Mehdi Astaraki1, Chunliang Wang2, Giulia Buizza3, Iuliana Toma-Dasu4, Marta Lazzeroni4, Örjan Smedby5. 1. KTH Royal Institute of Technology, Department of Biomedical Engineering and Health Systems, Hälsovägen 11C, SE-14157 Huddinge, Sweden; Karolinska Institutet, Department of Oncology-Pathology, Karolinska Universitetssjukhuset, Solna, SE-17176 Stockholm, Sweden. 2. KTH Royal Institute of Technology, Department of Biomedical Engineering and Health Systems, Hälsovägen 11C, SE-14157 Huddinge, Sweden. Electronic address: chunwan@kth.se. 3. Politecnico di Milano, Department of Electronics, Information and Bioengineering, piazza Leonardo da Vinci 42, Milan 20133, Italy. 4. Karolinska Institutet, Department of Oncology-Pathology, Karolinska Universitetssjukhuset, Solna, SE-17176 Stockholm, Sweden; Stockholm University, Department of Physics, SE-10691 Stockholm, Sweden. 5. KTH Royal Institute of Technology, Department of Biomedical Engineering and Health Systems, Hälsovägen 11C, SE-14157 Huddinge, Sweden.
Abstract
PURPOSE: To explore prognostic and predictive values of a novel quantitative feature set describing intra-tumor heterogeneity in patients with lung cancer treated with concurrent and sequential chemoradiotherapy. METHODS: Longitudinal PET-CT images of 30 patients with non-small cell lung cancer were analysed. To describe tumor cell heterogeneity, the tumors were partitioned into one to ten concentric regions depending on their sizes, and, for each region, the change in average intensity between the two scans was calculated for PET and CT images separately to form the proposed feature set. To validate the prognostic value of the proposed method, radiomics analysis was performed and a combination of the proposed novel feature set and the classic radiomic features was evaluated. A feature selection algorithm was utilized to identify the optimal features, and a linear support vector machine was trained for the task of overall survival prediction in terms of area under the receiver operating characteristic curve (AUROC). RESULTS: The proposed novel feature set was found to be prognostic and even outperformed the radiomics approach with a significant difference (AUROCSALoP = 0.90 vs. AUROCradiomic = 0.71) when feature selection was not employed, whereas with feature selection, a combination of the novel feature set and radiomics led to the highest prognostic values. CONCLUSION: A novel feature set designed for capturing intra-tumor heterogeneity was introduced. Judging by their prognostic power, the proposed features have a promising potential for early survival prediction.
PURPOSE: To explore prognostic and predictive values of a novel quantitative feature set describing intra-tumor heterogeneity in patients with lung cancer treated with concurrent and sequential chemoradiotherapy. METHODS: Longitudinal PET-CT images of 30 patients with non-small cell lung cancer were analysed. To describe tumor cell heterogeneity, the tumors were partitioned into one to ten concentric regions depending on their sizes, and, for each region, the change in average intensity between the two scans was calculated for PET and CT images separately to form the proposed feature set. To validate the prognostic value of the proposed method, radiomics analysis was performed and a combination of the proposed novel feature set and the classic radiomic features was evaluated. A feature selection algorithm was utilized to identify the optimal features, and a linear support vector machine was trained for the task of overall survival prediction in terms of area under the receiver operating characteristic curve (AUROC). RESULTS: The proposed novel feature set was found to be prognostic and even outperformed the radiomics approach with a significant difference (AUROCSALoP = 0.90 vs. AUROCradiomic = 0.71) when feature selection was not employed, whereas with feature selection, a combination of the novel feature set and radiomics led to the highest prognostic values. CONCLUSION: A novel feature set designed for capturing intra-tumor heterogeneity was introduced. Judging by their prognostic power, the proposed features have a promising potential for early survival prediction.
Authors: Mingquan Lin; Jacob F Wynne; Boran Zhou; Tonghe Wang; Yang Lei; Walter J Curran; Tian Liu; Xiaofeng Yang Journal: J Appl Clin Med Phys Date: 2021-06-24 Impact factor: 2.102
Authors: Martina Sollini; Francesco Bartoli; Andrea Marciano; Roberta Zanca; Riemer H J A Slart; Paola A Erba Journal: Eur J Hybrid Imaging Date: 2020-12-09