stereotactic bodyradiation therapy, Pretreatment planning CT images, Imaging biomarkers

Papers 2020-6-FitriaH

Automated classification of urinary stones based on microcomputed tomography images using convolutional neural network

Leni Aziyus Fitria, Freddy Haryanto, Hidetaka Arimura, Cui YunHao,Kenta Ninomiya, Risa Nakano, Mohammad Haekal, Yuni Warty, Umar Fauzi
Physica Medica: European Journal of Medical Physics, Volume 78 Page 201-208
Published:08 October 2020
DOI: https://doi.org/10.1016/j.ejmp.2020.09.007

Purpose:The classification of urinary stones is important prior to treatment because the treatments depend on three types of urinary stones, i.e., calcium, uric acid, and mixture stones. We have developed an automatic approach for the classification of urinary stones into the three types based on microcomputed tomography (micro-CT) images using a convolutional neural network (CNN).
Conclusion:The proposed automated CNN-based approach could successfully classify urinary stones into three types, namely calcium, uric acid, and mixture stones, using micro-CT images.
Keywords:Convolutional neural networkEnergy dispersive X-ray spectraMicro-CTUrinary stones

Papers 2020-5-HossainH

Automated Approach for Estimation of Grade Groups for Prostate Cancer based on Histological Image Feature Analysis

Alamgir Hossain, Hidetaka ARIMURA, Fumio Kinoshita, Kenta Ninomiya, Sumiko Watanabe, Kenjiro Imada, Ryoma Koyanagi, Yoshinao Oda
The Prostate, Volume 80 Issue 3 Page 291-302,
Published: 15 February 2020
DOI:10.1002/pros.23943

Background: There is a low reproducibility of the Gleason scores that determine the grade group of prostate cancer given the intra‐ and interobserver variability among pathologists.
This study aimed to develop an automated approach for estimating prostate cancer grade groups based on features obtained from histological image analysis.
Conclusions: Our results suggest that the proposed approach may support pathologists during the evaluation of grade groups for prostate cancer, thus mitigating intra‐ and interobserver variability.
Keywords:Gleason score, grade group, histological image features, International Society of Urological Pathology (ISUP), piecewise step function

Papers 2020-4-NinomiyaH

Homological radiomics analysis for prognostic prediction in lung cancer patients

Kenta NINOMIYA, Hidetaka ARIMURA
Physica Medica: European Journal of Medical Physics, Volume 69 Page 90-100,
Published: 01 Januray 2020
DOI: https://doi.org/10.1016/j.ejmp.2019.11.026

Purpose: This study explored a novel homological analysis method for prognostic prediction in lung cancer patients.
Conclusion: This study demonstrates the excellent potential of HFs for prognostic prediction in lung cancer patients.
Keywords: Homology, Topologically invariant, Betti number, Radiomics, Lung cancer, Survival prediction, Cox proportional hazard model

Papers 2020-3-HiroseH

Observer Uncertainties of Soft Tissue-based Patient Positioning in IGRTwith artificial intelligence for precision medicine in radiation therapy

Taka-aki Hirose, Hidetaka Arimura, Jun-ichi Fukunaga, Saiji Ohga, Tadamasa Yoshitake, Yoshiyuki Shioyama
Journal of Applied Clinical Medical Physics, Volume 21, Issue 2, Pages: 73-81, February 2020
Doi:org/10.1002/acm2.12817

Purpose: There remain uncertainties due to inter‐ and intraobserver variability in soft‐tissue‐based patient positioning even with the use of image‐guided radiation therapy (IGRT). This study aimed to reveal observer uncertainties of soft‐tissuebased patient positioning on cone‐beam computed tomography (CBCT) images for prostate cancer IGRT.
Conclusion: Intraobserver variability was sufficiently small and would be negligible. However, uncertainties due to interobserver variability for soft‐tissue‐based patient positioning using CBCT images should be considered in CTV‐to‐PTV margins.
Keywords:interobserver variation, intraobserver variation, prostate cancer image‐guided radiation therapy, PTV margin, soft‐tissue‐based patient positioning.

Papers 2020-2-KaiH

Semi-automated prediction approach of target shifts using machine learning with anatomical features between planning and pretreatment CT images in prostate radiotherapy

Yudai Kai, Hidetaka Arimura, Kenta Ninomiya, Tetsuo Saito, Yoshinobu Shimohigashi, Akiko Kuraoka, Masato Maruyama, Ryo Toya, Natsuo Oya
Journal of Radiation Research,Volume 61, Issue 2, March 2020, Pages 285-297
Publisehd: 29 Januray 2020
Doi.org/10.1093/jrr/rrz105

The goal of this study was to develop a semi-automated prediction approach of target shifts using machine learning architecture (MLA) with anatomical features for prostate radiotherapy. Our hypothesis was that anatomical features between planning computed tomography (pCT) and pretreatment cone-beam computed tomography (CBCT) images could be used to predict the target, i.e. clinical target volume (CTV) shifts, with small errors.
In conclusion, this study developed a semi-automated prediction approach to CTV shifts using five types ofMLAs with anatomical features between pCT and pretreatment CBCT images for improvement of the positioning PCa patients in IGRT.

