The cassandra paradox: looking into the crystal Ball of radiomics in thoracic surgery

Jonathan M. Decker , Joanna Sesti , Amber L. Turner , Subroto Paul

Artificial Intelligence Surgery ›› 2022, Vol. 2 ›› Issue (2) : 57 -63.

PDF
Artificial Intelligence Surgery ›› 2022, Vol. 2 ›› Issue (2) :57 -63. DOI: 10.20517/ais.2022.05
Editorial

The cassandra paradox: looking into the crystal Ball of radiomics in thoracic surgery

Author information +
History +
PDF

Cite this article

Download citation ▾
Jonathan M. Decker, Joanna Sesti, Amber L. Turner, Subroto Paul. The cassandra paradox: looking into the crystal Ball of radiomics in thoracic surgery. Artificial Intelligence Surgery, 2022, 2(2): 57-63 DOI:10.20517/ais.2022.05

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

Ko JP,Kaur M.Pulmonary Nodules: growth rate assessment in patients by using serial CT and three-dimensional volumetry.Radiology2012;262:662-71 PMCID:PMC3267080

[2]

Henschke CI,Yip R.Writing Committee for the I-ELCAP InvestigatorsLung cancers diagnosed at annual CT screening: volume doubling times.Radiology2012;263:578-83 PMCID:PMC3329268

[3]

Hasegawa M,Takashima S.Growth rate of small lung cancers detected on mass CT screening.Br J Radiol2000;73:1252-9

[4]

Wilson DO,Fuhrman C.Doubling times and CT screen-detected lung cancers in the Pittsburgh Lung Screening Study.Am J Respir Crit Care Med2012;185:85-9 PMCID:PMC3262038

[5]

Jennings SG,Tann M,Dowdeswell I.Distribution of stage I lung cancer growth rates determined with serial volumetric CT measurements.Radiology2006;241:554-63

[6]

Winer-Muram HT,Tarver RD.Volumetric growth rate of stage I lung cancer prior to treatment: serial CT scanning.Radiology2002;223:798-805

[7]

Sala E,Himoto Y.Unravelling tumour heterogeneity using next-generation imaging: radiomics, radiogenomics, and habitat imaging.Clin Radiol2017;72:3-10 PMCID:PMC5503113

[8]

Yang F,Li Q.Intratumor heterogeneity predicts metastasis of triple-negative breast cancer.Carcinogenesis2017;38:900-9

[9]

Burrell RA,Bartek J.The causes and consequences of genetic heterogeneity in cancer evolution.Nature2013;501:338-45

[10]

Liu J,Wang XW.The significance of intertumor and intratumor heterogeneity in liver cancer.Exp Mol Med2018;50:e416 PMCID:PMC5992990

[11]

Morris LG,Desrichard A.Pan-cancer analysis of intratumor heterogeneity as a prognostic determinant of survival.Oncotarget2016;7:10051-63 PMCID:PMC4891103

[12]

Mayerhoefer ME,Langs G.Introduction to radiomics.J Nucl Med2020;61:488-95

[13]

Benoit-Cattin H. Texture analysis for magnetic resonance imaging. Available from: https://books.google.com.hk/books?hl=zh-CN&lr=&id=kNFKek4RtnkC&oi=fnd&pg=PA9&dq=Texture+Analysis+for+Magnetic+Resonance+Imaging&ots=PGtzjqs0Sy&sig=cQPfAbJy7KIqWCXJEijzRLabik4&redir_esc=y#v=onepage&q=Texture%20Analysis%20for%20Magnetic%20Resonance%20Imaging&f=false [Last accessed on 21 Mar 2022]

[14]

Laine A.Texture classification by wavelet packet signatures.IEEE Trans Pattern Anal Machine Intell1993;15:1186-91

[15]

Gerlinger M,Horswell S.Intratumor heterogeneity and branched evolution revealed by multiregion sequencing.N Engl J Med2012;366:883-92 PMCID:PMC4878653

[16]

