Fully Automated Support System for Diagnosis of Breast Cancer in Contrast-Enhanced Spectral Mammography Images

Fanizzi, Annarita; Losurdo, L.; Basile, Teresa Maria Altomare; Bellotti, Roberto; Bottigli, U.; Delogu, Pasquale; Diacono, Domenico; Didonna, Vittorio; Fausto, Alfonso; Lombardi, Angela; Lorusso, Vito; Massafra, Raffaella; Tangaro, Sabina; Forgia, Daniele La

doi:10.3390/jcm8060891

Cited by 45 publications

(39 citation statements)

References 52 publications

(64 reference statements)

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“…Moreover, three MRI sequences per lesion were analyzed increasing data robustness. Finally, we also acknowledge that future software developments will reduce the necessity of freehand ROIs positioning 19. We also believe that further research will be critical to fully unveil the potential or radiomics in STT evaluation, indeed, it has been studied that radiomics features extracted from MR images are independently associated with survival when accounting for age and tumor grade and helps in differential diagnosis 20,21…”

Section: Discussionmentioning

confidence: 99%

Local recurrence of soft tissue sarcoma: a radiomic analysis

Tagliafico

Bignotti

Rossi

et al. 2019

Radiology and Oncology

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Background To perform a radiomics analysis in local recurrence (LR) surveillance of limb soft tissue sarcoma (STS) Patients and methods This is a sub-study of a prospective multicenter study with Institutional Review Board approval supported by ESSR (European Society of Musculoskeletal Radiology). radiomics analysis was done on fast spin echo axial T1w, T2w fat saturated and post-contrast T1w (T1wGd) 1.5T MRI images of consecutively recruited patients between March 2016 and September 2018. Results N = 11 adult patients (6 men and 5 women; mean age 57.8 ± 17.8) underwent MRI to exclude STS LR: a total of 33 follow-up events were evaluated. A total of 198 data-sets per patients of both pathological and normal tissue were analyzed. Four radiomics features were significantly correlated to tumor size (p < 0.02) and four radiomics features were correlated with grading (p < 0.05). ROC analysis showed an AUC between 0.71 (95%CI: 0.55–0.87) for T1w and 0.96 (95%CI: 0.87–1.00) for post-contrast T1w. Conclusions radiomics features allow to differentiate normal tissue from pathological tissue in MRI surveillance of local recurrence of STS. radiomics in STS evaluation is useful not only for detection purposes but also for lesion characterization.

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Section: Discussionmentioning

confidence: 99%

Local recurrence of soft tissue sarcoma: a radiomic analysis

Tagliafico

Bignotti

Rossi

et al. 2019

Radiology and Oncology

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“…Studies of CESM have essentially focused on imaging analysis with an auxiliary system for reporting, and on radiomic features for benignant–malignant differentiation [ 12 , 13 ].…”

Section: Discussionmentioning

confidence: 99%

“…These descriptors are common statistical features that were highly informative in the characterization of tumor tissues in CESM images [ 12 , 13 ]. Mean and variation coefficients and difference of maximum and minimum describe the average intensity and the variability of the gray-level distribution.…”

Section: Methodsmentioning

confidence: 99%

“…Pattern recognition performed by a radiologist is a subjective evaluation, but several studies have shown that the accuracy of radiomic features in the discrimination of benign and malignant breast lesions and evaluation prognosis, or response to therapies, can represent an added value to the diagnostic task [ 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 ]. Automatic recognition tools can provide objective information to support clinical decisions by acting as a second reader in screening while improving the radiologist’s confidence in the challenging diagnostic task [ 20 , 21 ].…”

