Texture Analysis in the Diagnosis of Primary Breast Cancer: Comparison of High-Resolution Dedicated Breast Positron Emission Tomography (dbPET) and Whole-Body PET/CT

Satoh, Yoko; Hirata, Kenji; Tamada, Daiki; Funayama, Satoshi; Oda, Hiroshi

doi:10.3389/fmed.2020.603303

Cited by 9 publications

(5 citation statements)

References 33 publications

(25 reference statements)

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“…32,33 parathyroid,34 breast,35,36 kidney,37 and prostate 38. Here, for the first time, we applied this engineering technology to the testis, confirming that US texture features could be useful in clinical practice to monitor testicular function during infertility work-up.…”

supporting

confidence: 61%

“…However, when all semen parameters have been considered together, multivariate and classification analyses confirmed the predictive ability of US texture to classify patients according to semen abnormalities, including sperm morphology. The radiomics ability to describe tissue structure and to predict tissue function has been recently confirmed in different contexts, such as thyroid, 32 , 33 parathyroid, 34 breast, 35 , 36 kidney, 37 and prostate. 38 Here, for the first time, we applied this engineering technology to the testis, confirming that US texture features could be useful in clinical practice to monitor testicular function during infertility work‐up.…”

Section: Discussionmentioning

confidence: 96%

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From subjective to objective: A pilot study on testicular radiomics analysis as a measure of gonadal function

et al. 2021

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Background:The connection between testicular ultrasound (US) parameters and testicular function, including both spermato-and steroidogenesis has been largely suggested, but their predictive properties are not routinely applied. Radiomics, a new engineering approach to radiological imaging, could overcome the visual limit of the sonographer.Objectives: This study is aimed at extracting objective testicular US features, correlating with testicular function, including both spermato-and steroidogenesis, using an engineering approach, in order to overcome the operator-dependent subjectivity. Materials and methods: Prospective observational pilot study from December 2019 to December 2020 on normozoospermic subjects and patients with semen variables alterations, excluding azoospermia. All patients underwent conventional semen analysis, pituitary-gonadal hormones assessment, and testicular US, performed by the same operator. US images were analyzed by Biolab (Turin) throughout image segmentation, image pre-processing, and texture features extraction.Results: One hundred seventy US testicular images were collected from 85 patients (age 38.6 ± 9.1 years). A total of 44 first-order and advanced features were extracted.US inhomogeneity defined by radiomics significantly correlates with the andrologist definition, showing for the first time a mathematical quantification of a subjective US evaluation. Thirteen US texture features correlated with semen parameters, predicting sperm concentration, total sperm number, progressive motility, total motility and morphology, and with gonadotropins serum levels, but not with total testosterone serum levels. Classification analyses confirmed that US textural features predicted patients' classification according to semen parameters alterations. Conclusions:Radiomics texture features qualitatively describe the testicular parenchyma with objective and reliable quantitative parameters, reflecting both the testicular spermatogenic capability and the action of pituitary gonadotropins. This is an innovative model in which US texture features represent a mirror of the pituitary-gonadal homeostasis in terms of reproductive function.

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confidence: 61%

Section: Discussionmentioning

confidence: 96%

From subjective to objective: A pilot study on testicular radiomics analysis as a measure of gonadal function

et al. 2021

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“…This imaging modality provides a very high detection rate, thanks to its 1.5–2.0 mm spatial resolution [ 99 ]. In a few studies, textural features were extracted to make a direct comparison between dbPET and whole-body PET [ 57 , 58 , 59 ]. Satoh et al [ 57 ], in a retrospective study of 44 patients, compared the two tomographs in classifying tumor characteristics of BC, obtaining similar results for both dbPET and whole-body PET/CT.…”

Section: Resultsmentioning

confidence: 99%

“…In a few studies, textural features were extracted to make a direct comparison between dbPET and whole-body PET [ 57 , 58 , 59 ]. Satoh et al [ 57 ], in a retrospective study of 44 patients, compared the two tomographs in classifying tumor characteristics of BC, obtaining similar results for both dbPET and whole-body PET/CT. Conversely, Moscoso et al [ 59 ], demonstrated strong correlations between FDG dbPET-derived radiomic features and both immunohistochemistry and molecular subtypes of BC, stronger than those obtained by whole-body PET.…”

