Precision Medical Imaging in Big Data: Radiomics_CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

Authors：

 WANGMin ,  SONGBin , HUANGZi-xing , CHENJie , HUFu-bi

Radiomics; Radiology; Precision medicine; Diagnosis;

Corresponding author：

SONGBin, Email: cjr.songbin@vip.163.com

Keywords：

放射组学; 放射学; 精准医学; 诊断

DOI：

10.7507/1007-9424.20160198

Video：

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Objective To summarize the development of process and clinical practice for radiomics. Methods Relevant literatures about the development of process and clinical practice of radiomics were collected to make a review. Results Radiomics, which resulting from big data, had been used in diagnosis, assessment of prognosis, and predictionof therapy response for neoplasm. Conclusion Radiomics is an important part of precision medical imaging in the eraof big data.

Citation： WANGMin, SONGBin, HUANGZi-xing, CHENJie, HUFu-bi. Precision Medical Imaging in Big Data: Radiomics. CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY, 2016, 23(6): 752-755. doi: 10.7507/1007-9424.20160198 Copy

1.	蒋力扬, 孟雪, 于金明. 用分子影像指导肿瘤精准治疗. 中华核医学与分子影像杂志, 2016, 36(1): 3-6.2 方驰华, 陶海粟. 影像学新技术在肝胆胰外科精准诊断与治疗中的应用. 中华消化外科杂志, 2016, 15(1): 22-26.
2.	方驰华, 陶海粟. 影像学新技术在肝胆胰外科精准诊断与治疗中的应用. 中华消化外科杂志, 2016, 15(1): 22-26.
3.	黄从云, 陈振松, 孙俊旗, 等. 精准肝癌切除术( 附32 例报道). 中国普外基础与临床杂志, 2010, 17(6): 532-537.
4.	冯晓源. 精准医疗，影像先行. 中华放射学杂志, 2016, 50(1): 1-2.
5.	Schuster SC. Next-generation sequencing transforms today’s biology. Nat Methods, 2008, 5(1): 16-18.
6.	Baumann M, H?lscher T, Begg AC. Towards genetic prediction of radiation responses: ESTRO’s GENEPI project. Radiother Oncol, 2003, 69(2): 121-125.
7.	Segal E, Sirlin CB, Ooi C, et al. Decoding global gene expression programs in liver cancer by noninvasive imaging. Nat Biotechnol, 2007, 25(6): 675-680.
8.	Diehn M, Nardini C, Wang DS, et al. Identification of noninvasive imaging surrogates for brain tumor gene-expression modules. Proc Natl Acad Sci U S A, 2008, 105(13): 5213-5218.
9.	Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer, 2012, 48(4): 441-446.
10.	Kumar V, Gu Y, Basu S, et al. Radiomics: the process and the challenges. Magn Reson Imaging, 2012, 30(9): 1234-1248.
11.	Vallières M, Freeman CR, Skamene SR, et al. A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities. Phys Med Biol, 2015, 60(14): 5471-5496.
12.	Cunliffe A, Armato SG, Castillo R, et al. Lung texture in serial thoracic computed tomography scans: correlation of radiomics-based features with radiation therapy dose and radiation pneumonitis development. Int J Radiat Oncol Biol Phys, 2015, 91(5): 1048-1056.
13.	Parmar C, Rios Velazquez E, Leijenaar R, et al. Robust radiomics feature quantification using semiautomatic volumetric segmentation. PLoS One, 2014, 9(7): e102107.
14.	So RW, Tang TW, Chung AC. Non-rigid image registration of brain magnetic resonance images using graph-cuts. Pattern Recognit, 2011, 44(10-11): 2450-2467.
15.	Lu K, Higgins WE. Segmentation of the central-chest lymph nodes in 3D MDCT images. Comput Biol Med, 2011, 41(9): 780-789.
