Literature DB >> 33315123

Ultrasound-guided targeted biopsies of CT-based radiomic tumour habitats: technical development and initial experience in metastatic ovarian cancer.

Lucian Beer1,2,3, Paula Martin-Gonzalez3,4, Maria Delgado-Ortet1,3, Marika Reinius1,3,4, Leonardo Rundo1,3, Ramona Woitek1,2,3, Stephan Ursprung1,3, Lorena Escudero1,3, Hilal Sahin1,3, Ionut-Gabriel Funingana3,4, Joo-Ern Ang3,4,5, Mercedes Jimenez-Linan6, Tristan Lawton7, Gaurav Phadke7, Sally Davey8, Nghia Q Nguyen9, Florian Markowetz3,4, James D Brenton3,4, Mireia Crispin-Ortuzar3,4, Helen Addley1,3,5, Evis Sala10,11.   

Abstract

PURPOSE: To develop a precision tissue sampling technique that uses computed tomography (CT)-based radiomic tumour habitats for ultrasound (US)-guided targeted biopsies that can be integrated in the clinical workflow of patients with high-grade serous ovarian cancer (HGSOC).
METHODS: Six patients with suspected HGSOC scheduled for US-guided biopsy before starting neoadjuvant chemotherapy were included in this prospective study from September 2019 to February 2020. The tumour segmentation was performed manually on the pre-biopsy contrast-enhanced CT scan. Spatial radiomic maps were used to identify tumour areas with similar or distinct radiomic patterns, and tumour habitats were identified using the Gaussian mixture modelling. CT images with superimposed habitat maps were co-registered with US images by means of a landmark-based rigid registration method for US-guided targeted biopsies. The dice similarity coefficient (DSC) was used to assess the tumour-specific CT/US fusion accuracy.
RESULTS: We successfully co-registered CT-based radiomic tumour habitats with US images in all patients. The median time between CT scan and biopsy was 21 days (range 7-30 days). The median DSC for tumour-specific CT/US fusion accuracy was 0.53 (range 0.79 to 0.37). The CT/US fusion accuracy was high for the larger pelvic tumours (DSC: 0.76-0.79) while it was lower for the smaller omental metastases (DSC: 0.37-0.53).
CONCLUSION: We developed a precision tissue sampling technique that uses radiomic habitats to guide in vivo biopsies using CT/US fusion and that can be seamlessly integrated in the clinical routine for patients with HGSOC. KEY POINTS: • We developed a prevision tissue sampling technique that co-registers CT-based radiomics-based tumour habitats with US images. • The CT/US fusion accuracy was high for the larger pelvic tumours (DSC: 0.76-0.79) while it was lower for the smaller omental metastases (DSC: 0.37-0.53).

Entities:  

Keywords:  Computed tomography; Ovarian neoplasms; Radiomics

Mesh:

Year:  2020        PMID: 33315123     DOI: 10.1007/s00330-020-07560-8

Source DB:  PubMed          Journal:  Eur Radiol        ISSN: 0938-7994            Impact factor:   5.315


  9 in total

1.  Decoding incidental ovarian lesions: use of texture analysis and machine learning for characterization and detection of malignancy.

Authors:  Hyesun Park; Lei Qin; Pamela Guerra; Camden P Bay; Atul B Shinagare
Journal:  Abdom Radiol (NY)       Date:  2020-07-29

Review 2.  Radiomic Signatures Associated with CD8+ Tumour-Infiltrating Lymphocytes: A Systematic Review and Quality Assessment Study.

Authors:  Syafiq Ramlee; David Hulse; Kinga Bernatowicz; Raquel Pérez-López; Evis Sala; Luigi Aloj
Journal:  Cancers (Basel)       Date:  2022-07-27       Impact factor: 6.575

Review 3.  Virtual Biopsy in Soft Tissue Sarcoma. How Close Are We?

Authors:  Amani Arthur; Edward W Johnston; Jessica M Winfield; Matthew D Blackledge; Robin L Jones; Paul H Huang; Christina Messiou
Journal:  Front Oncol       Date:  2022-07-01       Impact factor: 5.738

4.  Robust imaging habitat computation using voxel-wise radiomics features.

Authors:  Kinga Bernatowicz; Francesco Grussu; Marta Ligero; Alonso Garcia; Eric Delgado; Raquel Perez-Lopez
Journal:  Sci Rep       Date:  2021-10-11       Impact factor: 4.379

5.  The Potential of Photoacoustic Imaging in Radiation Oncology.

Authors:  Thierry L Lefebvre; Emma Brown; Lina Hacker; Thomas Else; Mariam-Eleni Oraiopoulou; Michal R Tomaszewski; Rajesh Jena; Sarah E Bohndiek
Journal:  Front Oncol       Date:  2022-03-03       Impact factor: 5.738

6.  Habitat radiomics analysis of pet/ct imaging in high-grade serous ovarian cancer: Application to Ki-67 status and progression-free survival.

Authors:  Xinghao Wang; Chen Xu; Marcin Grzegorzek; Hongzan Sun
Journal:  Front Physiol       Date:  2022-08-25       Impact factor: 4.755

7.  Diagnostic Value of Two-Dimensional Transvaginal Ultrasound Combined with Contrast-Enhanced Ultrasound in Ovarian Cancer.

Authors:  Rong Hu; Gulina Shahai; Hui Liu; Yuling Feng; Hong Xiang
Journal:  Front Surg       Date:  2022-05-27

8.  A Machine Learning Model Based on Unsupervised Clustering Multihabitat to Predict the Pathological Grading of Meningiomas.

Authors:  Xinghao Wang; Jia Li; Jing Sun; Wenjuan Liu; Linkun Cai; Pengfei Zhao; Zhenghan Yang; Han Lv; Zhenchang Wang
Journal:  Biomed Res Int       Date:  2022-09-12       Impact factor: 3.246

Review 9.  Radioproteomics in patients with ovarian cancer.

Authors:  Cathal McCague; Lucian Beer
Journal:  Br J Radiol       Date:  2021-06-11       Impact factor: 3.629

  9 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.