Radiomics in Oncology, Part 2: Thoracic, Genito-Urinary, Breast, Neurological, Hematologic and Musculoskeletal Applications

Polici, Michela; Zerunian, Marta; Pucciarelli, Francesco; Guido, Gisella; Polidori, Tiziano; Landolfi, Federica; Nicolai, Matteo; Lucertini, Elena; Tarallo, Mariarita; Bracci, Benedetta; Nacci, Ilaria; Rucci, Carlotta; Eid, Marwen; Iannicelli, Elsa; Laghi, Andréa

doi:10.3390/cancers13112681

Cited by 29 publications

(26 citation statements)

References 86 publications

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“…Radiomics is a developing field aimed at deriving automated quantitative imaging features from medical images that can predict nodule and tumor behavior non-invasively. In CT or FDG-PET/CT, radiomics has been extensively applied to lung cancer and multiple studies evaluated its role in diagnosis, prognosis, and response to treatment [52]. In MRI, there is also the possibility that radiomics is useful for diagnosis, prognosis, and response to treatment of lung cancer.…”

Section: Discussionmentioning

confidence: 99%

Pulmonary Nodule and Mass: Superiority of MRI of Diffusion-Weighted Imaging and T2-Weighted Imaging to FDG-PET/CT

Usuda

Ishikawa

Iwai

et al. 2021

Cancers

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The purpose of this retrospective study was to compare the diagnostic efficacy of FDG-PET/CT and MRI in discriminating malignant from benign pulmonary nodules and masses (PNMs). There were 278 lung cancers and 50 benign PNMs that were examined by FDG-PET/CT and MRI. The T2 contrast ratio (T2 CR) was designated as the ratio of T2 signal intensity of PNM divided by T2 signal intensity of the rhomboid muscle. The optimal cut-off values (OCVs) for differential diagnosis were 3.605 for maximum standardized uptake value (SUVmax), 1.459 × 10−3 mm2/s for apparent diffusion coefficient (ADC), and 2.46 for T2 CR. Areas under the receiver operating characteristics curves were 67.5% for SUVmax, 74.3% for ADC, and 72.4% for T2 CR, respectively. The sensitivity (0.658) of SUVmax was significantly lower than that (0.838) of ADC (p < 0.001) and that (0.871) of T2 CR (p < 0.001). The specificity (0.620) of SUVmax was that the same as (0.640) ADC and (0.640) of T2 CR. The accuracy (0.652) of SUVmax was significantly lower than that (0.808) of ADC (p < 0.001) and that (0.835) of T2 CR (p < 0.001). The sensitivity and accuracy of DWI and T2WI in MRI were significantly higher than those of FDG-PET/CT. Ultimately, MRI can replace FDG PET/CT for differential diagnosis of PNMs saving healthcare systems money while not sacrificing the quality of care.

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Section: Discussionmentioning

confidence: 99%

Pulmonary Nodule and Mass: Superiority of MRI of Diffusion-Weighted Imaging and T2-Weighted Imaging to FDG-PET/CT

Usuda

Ishikawa

Iwai

et al. 2021

Cancers

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“…It has found wide use in oncology, since features such as heterogeneity and shape have been correlated to the lesion’s biological behavior, so that, in combination with demographic, histologic, genomic or proteomic data, they can provide additional information about the tumor, potentially predicting clinical endpoints, such as survival and treatment response [ 71 , 72 ]. Consistent with this, radiomics can be seen as a useful tool for physicians in planning clinical trial settings specifically tailored to each patient in accordance with quantitative parameters and outcome data [ 73 ].…”

Section: Imaging Contribution In Gctb Managementmentioning

confidence: 99%

State of the Art and New Concepts in Giant Cell Tumor of Bone: Imaging Features and Tumor Characteristics