16.	Velazquez ER, Parmar C, Jermoumi M, et al. Volumetric CT-based segmentation of NSCLC using 3D-Slicer. Sci Rep, 2013, 3(12): 3529.
17.	Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology, 2016, 278(2): 563-577.
18.	O’Sullivan F, Roy S, O’Sullivan J, et al. Incorporation of tumor shape into an assessment of spatial heterogeneity for human sarcomas imaged with FDG-PET. Biostatistics, 2005, 6(2): 293-301.
19.	鲍玉斌, 王琢, 孙焕良, 等. 一种基于分形维的快速属性选择算法. 东北大学学报: 自然科学版, 2003, 24(6): 527-530.
20.	王耀南, 王绍源, 毛建旭. 基于分形维数的图像纹理分析. 湖南大学学报: 自然科学版, 2006, 33(5): 67-72.
21.	罗述谦. 医学图像处理与分析. 北京: 科学出版社, 2010: 50-56.
22.	Sun XX, Yu Q. Intra-tumor heterogeneity of cancer cells and its implications for cancer treatment. Acta Pharmacol Sin, 2015, 36(10): 1219-1227.
23.	Fisher R, Pusztai L, Swanton C. Cancer heterogeneity: implications for targeted therapeutics. Br J Cancer, 2013, 108(3): 479-485.
24.	Schwarz RF, Ng CK, Cooke SL, et al. Spatial and temporal heterogeneity in high-grade serous ovarian cancer: a phylogenetic analysis. PLoS Med, 2015, 12(2): e1001789.
25.	Rutman AM, Kuo MD. Radiogenomics: creating a link between molecular diagnostics and diagnostic imaging. Eur J Radiol, 2009, 70(2): 232-241.
26.	Wibmer A, Hricak H, Gondo T, et al. Haralick texture analysis of prostate MRI: utility for differentiating non-cancerous prostate from prostate cancer and differentiating prostate cancers with different Gleason scores. Eur Radiol, 2015, 25(10): 2840-2850.
27.	Honda T, Kondo T, Murakami S, et al. Radiographic and pathological analysis of small lung adenocarcinoma using the new IASLC classification. Clin Radiol, 2013, 68(1): e21-e26.
28.	Grove O, Berglund AE, Schabath MB, et al. Quantitative computed tomographic descriptors associate tumor shape complexity and intratumor heterogeneity with prognosis in lung adenocarcinoma. PLoS One, 2015, 10(3): e0118261.
29.	Li H, Zhu Y, Burnside ES, et al. MR imaging radiomics signatures for predicting the risk of breast cancer recurrence as given by research versions of MammaPrint, oncotype DX, and PAM50 gene assays. Radiology, 2016, [Epub ahead of print].
30.	Huang YQ, Liang CH, He L, et al. Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer. J Clin Oncol, 2016, [Epub ahead of print].
31.	Kuo MD, Gollub J, Sirlin CB, et al. Radiogenomic analysis to identify imaging phenotypes associated with drug response gene expression programs in hepatocellular carcinoma. J Vasc Interv Radiol, 2007, 18(7): 821-831.
32.	Antunes J, Viswanath S, Rusu M, et al. Radiomics analysis on FLTPET/ MRI for characterization of early treatment response in renal cell carcinoma: a Proof-of-Concept Study. Transl Oncol, 2016, 9(2): 155-162.
33.	Mattonen SA, Palma DA, Johnson C, et al. Detection of local cancer recurrence after stereotactic ablative radiation therapy for lung cancer: physician performance versus radiomic assessment. Int J Radiat Oncol Biol Phys, 2016, 94(5): 1121-1128.
34.	Jin JY, Kong FM. Personalized radiation therapy (PRT) for lung cancer. Adv Exp Med Biol, 2016, 890: 175-202.