Wilson R.Radiomics of pulmonary nodules and lung cancer.Transl Lung Cancer Res2017;6:86-91 PMCID:PMC5344835

[17]

Gillies RJ,Hricak H.Radiomics: images are more than pictures, they are data.Radiology2016;278:563-77 PMCID:PMC4734157

[18]

Lambin P,Leijenaar R.Radiomics: extracting more information from medical images using advanced feature analysis.Eur J Cancer2012;48:441-6 PMCID:PMC4533986

[19]

Soret M,Buvat I.Partial-volume effect in PET tumor imaging.J Nucl Med2007;48:932-45

[20]

Hatt M,van Baardwijk A,Pradier O.Impact of tumor size and tracer uptake heterogeneity in (18)F-FDG PET and CT non-small cell lung cancer tumor delineation.J Nucl Med2011;52:1690-7 PMCID:PMC3482198

[21]

Maley CC,Finley JC.Genetic clonal diversity predicts progression to esophageal adenocarcinoma.Nat Genet2006;38:468-73

[22]

Marusyk A,Polyak K.Intra-tumour heterogeneity: a looking glass for cancer?.Nat Rev Cancer2012;12:323-34

[23]

Chicklore S,Siddique M,Marsden PK.Quantifying tumour heterogeneity in 18F-FDG PET/CT imaging by texture analysis.Eur J Nucl Med Mol Imaging2013;40:133-40

[24]

Fisher R,Swanton C.Cancer heterogeneity: implications for targeted therapeutics.Br J Cancer2013;108:479-85 PMCID:PMC3593543

[25]

Petkovska I,McNitt-Gray MF.Pulmonary nodule characterization: a comparison of conventional with quantitative and visual semi-quantitative analyses using contrast enhancement maps.Eur J Radiol2006;59:244-52 PMCID:PMC1618788

[26]

Horeweg N,Vliegenthart R.Volumetric computed tomography screening for lung cancer: three rounds of the NELSON trial.Eur Respir J2013;42:1659-67

[27]

Hawkins S,Liu Y.Predicting malignant nodules from screening CT scans.J Thorac Oncol2016;11:2120-8 PMCID:PMC5545995

[28]

Lee SH,Goo JM,Kim YJ.Usefulness of texture analysis in differentiating transient from persistent part-solid nodules(PSNs): a retrospective study.PLoS One2014;9:e85167 PMCID:PMC3885675

[29]

Kido S,Higashiyama M,Kuroda C.Fractal analysis of small peripheral pulmonary nodules in thin-section CT: evaluation of the lung-nodule interfaces.J Comput Assist Tomogr2002;26:573-8

[30]

Liu Y,Balagurunathan Y.Radiomic features are associated with EGFR mutation status in lung adenocarcinomas.Clin Lung Cancer2016;17:441-8.e6 PMCID:PMC5548419

[31]

Weiss GJ,Miles KA.Noninvasive image texture analysis differentiates K-ras mutation from pan-wildtype NSCLC and is prognostic.PLoS One2014;9:e100244 PMCID:PMC4079229

[32]

Yip SS,Coroller TP.Associations between somatic mutations and metabolic imaging phenotypes in non-small cell lung cancer.J Nucl Med2017;58:569-76 PMCID:PMC5373502

[33]

Maldonado F,Raghunath S.Noninvasive characterization of the histopathologic features of pulmonary nodules of the lung adenocarcinoma spectrum using computer-aided nodule assessment and risk yield (CANARY)--a pilot study.J Thorac Oncol2013;8:452-60 PMCID:PMC3597987

[34]

Dong X,Wu P.Three-dimensional positron emission tomography image texture analysis of esophageal squamous cell carcinoma: relationship between tumor 18F-fluorodeoxyglucose uptake heterogeneity, maximum standardized uptake value, and tumor stage.Nucl Med Commun2013;34:40-6

[35]

Yang F,Dehdashti F.Temporal analysis of intratumoral metabolic heterogeneity characterized by textural features in cervical cancer.Eur J Nucl Med Mol Imaging2013;40:716-27 PMCID:PMC3625466