Papers 2020-1-Haseai

Similar-cases-based planning approaches with beam angle optimizations using water equivalent path length for lung stereotactic body radiation therapy

Shu Haseai, Hidetaka Arimura, Kaori Asai, Tadamasa Yoshitake, Yoshiyuki Shioyama
Radiological Physics and Technology, 13, 119-127,(2020)
Published: 14 March 2020
Doi.org/10.1007/s12194-020-00558-3

This study aimed to propose automated treatment planning approaches based on similar cases with beam angle optimizations using water equivalent path length (WEPL) to avoid lung and rib doses for lung stereotactic body radiation therapy (SBRT)
This study indicates a potential of similar cases, whose beam angle configurations were optimized with WEPL to avoid lung and rib doses in lung SBRT plans.
Keywords Automated treatment planning, Similar cases, Lung stereotactic body radiation therapy, Optimization, Water equivalent path length

Review paper

Radiomics with artificial intelligence for precision medicine in radiation therapy

Hidetaka Arimura, Mazen Soufi, Hidemi Kamezawa, Kenta Ninomiya,Masahiro Yamada
Journal of Radiation Research, Vol. 60, Issue 1, January 2019, pp. 150-157, 2019.01
Publshed: 22 September 2018
Doi: 10.1093/jrr/rry077

Recently, the concept of radiomics has emerged from radiation oncology. It is a novel approach for solving the issues of precision medicine and how it can be performed, based on multimodality medical images that are noninvasive, fast and low in cost.
Radiomics is the comprehensive analysis of massive numbers of medical images in order to extract a large number of phenotypic features (radiomic biomarkers) reflecting cancer traits, and it explores the associations between the features and patients’ prognoses in order to improve decision-making in precision medicine.
Therefore, radiomic approaches, in combination with AI, may potentially enable practical use of precision medicine in radiation therapy by predicting outcomes and toxicity for individual patients.
Keywords: radiomics; artificial intelligence; precision medicine; radiation therapy; medical images; cancer traits

Books

「レディオミクス入門」
著者：有村 秀孝 編、角谷 倫之 編　
発売日：2021/10/19、発行元：オーム社
ISBN978-4-274-22638-0

医療分野にもAIが本格的に導入されつつある中で、AIを活用して網羅的な解析を行うレディオミクスが注目されています。本書は，レディオミクスについて系統的にまとめた初めての書籍です
レディオミクスの概要、レディオミクスの応用例、各応用例の精度、オープンソフトウェア紹介などで構成しています。レディオミクスは複雑な概念を含むので、図を多数掲載した、わかりやすいレディオミクスの入門テキストです。

H

「放射線治療AIと外科治療AI 医療AIとディープラーニングシリーズ」
著者：藤田広志シリーズ監修、有村秀孝編、諸岡健一編、2020/04/21、発行元：オーム社
分担執筆：「はじめに」、「Ⅱ放射線治療AI編 Chapter 1 放射線治療AIの概要」
ISBN978-4-274-22547-5

放射線治療と外科治療に関して、最新の内容をコンパクトにまとめてあります。
現在進行形のテーマをとりあげ現状と今後の進展について初学者にもわかるように、AI技術の必要性から始めてディープラーニングだけでなく、広くAI技術（機械学習を含む）を使った内容を中心に紹介しています。

A

Image-Based Computer-Assisted Radiation Therapy

Edited by Hidetaka Arimura
Springer, April 2017
ISBN:978-981-10-2943-1
https://doi.org/10.1007/978-981-10-2945-5

This book provides a comprehensive overview of the state-of-the-art computational intelligence research and technologies in computer-assisted radiation therapy based on image engineering. It also traces major technical advancements and research findings in the field of image-based computer-assisted radiation therapy.
In high-precision radiation therapies, novel approaches in image engineering including computer graphics, image processing, pattern recognition, and computational anatomy play important roles in improving the accuracy of radiation therapy and assisting decision making by radiation oncology professionals, such as radiation oncologists, radiation technologists, and medical physicists, in each phase of radiation therapy.
All the topics presented in this book broaden understanding of the modern medical technologies and systems for image-based computer-assisted radiation therapy. Therefore this volume will greatly benefit not only radiation oncologists and radiologists but also radiation technologists, professors in medical physics or engineering, and engineers involved in the development of products to utilize this advanced therapy.

Others

AFOMP Monthly Webinar, Sept 2, 2021: Radiomics and Radiogenomics with AI for Oncology
Topi: Radiomics and Radiogenomics with AI for Oncology
Speaker: Dr Arimura Hidetaka, Moderator: Dr. Hui-Yu Tsai

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