1. 蒋力扬, 孟雪, 于金明. 用分子影像指导肿瘤精准治疗. 中华核医学与分子影像杂志, 2016, 36(1): 3-6.2 方驰华, 陶海粟. 影像学新技术在肝胆胰外科精准诊断与治疗中的应用. 中华消化外科杂志, 2016, 15(1): 22-26.
2. 方驰华, 陶海粟. 影像学新技术在肝胆胰外科精准诊断与治疗中的应用. 中华消化外科杂志, 2016, 15(1): 22-26.
3. 黄从云, 陈振松, 孙俊旗, 等. 精准肝癌切除术( 附32 例报道). 中国普外基础与临床杂志, 2010, 17(6): 532-537.
4. 冯晓源. 精准医疗，影像先行. 中华放射学杂志, 2016, 50(1): 1-2.
5. Schuster SC. Next-generation sequencing transforms today’s biology. Nat Methods, 2008, 5(1): 16-18.
6. Baumann M, H?lscher T, Begg AC. Towards genetic prediction of radiation responses: ESTRO’s GENEPI project. Radiother Oncol, 2003, 69(2): 121-125.
7. Segal E, Sirlin CB, Ooi C, et al. Decoding global gene expression programs in liver cancer by noninvasive imaging. Nat Biotechnol, 2007, 25(6): 675-680.
8. Diehn M, Nardini C, Wang DS, et al. Identification of noninvasive imaging surrogates for brain tumor gene-expression modules. Proc Natl Acad Sci U S A, 2008, 105(13): 5213-5218.
9. Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer, 2012, 48(4): 441-446.
10. Kumar V, Gu Y, Basu S, et al. Radiomics: the process and the challenges. Magn Reson Imaging, 2012, 30(9): 1234-1248.
11. Vallières M, Freeman CR, Skamene SR, et al. A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities. Phys Med Biol, 2015, 60(14): 5471-5496.
12. Cunliffe A, Armato SG, Castillo R, et al. Lung texture in serial thoracic computed tomography scans: correlation of radiomics-based features with radiation therapy dose and radiation pneumonitis development. Int J Radiat Oncol Biol Phys, 2015, 91(5): 1048-1056.
13. Parmar C, Rios Velazquez E, Leijenaar R, et al. Robust radiomics feature quantification using semiautomatic volumetric segmentation. PLoS One, 2014, 9(7): e102107.
14. So RW, Tang TW, Chung AC. Non-rigid image registration of brain magnetic resonance images using graph-cuts. Pattern Recognit, 2011, 44(10-11): 2450-2467.
15. Lu K, Higgins WE. Segmentation of the central-chest lymph nodes in 3D MDCT images. Comput Biol Med, 2011, 41(9): 780-789.
16. Velazquez ER, Parmar C, Jermoumi M, et al. Volumetric CT-based segmentation of NSCLC using 3D-Slicer. Sci Rep, 2013, 3(12): 3529.
17. Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology, 2016, 278(2): 563-577.
18. O’Sullivan F, Roy S, O’Sullivan J, et al. Incorporation of tumor shape into an assessment of spatial heterogeneity for human sarcomas imaged with FDG-PET. Biostatistics, 2005, 6(2): 293-301.
19. 鲍玉斌, 王琢, 孙焕良, 等. 一种基于分形维的快速属性选择算法. 东北大学学报: 自然科学版, 2003, 24(6): 527-530.
20. 王耀南, 王绍源, 毛建旭. 基于分形维数的图像纹理分析. 湖南大学学报: 自然科学版, 2006, 33(5): 67-72.
21. 罗述谦. 医学图像处理与分析. 北京: 科学出版社, 2010: 50-56.
22. Sun XX, Yu Q. Intra-tumor heterogeneity of cancer cells and its implications for cancer treatment. Acta Pharmacol Sin, 2015, 36(10): 1219-1227.
23. Fisher R, Pusztai L, Swanton C. Cancer heterogeneity: implications for targeted therapeutics. Br J Cancer, 2013, 108(3): 479-485.
24. Schwarz RF, Ng CK, Cooke SL, et al. Spatial and temporal heterogeneity in high-grade serous ovarian cancer: a phylogenetic analysis. PLoS Med, 2015, 12(2): e1001789.
25. Rutman AM, Kuo MD. Radiogenomics: creating a link between molecular diagnostics and diagnostic imaging. Eur J Radiol, 2009, 70(2): 232-241.
26. Wibmer A, Hricak H, Gondo T, et al. Haralick texture analysis of prostate MRI: utility for differentiating non-cancerous prostate from prostate cancer and differentiating prostate cancers with different Gleason scores. Eur Radiol, 2015, 25(10): 2840-2850.
27. Honda T, Kondo T, Murakami S, et al. Radiographic and pathological analysis of small lung adenocarcinoma using the new IASLC classification. Clin Radiol, 2013, 68(1): e21-e26.
28. Grove O, Berglund AE, Schabath MB, et al. Quantitative computed tomographic descriptors associate tumor shape complexity and intratumor heterogeneity with prognosis in lung adenocarcinoma. PLoS One, 2015, 10(3): e0118261.
29. Li H, Zhu Y, Burnside ES, et al. MR imaging radiomics signatures for predicting the risk of breast cancer recurrence as given by research versions of MammaPrint, oncotype DX, and PAM50 gene assays. Radiology, 2016, [Epub ahead of print].
30. Huang YQ, Liang CH, He L, et al. Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer. J Clin Oncol, 2016, [Epub ahead of print].
31. Kuo MD, Gollub J, Sirlin CB, et al. Radiogenomic analysis to identify imaging phenotypes associated with drug response gene expression programs in hepatocellular carcinoma. J Vasc Interv Radiol, 2007, 18(7): 821-831.
32. Antunes J, Viswanath S, Rusu M, et al. Radiomics analysis on FLTPET/ MRI for characterization of early treatment response in renal cell carcinoma: a Proof-of-Concept Study. Transl Oncol, 2016, 9(2): 155-162.
33. Mattonen SA, Palma DA, Johnson C, et al. Detection of local cancer recurrence after stereotactic ablative radiation therapy for lung cancer: physician performance versus radiomic assessment. Int J Radiat Oncol Biol Phys, 2016, 94(5): 1121-1128.
34. Jin JY, Kong FM. Personalized radiation therapy (PRT) for lung cancer. Adv Exp Med Biol, 2016, 890: 175-202.

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