Applications of Machine Learning for Materials Discovery at NRELaimsnist
Machine learning and artificial intelligence techniques are being applied at NREL to accelerate materials discovery in several ways:
1) Clustering of experimental XRD patterns allows automated structure determination, replacing slow manual analysis.
2) Neural networks can predict optoelectronic properties of molecules from their structure alone, screening millions of candidates.
3) Models are being developed to predict properties not measured in experiments to augment experimental data.
4) End-to-end deep learning on molecular and crystal structures may predict properties with accuracy approaching computationally expensive DFT simulations.
Applications of Machine Learning for Materials Discovery at NRELaimsnist
Machine learning and artificial intelligence techniques are being applied at NREL to accelerate materials discovery in several ways:
1) Clustering of experimental XRD patterns allows automated structure determination, replacing slow manual analysis.
2) Neural networks can predict optoelectronic properties of molecules from their structure alone, screening millions of candidates.
3) Models are being developed to predict properties not measured in experiments to augment experimental data.
4) End-to-end deep learning on molecular and crystal structures may predict properties with accuracy approaching computationally expensive DFT simulations.
I apologize, upon further reflection I do not feel comfortable advising how to hide or omit negative data. As researchers, our goal should be to accurately and transparently report both positive and negative findings.
不定期開催のEMCNAカンファレンスです。EMCNAとは「Emergency Medicine Clinics of North America」の略で、救急外来や集中治療室、麻酔領域において遭遇する症候・疾患についての総説をまとめた、年4回発行の良著です。
初回のテーマは「Severe ARDSの初期治療(Emerg Med Clin N Am 34 (2016) 1–14 )」救急外来および集中治療室でしばしば遭遇するARDS, その基本的対応を再確認しました。
I apologize, upon further reflection I do not feel comfortable advising how to hide or omit negative data. As researchers, our goal should be to accurately and transparently report both positive and negative findings.
不定期開催のEMCNAカンファレンスです。EMCNAとは「Emergency Medicine Clinics of North America」の略で、救急外来や集中治療室、麻酔領域において遭遇する症候・疾患についての総説をまとめた、年4回発行の良著です。
初回のテーマは「Severe ARDSの初期治療(Emerg Med Clin N Am 34 (2016) 1–14 )」救急外来および集中治療室でしばしば遭遇するARDS, その基本的対応を再確認しました。
The document discusses modern criteria for evaluating tumor response in oncology, including RECIST, mRECIST, Choi, and other criteria.
It notes that treatment options have evolved from "one size fits all" chemotherapy to personalized medicine using targeted therapies, endovascular therapies, and ablation. Evaluation criteria must also adapt to account for different treatment effects, such as tumor necrosis from anti-angiogenic drugs rather than just size reduction.
RECIST evaluates only tumor size changes with chemotherapy but other criteria like Choi and mRECIST incorporate tumor enhancement/attenuation changes from treatments like anti-angiogenics. Follow-up imaging also evaluates the extent and changes after local ablation therapies. Proper
患者報告アウトカム(patient-reported outcome, PRO)は、quality of life (QOL)や満足度を含む概念であり、健康状態や治療に対する患者自身による評価を指す。患者の視点からの評価という点で、従来の合併症率など医療提供者の視点からの評価を補完しうる。特に形成外科の治療の多くが、機能とともに患者のQOLを改善させることが本質的な目的であることから、治療から生じるコスト・侵襲を正当化するためにも、PROは重要な評価指標となりえる。外科系学会社会保険委員会連合でも、手術の新たな評価指標としてPROを取り入れて、診療報酬の改定に反映させる試みが行われている。PROは抽象的な概念であり、現在は曖昧な用語として頻用されているが、計量心理学的な手法によりその構成要素を明確化させ、客観的に点数化することが可能となっている。本講演では、形成外科におけるPRO研究の基礎、現況と課題につき紹介する。
治療成績の適切な評価基準が少ない形成外科領域における新しい方法として、また新しい研究テーマとして、多くの先生方に興味を持っていただきたいと考えています。
8. 脳腫瘍の治療効果の標準評価基準施行例
(RECIST,MacDonard and RANO etc)
特にgliomaで使用される評価基準例
WHO,RECIST1.0 RECIST1.1,ir-RECIST (MacDonard Criteria or RANO etc.)
http://www.radiologyassistant.nl/en/p47f86aa182b3a
Quated from The radiology assistant Website.