[36]

Cook GJ,Siddique M.Are pretreatment 18F-FDG PET tumor textural features in non-small cell lung cancer associated with response and survival after chemoradiotherapy?.J Nucl Med2013;54:19-26

[37]

Coroller TP,Narayan V.Radiomic phenotype features predict pathological response in non-small cell lung cancer.Radiother Oncol2016;119:480-6 PMCID:PMC4930885

[38]

Wang T,She Y.Radiomics signature predicts the recurrence-free survival in stage I non-small cell lung cancer.Ann Thorac Surg2020;109:1741-9

[39]

Papp L,Grahovac M,Beyer T.Optimized feature extraction for radiomics analysis of 18F-FDG PET imaging.J Nucl Med2019;60:864-72

[40]

Lasnon C,Lavigne B.18F-FDG PET/CT heterogeneity quantification through textural features in the era of harmonisation programs: a focus on lung cancer.Eur J Nucl Med Mol Imaging2016;43:2324-35

[41]

Pavic M,Würms X.Influence of inter-observer delineation variability on radiomics stability in different tumor sites.Acta Oncol2018;57:1070-4

[42]

da Silva GLF,Silva AC,Gattass M.Convolutional neural network-based PSO for lung nodule false positive reduction on CT images.Comput Methods Programs Biomed2018;162:109-18

[43]

Paul R,Balagurunathan Y.Deep feature transfer learning in combination with traditional features predicts survival among patients with lung adenocarcinoma.Tomography2016;2:388-95 PMCID:PMC5218828

[44]

Avanzo M,Pirrone G.Radiomics and deep learning in lung cancer.Strahlenther Onkol2020;196:879-87

[45]

Koike T,Yoshiya K,Suzuki R.Intentional limited pulmonary resection for peripheral T1 N0 M0 small-sized lung cancer.J Thorac Cardiovasc Surg2003;125:924-8

[46]

Keenan RJ,Maley RH Jr.Segmental resection spares pulmonary function in patients with stage I lung cancer.Ann Thorac Surg2004;78:228-33; discussion 228-33

[47]

Harada H,Sakamoto T,Tsubota N.Functional advantage after radical segmentectomy versus lobectomy for lung cancer.Ann Thorac Surg2005;80:2041-5

[48]

Okada M,Sakamoto T.Effect of tumor size on prognosis in patients with non-small cell lung cancer: the role of segmentectomy as a type of lesser resection.J Thorac Cardiovasc Surg2005;129:87-93

[49]

Yoshida J,Yokose T.Limited resection trial for pulmonary ground-glass opacity nodules: fifty-case experience.J Thorac Cardiovasc Surg2005;129:991-6

[50]

Altorki NK,Hanaoka T.I-ELCAP InvestigatorsSublobar resection is equivalent to lobectomy for clinical stage 1A lung cancer in solid nodules.J Thorac Cardiovasc Surg2014;147:754-62; Discussion 762-4

[51]

Dziedzic R,Marjanski T.Stage I non-small-cell lung cancer: long-term results of lobectomy versus sublobar resection from the Polish National Lung Cancer Registry.Eur J Cardiothorac Surg2017;52:363-9

[52]

Kates M,Wisnivesky JP.Survival following lobectomy and limited resection for the treatment of stage I non-small cell lung cancer<=1 cm in size: a review of SEER data.Chest2011;139:491-6

[53]

Wisnivesky JP,Swanson S.Limited resection for the treatment of patients with stage IA lung cancer.Ann Surg2010;251:550-4

[54]

Sadeghi AH,Taverne YJHJ.Virtual reality and artificial intelligence for 3-dimensional planning of lung segmentectomies.JTCVS Tech2021;7:309-21 PMCID:PMC8312141

[55]

Iizuka S,Yoshimura K.Predictors of indocyanine green visualization during fluorescence imaging for segmental plane formation in thoracoscopic anatomical segmentectomy.J Thorac Dis2016;8:985-91 PMCID:PMC4842821

AI Summary AI Mindmap
PDF

50

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/