| 1. |
- Astaraki, Mehdi, PhD Student, 1984-, et al.
(författare)
-
Early survival prediction in non-small cell lung cancer from PET/CT images using an intra-tumor partitioning method
- 2019
-
Ingår i: Physica medica (Testo stampato). - : Elsevier BV. - 1120-1797 .- 1724-191X. ; 60, s. 58-65
-
Tidskriftsartikel (refereegranskat)abstract
- PurposeTo 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.MethodsLongitudinal 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).ResultsThe 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.ConclusionA 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.
|
|
| 2. |
|
|
| 3. |
- Buizza, Giulia, et al.
(författare)
-
Early tumor response prediction for lung cancer patients using novel longitudinal pattern features from sequential PET/CT image scans
- 2018
-
Ingår i: Physica medica (Testo stampato). - : ELSEVIER SCI LTD. - 1120-1797 .- 1724-191X. ; 54, s. 21-29
-
Tidskriftsartikel (refereegranskat)abstract
- Purpose: A new set of quantitative features that capture intensity changes in PET/CT images over time and space is proposed for assessing the tumor response early during chemoradiotherapy. The hypothesis whether the new features, combined with machine learning, improve outcome prediction is tested. Methods: The proposed method is based on dividing the tumor volume into successive zones depending on the distance to the tumor border. Mean intensity changes are computed within each zone, for CT and PET scans separately, and used as image features for tumor response assessment. Doing so, tumors are described by accounting for temporal and spatial changes at the same time. Using linear support vector machines, the new features were tested on 30 non-small cell lung cancer patients who underwent sequential or concurrent chemoradiotherapy. Prediction of 2-years overall survival was based on two PET-CT scans, acquired before the start and during the first 3 weeks of treatment. The predictive power of the newly proposed longitudinal pattern features was compared to that of previously proposed radiomics features and radiobiological parameters. Results: The highest areas under the receiver operating characteristic curves were 0.98 and 0.93 for patients treated with sequential and concurrent chemoradiotherapy, respectively. Results showed an overall comparable performance with respect to radiomics features and radiobiological parameters. Conclusions: A novel set of quantitative image features, based on underlying tumor physiology, was computed from PET/CT scans and successfully employed to distinguish between early responders and non-responders to chemoradiotherapy.
|
|
| 4. |
- Ureba, Ana, et al.
(författare)
-
Biologically guided automated treatment planning and evaluation : potential for treatment adaptation in head and neck cancer
- 2023
-
Ingår i: Acta Oncologica. - 0284-186X .- 1651-226X. ; 62:11, s. 1389-1393
-
Tidskriftsartikel (refereegranskat)abstract
- Radiation therapy is among the main treatment options offered as a non-surgical solution in the treatment of cancer. This technology-driven treatment modality has evolved significantly over the past few decades, aiming to improve the therapeutic ratio and local tumour control. Modern external beam radiotherapy such as intensity modulated radiotherapy (IMRT), intensity modulated arc therapy (IMAT) or volumetric arc therapy (VMAT), and intensity modulated proton therapy (IMPT) approaches modulate intensity to deliver precisely the radiation doses required to specific areas within the tumour, and to spare nearby organs [Citation1,Citation2]. The use of these techniques in combination with image-guided techniques and multimodality imaging has provided more accurate radiotherapy allowing for a reduction of long-term adverse effects [Citation3]. Nowadays, most treatment planning systems (TPS) integrate imaging information for the diagnosis, planning, and delivery process, offering tools for image registration and fusion, dose calculation, and optimisation, among others.
|
|
| 5. |
- Ureba, Ana, et al.
(författare)
-
Photon and Proton Dose Painting Based on Oxygen Distribution – Feasibility Study and Tumour Control Probability Assessment
- 2022
-
Ingår i: Oxygen Transport to Tissue XLIII. - Cham : Springer. - 9783031141898 - 9783031141904 ; 1395, s. 223-228
-
Konferensbidrag (refereegranskat)abstract
- Solid tumours may present hypoxic sub-regions of increased radioresistance. Hypoxia quantification requires of clinically implementable, non-invasive and reproducible techniques as positron emission tomography (PET). PET-based dose painting strategies aiming at targeting those sub-regions may be limited by the resolution gap between the PET imaging resolution and the smaller scale at which hypoxia occurs. The ultimate benefit of the usage of dose painting may be reached if the planned dose distribution can be performed and delivered consistently. This study aimed at assessing the feasibility of two PET-based dose painting strategies using two beam qualities (photon or proton beams) in terms of tumour control probability (TCP), accounting for underlying oxygen distribution at sub-millimetre scale.A tumour oxygenation model at submillimetre scale was created consisting of three regions with different oxygen partial pressure distributions, being hypoxia decreasing from core to periphery. A published relationship between uptake and oxygen partial pressure was used and a PET image of the tumour was simulated. The fundamental effects that limit the PET camera resolution were considered by processing the uptake distribution with a Gaussian 3D filter and re-binning to a PET image voxel size of 2 mm. Prescription doses to overcome tumour hypoxia were calculated based on the processed images, and planned using robust optimisation.Normal tissue complication probabilities and TCPs after the delivery of the planned doses were calculated for the nominal plan and the lowest bounds of the dose volume histograms resulting from the robust scenarios planned, taking into account the underlying oxygenation at submillimetre scale. Results were presented for the two beam qualities and the two dose painting strategies: by contours (DPBC) and by using a voxel grouping-based approach (DPBOX).In the studied case, DPBOX outperforms DPBC with respect to TCP regardless the beam quality, although both dose painting strategy plans demonstrated robust target coverage.
|
|
