The Role of 3D Ultrasound and 3D Power Doppler Imaging in the Diagnosis and Evaluation of Ovarian Cancer: New Perspectives

Redondo, MT; Orensanz, I.; Fj, Salazar; Iniesta, S; Bueno, B; Pérez-Medina, Tirso; Bajo, J.M.; Cristina, HU Santa; Vives, Maestro

doi:10.5005/jp-journals-10009-1097

Cited by 2 publications

(2 citation statements)

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“…It was extensively reported that the oxygen distribution in a tumor has a significant effect on radioresponsivity and prognosis. 31,33,34 Some works addressed the problem by modeling spheroids featuring multiple necrotic cores. 35 Another possible approach is to include further information on the well-known hypoxic fraction (HF) which could be estimated noninvasively by means of magnetic resonance imaging (MRI) acquisitions 36 and to relate it to the oxygen enhancement ratio (OER) accounting for the increased effectiveness of the radiation in presence of higher oxygen concentration.…”

Section: Discussionmentioning

confidence: 99%

“…Perspective studies could be considered, for example, exploiting positron emission tomography functional images capturing tumor activity or, in the particular case of cervix cancer, assessing the tumor perfusion by means of Doppler sonography. 30,31 The volume normalization allowed us to apply the same model to all the patients (cMg/cMl) computing a unique sensitivity parameter a independently on the initial tumor size. However, a (as well as r, k) is likely to vary significantly on a patient-specific basis since the active layer is composed by several cell lines featuring different radiosensitivity.…”

Section: Discussionmentioning

confidence: 99%

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Kinetic Models for Predicting Cervical Cancer Response to Radiation Therapy on Individual Basis Using Tumor Regression Measured In Vivo With Volumetric Imaging

Belfatto

Riboldi

Ciardo

et al. 2015

Technol Cancer Res Treat

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This article describes a macroscopic mathematical modeling approach to capture the interplay between solid tumor evolution and cell damage during radiotherapy. Volume regression profiles of 15 patients with uterine cervical cancer were reconstructed from serial cone-beam computed tomography data sets, acquired for image-guided radiotherapy, and used for model parameter learning by means of a genetic-based optimization. Patients, diagnosed with either squamous cell carcinoma or adenocarcinoma, underwent different treatment modalities (image-guided radiotherapy and image-guided chemo-radiotherapy). The mean volume at the beginning of radiotherapy and the end of radiotherapy was on average 23.7 cm 3 (range: 12.7-44.4 cm 3 ) and 8.6 cm 3 (range: 3.6-17.1 cm 3 ), respectively. Two different tumor dynamics were taken into account in the model: the viable (active) and the necrotic cancer cells. However, according to the results of a preliminary volume regression analysis, we assumed a short dead cell resolving time and the model was simplified to the active tumor volume. Model learning was performed both on the complete patient cohort (cohort-based model learning) and on each single patient (patient-specific model learning). The fitting results (mean error: *16% and *6% for the cohort-based model and patient-specific model, respectively) highlighted the model ability to quantitatively reproduce tumor regression. Volume prediction errors of about 18% on average were obtained using cohort-based model computed on all but 1 patient at a time (leave-one-out technique). Finally, a sensitivity analysis was performed and the data uncertainty effects evaluated by simulating an average volume perturbation of about 1.5 cm 3 obtaining an error increase within 0.2%. In conclusion, we showed that simple time-continuous models can represent tumor regression curves both on a patient cohort and patient-specific basis; this discloses the opportunity in the future to exploit such models to predict how changes in the treatment schedule (number of fractions, doses, intervals among fractions) might affect the tumor regression on an individual basis.Keywords tumor mathematical modeling, uterine cervical cancer, radiosensitivity, linear-quadratic model, image-guided radiotherapy Abbreviations ADC, adenocarcinoma; CBCT, cone-beam computed tomography; ChT, chemotherapy; cM, cohort-based model; cMg, cohortbased model learning using Gompertzian growth function; cMl, cohort-based model learning using logistic growth function; CT, computed tomography; GTV, gross tumor volume; PTV, planning target volume; IGRT, image-guided radiotherapy; K, tumor cell carrying capacity of the tissue; LOO, leave-one-out; LQ, linear-quadratic; MRI, magnetic resonance imaging; ODE, ordinary differential equation; PET, positron emission tomography; pM, patient-specific model; pMg, patient-specific model learning using Gompertzian growth function; pMg5, pMg performed on the reduced data set of patient 5; pMg5*, pMg performed on the complete data set of patient 5;...

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Kinetic Models for Predicting Cervical Cancer Response to Radiation Therapy on Individual Basis Using Tumor Regression Measured In Vivo With Volumetric Imaging

Belfatto

Riboldi

Ciardo

et al. 2015

Technol Cancer Res Treat

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Three-Dimensional Angioultrasonography for the Prediction of Malignancy in Ovarian Masses

Pérez-Medina

Orensanz²,

Pereira³

et al. 2013

Gynecol Obstet Invest

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Objective: To determine the efficacy of the angiographic indexes of 3D power Doppler angiography (3D-PDA) for the diagnosis of malignancy in complex ovarian masses. Methods: A prospective, observational study of 72 patients with complex adnexal mass. Results: In the morphological study, 3D ultrasound showed sensitivity, specificity, PPV, NPV, PLHR and NLHR of 84.6%, 81.9%, 85.1%, 81.8%, 4.65 and 0.19. No differences in vascular indexes (VI, FI, VFI) between malignant and benign masses were found: VI 5.38 (CI 95% 3.06-7.7) vs. 6.29 (CI 95% 4.41-8.17) (p = 0.53); FI 29.6 (CI 95% 25.17-34.08) vs. 33.8 (CI 95% 30.03-37.3) (p = 0.15); VFI 1.68 (CI 95% 0.94-2.42) vs. 2.37 (CI 95% 1.49-3.25) (p = 0.24). When analysed according to different stages, VI was higher in patients with more advanced stages of disease; 4.34 (95% CI 2.21-6.47) vs. 7.38 (95% CI 4.7-10.06) (p = 0.11). FI was significantly lower in patients with early stages of disease; FI 29.07 (95% CI 21.49-36.68) vs. 36.46 (95% CI 32.31-40.62) (p = 0.04). For VFI, differences were not significant, although there was a strong trend; VFI 1.47 (95% CI 0.67-2.28) vs. 2.86 (95% CI 1.57-4.16) (p = 0.11). 3D-PDA indexes were significantly higher in patients with positive adenopathies. Conclusion: 3D-PDA values increase progressively, but not significantly, with the stage of the disease.

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The Role of 3D Ultrasound and 3D Power Doppler Imaging in the Diagnosis and Evaluation of Ovarian Cancer: New Perspectives

Cited by 2 publications

References 30 publications

Kinetic Models for Predicting Cervical Cancer Response to Radiation Therapy on Individual Basis Using Tumor Regression Measured In Vivo With Volumetric Imaging

Kinetic Models for Predicting Cervical Cancer Response to Radiation Therapy on Individual Basis Using Tumor Regression Measured In Vivo With Volumetric Imaging

Three-Dimensional Angioultrasonography for the Prediction of Malignancy in Ovarian Masses

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