Biomarkers from in vivo molecular imaging of breast cancer: pretreatment 18F-FDG PET predicts patient prognosis, and pretreatment DWI-MR predicts response to neoadjuvant chemotherapy

Gallivanone, Francesca; Panzeri, Marta; Canevari, Carla; Losio, Claudio; Cobelli, Francesco De; Castiglioni, Isabella

doi:10.1007/s10334-017-0610-7

Cited by 18 publications

(17 citation statements)

References 48 publications

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“…Radiomic imaging features were extracted from each segmented MTV as morphological and statistical imaging features. Morphological imaging features (IF M ) were obtained starting from the shape and size characteristics of the segmented MTV [ 5 , 8 ].…”

Section: Methodsmentioning

confidence: 99%

“…These approaches exploit the fact that digital medical images are inherently quantitative and that their quantitative values express several tissue functional characteristics, such as metabolism or proliferation [ 3 ], with a role for the onset and progression of cancer. Recent studies have been devoted to the development of automatic or semiautomatic methods for the extraction of quantitative indexes from images to be used as imaging biomarkers of cancer disease [ 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

“…The rationale behind such advanced image features is the hypothesis that imaging in vivo heterogeneities of a cancer lesion is able to reflect the tumor phenotype with the advantage of a noninvasive technique. The recent literature shows the high potential of such quantitative heterogeneity features, defined “radiomics” [ 5 ], thanks to their proven abilities to be correlated with “omics” data. Radiomics refer to a large number of mathematical image descriptors extracted from the volume of an entire cancer lesion by the use of different image analytics methods, including morphological and statistical analyses [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

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Parameters Influencing PET Imaging Features: A Phantom Study with Irregular and Heterogeneous Synthetic Lesions

Gallivanone

Interlenghi

D’Ambrosio

et al. 2018

Contrast Media & Molecular Imaging

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Aim To evaluate reproducibility and stability of radiomic features as effects of the use of different volume segmentation methods and reconstruction settings. The potential of radiomics in really capturing the presence of heterogeneous tumor uptake and irregular shape was also investigated. Materials and Methods An anthropomorphic phantom miming real clinical situations including synthetic lesions with irregular shape and nonuniform radiotracer uptake was used. 18F-FDG PET/CT measurements of the phantom were performed including 38 lesions of different shape, size, lesion-to-background ratio, and radiotracer uptake distribution. Different reconstruction parameters and segmentation methods were considered. COVs were calculated to quantify feature variations over the different reconstruction settings. Friedman test was applied to the values of the radiomic features obtained for the considered segmentation approaches. Two sets of test-retest measurement were acquired and the pairwise intraclass correlation coefficient was calculated. Fifty-eight morphological and statistical features were extracted from the segmented lesion volumes. A Mann–Whitney test was used to evaluate significant differences among each feature when calculated from heterogeneous versus homogeneous uptake. The significance of each radiomic feature in terms of capturing heterogeneity was evaluated also by testing correlation with gold standard indexes of heterogeneity and sphericity. Results The choice of the segmentation method has a strong impact on the stability of radiomic features (less than 20% can be considered stable features). Reconstruction affects the estimate of radiomic features (only 26% are stable). Thirty-one radiomic features (53%) resulted to be reproducible, 11 of them are able to discriminate heterogeneity. Among these, we found a subset of 3 radiomic features strongly correlated with GS heterogeneity index that can be suggested as good features for retrospective evaluations.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Parameters Influencing PET Imaging Features: A Phantom Study with Irregular and Heterogeneous Synthetic Lesions

Gallivanone

Interlenghi

D’Ambrosio

et al. 2018

Contrast Media & Molecular Imaging

Self Cite

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“…In particular, in breast cancer research, several radiomic studies have been performed and are mainly based on dynamic contrast-enhanced- (DCE-) MRI or combine MRI with other imaging modalities, such as PET [ 43 ]. MRI, in fact, is the most sensitive imaging modality for soft tissue tumor detection, characterization, and accurate extent definition [ 14 , 44 ]; moreover, DCE-MRI is of great value in the characterization of anatomic and functional properties of breast cancer [ 45 ].…”

Section: Introductionmentioning

confidence: 99%

DCE-MRI Pharmacokinetic-Based Phenotyping of Invasive Ductal Carcinoma: A Radiomic Study for Prediction of Histological Outcomes