Parmeggiani

Miceli

Errani

et al. 2021

Cancers

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Giant cell tumor of bone (GCTB) is classified as an intermediate malignant tumor due to its locally aggressive behavior, burdened by high local recurrence rate. GCTB accounts for about 4–5% of all primary bone tumors and typically arises in the metaphysis and epiphyses of the long tubular bones. Mutation of gene H3F3A is at the basis of GCTB etiopathogenesis, and its immunohistochemical expression is a valuable method for practical diagnosis, even if new biomarkers have been identified for early diagnosis and for potential tumor recurrence prediction. In the era of computer-aided diagnosis, imaging plays a key role in the assessment of GCTB for surgical planning, patients’ prognosis prediction and post treatment evaluation. Cystic changes, penetrating irregular margins and adjacent soft tissue invasion on preoperative Magnetic Resonance Imaging (MRI) have been associated with a higher rate of local recurrence. Distance from the tumor edge to the articular surface and thickness of unaffected cortical bone around the tumor should be evaluated on Computed Tomography (CT) as related to local recurrence. Main features associated with local recurrence after curettage are bone resorption around the graft or cement, soft tissue mass formation and expansile destruction of bone. A denosumab positive response is represented by a peripherical well-defined osteosclerosis around the lesion and intralesional ossification. Radiomics has proved to offer a valuable contribution in aiding GCTB pre-operative diagnosis through clinical-radiomics models based on CT scans and multiparametric MR imaging, possibly guiding the choice of a patient-tailored treatment. Moreover, radiomics models based on texture analysis demonstrated to be a promising alternative solution for the assessment of GCTB response to denosumab both on conventional radiography and CT since the quantitative variation of some radiomics features after therapy has been correlated with tumor response, suggesting they might facilitate disease monitoring during post-denosumab surveillance.

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“…The major results were reached in tumor degree differentiation with the goal to overcome and support conventional tumor biopsy, often altered by substantial intrinsic bias (e.g., lesion sampling, operator experience, and bleeding) [ 17 ]. In the future landscape of personalized medicine, radiomics might enter in the structured workflow of NENs by providing several objective parameters useful for stratifying patients according to tumor aggressiveness, risk of recurrence, and mortality [ 6 , 7 ]. In this study we proposed both a comparison and survival analysis between responders and non-responder NETs to Everolimus, then we built a radiomic model having as clinical endpoint the death.…”

Section: Introductionmentioning

confidence: 99%

CT-based radiomics for prediction of therapeutic response to Everolimus in metastatic neuroendocrine tumors

et al. 2022

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Aim To test radiomic approach in patients with metastatic neuroendocrine tumors (NETs) treated with Everolimus, with the aim to predict progression-free survival (PFS) and death. Materials and methods Twenty-five patients with metastatic neuroendocrine tumors, 15/25 pancreatic (60%), 9/25 ileal (36%), 1/25 lung (4%), were retrospectively enrolled between August 2013 and December 2020. All patients underwent contrast-enhanced CT before starting Everolimus, histological diagnosis, tumor grading, PFS, overall survival (OS), death, and clinical data collected. Population was divided into two groups: responders (PFS ≤ 11 months) and non-responders (PFS > 11 months). 3D segmentation was performed on whole liver of naïve CT scans in arterial and venous phases, using a dedicated software (3DSlicer v4.10.2). A total of 107 radiomic features were extracted and compared between two groups (T test or Mann–Whitney), radiomics performance assessed with receiver operating characteristic curve, Kaplan–Meyer curves used for survival analysis, univariate and multivariate logistic regression performed to predict death, and interobserver variability assessed. All significant radiomic comparisons were validated by using a synthetic external cohort. P < 0.05 is considered significant. Results 15/25 patients were classified as responders (median PFS 25 months and OS 29 months) and 10/25 as non-responders (median PFS 4.5 months and OS 23 months). Among radiomic parameters, Correlation and Imc1 showed significant differences between two groups (P < 0.05) with the best performance (internal cohort AUC 0.86–0.84, P < 0.0001; external cohort AUC 0.84–0.90; P < 0.0001). Correlation < 0.21 resulted correlated with death at Kaplan–Meyer analysis (P = 0.02). Univariate analysis showed three radiomic features independently correlated with death, and in multivariate analysis radiomic model showed good performance with AUC 0.87, sensitivity 100%, and specificity 66.7%. Three features achieved 0.77 ≤ ICC < 0.83 and one ICC = 0.92. Conclusions In patients affected by metastatic NETs eligible for Everolimus treatment, radiomics could be used as imaging biomarker able to predict PFS and death.

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Radiomics in Oncology, Part 2: Thoracic, Genito-Urinary, Breast, Neurological, Hematologic and Musculoskeletal Applications

Cited by 29 publications

References 86 publications

Pulmonary Nodule and Mass: Superiority of MRI of Diffusion-Weighted Imaging and T2-Weighted Imaging to FDG-PET/CT

Pulmonary Nodule and Mass: Superiority of MRI of Diffusion-Weighted Imaging and T2-Weighted Imaging to FDG-PET/CT

State of the Art and New Concepts in Giant Cell Tumor of Bone: Imaging Features and Tumor Characteristics

CT-based radiomics for prediction of therapeutic response to Everolimus in metastatic neuroendocrine tumors

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