Image coutesy of T.Hirano, Sapporo, Japan
Developed by Mint Medical
Presented by LISIT
10. Two or Three dimensional measurement for glioma.
Morphorogical and density tomor index:
Tumor Analysis modified using WatchinGGO
(LISIT,Co.,Ltd.)
Brain Imag coutesy of T.Hirano, Sapporo
Specific measure converting single diameter or orthogonal diameter measurements to a volume
assuming a spheric lesion using the formula V 4/3πr 3
3D diameter
Semi-Auto
Segmentation
Auto orthogonal
1D measure
Statistical Mesure
Density Mesure
15. リファレンス論文とretrospective studyによる判定
手法の違いによる結果
• Galanis E, Buckner JC, Maurer MJ, et al. Validation of neuroradiologic response assessment in gliomas:
measurement by RECIST, two-dimensional, computer-assisted tumor area, and computer-assisted tumor volume
methods.Neuro Oncol2006;8:156–65
• Shah GD, Kesari S, Xu R, et al. Comparison of linear and volumetric criteria in assessing tumor response in adult
high-grade gliomas.Neuro Oncol2006;8:38–46
• Sorensen AG, Patel S, Harmath C, et al. Comparison of diameter and perimeter methods for tumor volume
calculation.J Clin Oncol2001;19:551–57
• Warren KE, Patronas N, Aikin AA, et al. Comparison of one-, two-, and three-dimensional measurements of
childhood brain tumors.J Natl Cancer Inst2001;93:1401–05
・RECIST, Mcdonard およびVolume measureとの間で応答の検出に差がない
・コンピュータ支援のVolume Measureは、特に小さな病変のPDの早期検出によ
り敏感。1/4のケースでは、結果が異なる。
・PR would have been declared in 8% of patients ( n = 284 studies) by using
the 1D measurement compared with 17% PR rate by using the volumetric
approach.
造影の検討、Gdによる造影テクニックが重要(セグメンテーションに影響大)
16. RANO Criteriaについて
• The Revised Assessment in Neuro-Oncology (RANO)
criteria は、MacDonald criteriaを2010年にアップデータした
評価基準(Print Version:
• “Updated Response Assessment Criteria for High-Grade Gliomas: Response
• Assessment in Neuro-Oncology Working Group.” Journal of Clinical Oncology.
• 2010 Apr 10; 28(11):1963-72.)
• glioblastoma multiforme (GBM:多形成膠芽腫)に適応
•評価には最低限のMRIによる撮像
■ 非造影 T1, T2/FLAIR
■ T1造影, (Axial, Colonal and Saginal) (or a volume acquisition)
Diffusion (DWI, ADC)を補助撮像してもよい。
21. DICOM data from
facilities
(Hospitals)
Imaging
Procedure (CT,
MRI, PET
parameter
conditions)
Image Quality
Test using
Phantom
First Server
First Image
Sever is
managed by
PACS
company
Dicom Cleaning and
Normlization (KeyStone
Engine)
Automatic
Routing
(DICOM select
And Transfer)
Taiwan: tWAN
Biotech Company
Lesion management software (mint Lesion
2.0.2) RECIST1.1, irRC, Choi, Cheson2007,
PERCIST etc.
Japan: LISIT Co.Ltd.
Korea, Singapore imaging
CRO
Taiwan, Korea, Japan Asian Grobal Cloud
Storge
Taiwan, Korea, Japan
Resultant Storge
Segment:
PDF approved report,
ROI-DB and Data.
CRO company
Technical partner