Section: Introductionmentioning

confidence: 99%

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Radiomic Analysis in Contrast-Enhanced Spectral Mammography for Predicting Breast Cancer Histological Outcome

et al. 2020

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Contrast-Enhanced Spectral Mammography (CESM) is a recently introduced mammographic method with characteristics particularly suitable for breast cancer radiomic analysis. This work aims to evaluate radiomic features for predicting histological outcome and two cancer molecular subtypes, namely Human Epidermal growth factor Receptor 2 (HER2)-positive and triple-negative. From 52 patients, 68 lesions were identified and confirmed on histological examination. Radiomic analysis was performed on regions of interest (ROIs) selected from both low-energy (LE) and ReCombined (RC) CESM images. Fourteen statistical features were extracted from each ROI. Expression of estrogen receptor (ER) was significantly correlated with variation coefficient and variation range calculated on both LE and RC images; progesterone receptor (PR) with skewness index calculated on LE images; and Ki67 with variation coefficient, variation range, entropy and relative smoothness indices calculated on RC images. HER2 was significantly associated with relative smoothness calculated on LE images, and grading tumor with variation coefficient, entropy and relative smoothness calculated on RC images. Encouraging results for differentiation between ER+/ER−, PR+/PR−, HER2+/HER2−, Ki67+/Ki67−, High-Grade/Low-Grade and TN/NTN were obtained. Specifically, the highest performances were obtained for discriminating HER2+/HER2− (90.87%), ER+/ER− (83.79%) and Ki67+/Ki67− (84.80%). Our results suggest an interesting role for radiomics in CESM to predict histological outcomes and particular tumors’ molecular subtype.

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“…Second, as we focus on the disease natural history of small breast cancers rather than all breast cancers, large samples are, therefore, required if these new phenotypes are further considered. In a similar manner, the current study is also limited to allow for the incorporation of new mammographic techniques and alternative imaging techniques, which have also been tested as screening tools, such as tomosynthesis [ 29 , 30 , 31 , 32 , 33 ], contrast enhanced spectral mammography (CESM) [ 34 , 35 ], magnetic resonance imaging (MRI) [ 36 , 37 ], ultrasound [ 37 , 38 ] and also fully automated support systems and AI systems [ 39 , 40 ]. In spite of the lack of such information, we believe the expedient use of fully automated systems such as computer-aided systems may yield sensitivity as favorable as that operated by the top radiologist (Tabar Laszlo), one of the co-authors, in the second period when such a fully automated system is still immature.…”

Section: Discussionmentioning

confidence: 99%

Precision Science on Incidence and Progression of Early-Detected Small Breast Invasive Cancers by Mammographic Features

Chang

Chuang

Hsu

et al. 2020

Cancers

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The aim was to evaluate how the inter-screening interval affected the performance of screening by mammographic appearances. This was a Swedish retrospective screening cohort study with information on screening history and mammography features in two periods (1977–1985 and 1996–2010). The pre-clinical incidence and the mean sojourn time (MST) for small breast cancer allowing for sensitivity by mammographic appearances were estimated. The percentage of interval cancer against background incidence (I/E ratio) was used to assess the performance of mammography screening by different inter-screening intervals. The sensitivity-adjusted MSTs (in years) were heterogeneous with mammographic features, being longer for powdery and crushed stone-like calcifications (4.26, (95% CI, 3.50–5.26)) and stellate masses (3.76, (95% CI, 3.15–4.53)) but shorter for circular masses (2.65, (95% CI, 2.06–3.55)) in 1996–2010. The similar trends, albeit longer MSTs, were also noted in 1977–1985. The I/E ratios for the stellate type were 23% and 32% for biennial and triennial screening, respectively. The corresponding figures were 32% and 43% for the circular type and 21% and 29% for powdery and crushed stone-like calcifications, respectively. Mammography-featured progressions of small invasive breast cancer provides a new insight into personalized quality assurance, surveillance, treatment and therapy of early-detected breast cancer.

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Fully Automated Support System for Diagnosis of Breast Cancer in Contrast-Enhanced Spectral Mammography Images

Cited by 45 publications

References 52 publications

Local recurrence of soft tissue sarcoma: a radiomic analysis

Local recurrence of soft tissue sarcoma: a radiomic analysis

Radiomic Analysis in Contrast-Enhanced Spectral Mammography for Predicting Breast Cancer Histological Outcome

Precision Science on Incidence and Progression of Early-Detected Small Breast Invasive Cancers by Mammographic Features

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