Monti

Aiello

Incoronato

et al. 2018

Contrast Media & Molecular Imaging

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Breast cancer is a disease affecting an increasing number of women worldwide. Several efforts have been made in the last years to identify imaging biomarker and to develop noninvasive diagnostic tools for breast tumor characterization and monitoring, which could help in patients' stratification, outcome prediction, and treatment personalization. In particular, radiomic approaches have paved the way to the study of the cancer imaging phenotypes. In this work, a group of 49 patients with diagnosis of invasive ductal carcinoma was studied. The purpose of this study was to select radiomic features extracted from a DCE-MRI pharmacokinetic protocol, including quantitative maps of ktrans, kep, ve, iAUC, and R1 and to construct predictive models for the discrimination of molecular receptor status (ER+/ER−, PR+/PR−, and HER2+/HER2−), triple negative (TN)/non-triple negative (NTN), ki67 levels, and tumor grade. A total of 163 features were obtained and, after feature set reduction step, followed by feature selection and prediction performance estimations, the predictive model coefficients were computed for each classification task. The AUC values obtained were 0.826 ± 0.006 for ER+/ER−, 0.875 ± 0.009 for PR+/PR−, 0.838 ± 0.006 for HER2+/HER2−, 0.876 ± 0.007 for TN/NTN, 0.811 ± 0.005 for ki67+/ki67−, and 0.895 ± 0.006 for lowGrade/highGrade. In conclusion, DCE-MRI pharmacokinetic-based phenotyping shows promising for discrimination of the histological outcomes.

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“…Biological heterogeneity may be a reflection of cell populations with differing growth rates, vascularity, necrosis and cavitation which, when seen histologically, are features that may imply aggressive behavior. No single mechanism has been identified as a direct link between biologic heterogeneity and radiotracer heterogeneity (Gallivanone et al, 2017;Van Gomez Lopez et al, 2014) and it is likely a multifactorial association. The morphological appearance of lung tumors on CT can often be heterogenous, for example showing regions of contrast enhancement, necrosis, cavitation or ground-glass opacification.…”

Section: Discussionmentioning

confidence: 99%

Tumor heterogeneity measurement using [18F] FDG PET/CT shows prognostic value in patients with non-small cell lung cancer

Hughes

Mou

O’Regan

et al. 2018

European J Hybrid Imaging

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Background: The aim of this study was to evaluate primary tumor heterogeneity in patients with FDG-avid non-small cell lung cancer on PET/CT, with a view to optimising prognostic information from the metabolic signature of the primary tumor. Methods: A retrospective analysis of 94 [ 18 F] FDG PET/CTs (56 M:38F) in patients with a diagnosis of primary lung malignancy was performed. Data collected included patient demographics, tumor size, maximum standardized uptake value (SUVmax), clinical stage and tumor histology. Clinical follow up and survival data were obtained from the available medical records. Tumor FDG spatial uptake heterogeneity was evaluated by the lack of conformity of the FDG pattern within the tumor region of interest to a simple 3-dimensional ellipsoidal form. A multivariate Cox regression analysis was used to assess the added prognostic benefit of heterogeneity information beyond radiological staging and other factors. Results: Ninety four patients (mean age 67 years, range 36-85; 59.6% male) were available for analysis. The clinical staging distribution had 25 Stage I, 14 Stage II, 38 Stage III and 17 Stage IV. Mean tumor FDG spatial uptake heterogeneity was 25.87% with a range 2.78%-83.52%. Multivariate analysis found that heterogeneity, clinical stage, SUVmax and gender were associated with survival. Greater FDG spatial uptake heterogeneity is associated with significantly shorter survival (p = 0.0152). An increase of 19.5% (1 standard deviation) in FDG spatial uptake heterogeneity, is associated with a 43% increase in the risk of death. Conclusion: Quantification of the FDG spatial uptake heterogeneity of lung tumors has potential to add prognostic information to lung cancer staging beyond SUVmax and clinical stage information.

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Biomarkers from in vivo molecular imaging of breast cancer: pretreatment 18F-FDG PET predicts patient prognosis, and pretreatment DWI-MR predicts response to neoadjuvant chemotherapy

Cited by 18 publications

References 48 publications

Parameters Influencing PET Imaging Features: A Phantom Study with Irregular and Heterogeneous Synthetic Lesions

Parameters Influencing PET Imaging Features: A Phantom Study with Irregular and Heterogeneous Synthetic Lesions

DCE-MRI Pharmacokinetic-Based Phenotyping of Invasive Ductal Carcinoma: A Radiomic Study for Prediction of Histological Outcomes

Tumor heterogeneity measurement using [18F] FDG PET/CT shows prognostic value in patients with non-small cell lung cancer

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