Section: Resultsmentioning

confidence: 99%

PET-Derived Radiomics and Artificial Intelligence in Breast Cancer: A Systematic Review

Urso

Manco

Castello

et al. 2022

IJMS

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Breast cancer (BC) is a heterogeneous malignancy that still represents the second cause of cancer-related death among women worldwide. Due to the heterogeneity of BC, the correct identification of valuable biomarkers able to predict tumor biology and the best treatment approaches are still far from clear. Although molecular imaging with positron emission tomography/computed tomography (PET/CT) has improved the characterization of BC, these methods are not free from drawbacks. In recent years, radiomics and artificial intelligence (AI) have been playing an important role in the detection of several features normally unseen by the human eye in medical images. The present review provides a summary of the current status of radiomics and AI in different clinical settings of BC. A systematic search of PubMed, Web of Science and Scopus was conducted, including all articles published in English that explored radiomics and AI analyses of PET/CT images in BC. Several studies have demonstrated the potential role of such new features for the staging and prognosis as well as the assessment of biological characteristics. Radiomics and AI features appear to be promising in different clinical settings of BC, although larger prospective trials are needed to confirm and to standardize this evidence.

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“…Spiral PC-BCT instead, is a truly three-dimensional breast imaging modality suitable for TA. To the best of our concerns, our study is the first investigating the potential of TA in spiral PC-BCT, though there have been investigations on cone-beam CT or positron emission tomography/CT in BC patients [ 18 , 37 ]. Due to its three-dimensional nature, spiral PC-BCT can provide around 3,500 images (including raw data images and high-resolution images), resulting into a systematically higher evaluation time.…”

Section: Discussionmentioning

confidence: 99%

Radiomics in photon-counting dedicated breast CT: potential of texture analysis for breast density classification

et al. 2022

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Background We investigated whether features derived from texture analysis (TA) can distinguish breast density (BD) in spiral photon-counting breast computed tomography (PC-BCT). Methods In this retrospective single-centre study, we analysed 10,000 images from 400 PC-BCT examinations of 200 patients. Images were categorised into four-level density scale (a–d) using Breast Imaging Reporting and Data System (BI-RADS)-like criteria. After manual definition of representative regions of interest, 19 texture features (TFs) were calculated to analyse the voxel grey-level distribution in the included image area. ANOVA, cluster analysis, and multinomial logistic regression statistics were used. A human readout then was performed on a subset of 60 images to evaluate the reliability of the proposed feature set. Results Of the 19 TFs, 4 first-order features and 7 second-order features showed significant correlation with BD and were selected for further analysis. Multinomial logistic regression revealed an overall accuracy of 80% for BD assessment. The majority of TFs systematically increased or decreased with BD. Skewness (rho -0.81), as a first-order feature, and grey-level nonuniformity (GLN, -0.59), as a second-order feature, showed the strongest correlation with BD, independently of other TFs. Mean skewness and GLN decreased linearly from density a to d. Run-length nonuniformity (RLN), as a second-order feature, showed moderate correlation with BD, but resulted in redundant being correlated with GLN. All other TFs showed only weak correlation with BD (range -0.49 to 0.49, p < 0.001) and were neglected. Conclusion TA of PC-BCT images might be a useful approach to assess BD and may serve as an observer-independent tool.

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Texture Analysis in the Diagnosis of Primary Breast Cancer: Comparison of High-Resolution Dedicated Breast Positron Emission Tomography (dbPET) and Whole-Body PET/CT

Cited by 9 publications

References 33 publications

From subjective to objective: A pilot study on testicular radiomics analysis as a measure of gonadal function

From subjective to objective: A pilot study on testicular radiomics analysis as a measure of gonadal function

PET-Derived Radiomics and Artificial Intelligence in Breast Cancer: A Systematic Review

Radiomics in photon-counting dedicated breast CT: potential of texture analysis for breast density classification

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