Abstract 1833
Background
Although the personalized medicine has always focused on the genomic or proteomic characterization of tumor, medical imaging is still one of the major factors to guide therapy and to monitor the progression of the tumor. Radiomics is an emerging field that converts the medical image data into the mineable quantitative features via the automatically algorithms, and can server as a bridge between medical image, genomics and clinical-parameters. Serval studies have demonstrated that the radiomic-based model can predict outcome of RCC, but the correlation between radiomic features and histological subtypes of RCC is still unknown. The aim of this letter is to focus on the ability of radiomics to identify the histological subtypes and metastasis of RCC.
Methods
This study included Forty-four patients diagnosed with renal tumor. For each renal lesion, The CT images of volume of interest (VOI) were obtained semi-automatically by two experienced nuclear medicine physician, 85 texture features were extracted from each VOI using the first-order statistics features, Shape Based Features, Gray Level Neighboring Gray Level Dependence Matrix and Neighboring Gray Tone Difference Matrix.
Results
To investigate the value of radiomic features to capture phenotypic differences of RCC, we performed Unsupervised Clustering of patients with similar radiomic expression patterns. We analyzed the two main clusters of patients with clinical parameters, and found that the tumor clusters were statistically and significantly associated with primary tumor stage (P < 0.001), M-stage (P = 0.049) and benign (P = 0.037), wherein high T-stages, M-stage and tumor group showed in cluster II. RCC histology and N-stage (lymph-node) did not reach statistical significance for their association with the radiomic expression patterns (P = 0.165, 0.361, respectively). In addition, we analyzed the overall survival (OS) of the each radiomic features, and showed that P25, IMC1 and IMC2 were associated with OS (P = 0.002, 0.002, 0.016, respectively, log-rank test).
Conclusions
The radiomic features from medical images could be helpful in deciphering T-stages, metastasis and benign of RCC and may have potential as imaging biomarker for prediction of RCC overall survival.
Clinical trial identification
Editorial acknowledgement
Legal entity responsible for the study
Zhejiang Cancer Hospital.
Funding
National Natural Science Foundation of China (No 81402117, 81671775), Natural Science Foundation of Zhejiang Province (No LY17H160043) and Qianjiang talent plan of Zhejiang Province (No QJD1602025).
Disclosure
All authors have declared no conflicts of interest.
Resources from the same session
2927 - Singapore Caregiver Quality Of Life Scale (SCQOLS): Turkish Validity and Reliability Study
Presenter: Nur Basak
Session: Poster Display session 3
Resources:
Abstract
5066 - Screening for Psicosocial Distress in recently diagnosed cancer patients
Presenter: Eva Baillès
Session: Poster Display session 3
Resources:
Abstract
6074 - Socio-demographic characteristics and quality of life analysis of cancer survivors followed at a Primary Care Center.
Presenter: Begona Grana Suarez
Session: Poster Display session 3
Resources:
Abstract
5129 - The adhesion in the screening measures in carrying patients of breast cancer and ovary hereditary and the relationship with the psychological aspects
Presenter: Melinda Concepcion
Session: Poster Display session 3
Resources:
Abstract
5635 - Assessment of emotional discomfort of oncological patients in the first nursing visit at Donostia University Hospital
Presenter: Elena Uranga
Session: Poster Display session 3
Resources:
Abstract
858 - A systematic review and meta-analysis of the distress thermometer for the screening of distress in Chinese patients with cancer
Presenter: Hui Hui Sun
Session: Poster Display session 3
Resources:
Abstract
4475 - Pharmacist and Nurse (PN) Led Melanoma Immunotherapy Clinic: Patient Experience Survey
Presenter: Dharmisha Chauhan
Session: Poster Display session 3
Resources:
Abstract
1871 - Phone Triage & Acute Review Clinics: The emerging role of the Oncology Specialist Nurse
Presenter: Fiona Barrett
Session: Poster Display session 3
Resources:
Abstract
5193 - Patient reported outcomes during immunotherapy: symptom burden in daily clinical practice
Presenter: José Koldenhof
Session: Poster Display session 3
Resources:
Abstract
2453 - Factors related to hospital length of stay, re-admissions and unplanned care for patients with cancer, an on-going study
Presenter: Helena Ullgren
Session: Poster Display session 3
Resources:
Abstract