Radiomic features for prostate cancer grade detection through formal verification

Santone, Antonella; Brunese, Maria Chiara; Donnarumma, Federico; Guerriero, Pasquale; Mercaldo, Francesco; Reginelli, Alfonso; Miele, Vittorio; Giovagnoni, Andrea; Brunese, Luca

doi:10.1007/s11547-020-01314-8

Cited by 55 publications

(43 citation statements)

References 31 publications

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“…These models were already studied in a variety of cancers [ 35 , 36 , 37 , 38 , 39 ]. The recent rise in artificial intelligence (AI) and machine learning (ML) algorithms has introduced new classifications for PCa, regarding the differentiation of favorable from unfavorable disease [ 40 , 41 ]; the quantitative assessment of information predicting the tumor Gleason score [ 31 , 32 , 42 , 43 , 44 ] and biochemical recurrence (BCR)-free survival [ 45 ]; the identification of tumors through mpMRI [ 43 , 46 ]; the development of new detection features, such as advanced zoomed diffusion-weighted imaging (DWI) and conventional full-field-of-view DWI [ 47 ]; texture analysis of prostate MRI in the prostate imaging reporting and data system (PIRADS) for PI-RADS 3 score lesions [ 48 ]; the creation of frameworks for automated PCa localization and detection [ 49 ]; and, finally, the management of radiotherapy treatment and toxicity [ 50 , 51 , 52 , 53 , 54 , 55 , 56 ], and the prediction of BCR [ 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 ]. Additionally, radiomics and AI algorithms will help to limit the discrepancies between different readers [ 66 ].…”

Section: Resultsmentioning

confidence: 99%

Prostate Cancer Radiogenomics—From Imaging to Molecular Characterization

Ferro

Cobelli

Vartolomei

et al. 2021

IJMS

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Radiomics and genomics represent two of the most promising fields of cancer research, designed to improve the risk stratification and disease management of patients with prostate cancer (PCa). Radiomics involves a conversion of imaging derivate quantitative features using manual or automated algorithms, enhancing existing data through mathematical analysis. This could increase the clinical value in PCa management. To extract features from imaging methods such as magnetic resonance imaging (MRI), the empiric nature of the analysis using machine learning and artificial intelligence could help make the best clinical decisions. Genomics information can be explained or decoded by radiomics. The development of methodologies can create more-efficient predictive models and can better characterize the molecular features of PCa. Additionally, the identification of new imaging biomarkers can overcome the known heterogeneity of PCa, by non-invasive radiological assessment of the whole specific organ. In the future, the validation of recent findings, in large, randomized cohorts of PCa patients, can establish the role of radiogenomics. Briefly, we aimed to review the current literature of highly quantitative and qualitative results from well-designed studies for the diagnoses, treatment, and follow-up of prostate cancer, based on radiomics, genomics and radiogenomics research.

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Section: Resultsmentioning

confidence: 99%

Prostate Cancer Radiogenomics—From Imaging to Molecular Characterization

Ferro

Cobelli

Vartolomei

et al. 2021

IJMS

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“…However, it is necessary to point out that all SR sections are independent from each other, so that the Patient Clinical Data and Clinical Evaluation sections are optional and may be filled in or not upon user choice, although they were conceived with the aim of creating databases. In fact, the possibility of collecting all these data could allow the creation of a large database, not only for epidemiological studies, but also in the highest conception of radiology, to lay the foundations for radiomics studies [34][35][36][37]. Radiology reports should be rich in data that could potentially be pooled, analyzed and correlated with patient outcomes, thereby assisting future clinical and imaging guidelines.…”

Section: Discussionmentioning

confidence: 99%

Structured Reporting of Lung Cancer Staging: A Consensus Proposal

et al. 2021

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Background: Structured reporting (SR) in radiology is becoming necessary and has recently been recognized by major scientific societies. This study aimed to build CT-based structured reports for lung cancer during the staging phase, in order to improve communication between radiologists, members of the multidisciplinary team and patients. Materials and Methods: A panel of expert radiologists, members of the Italian Society of Medical and Interventional Radiology, was established. A modified Delphi exercise was used to build the structural report and to assess the level of agreement for all the report sections. The Cronbach’s alpha (Cα) correlation coefficient was used to assess internal consistency for each section and to perform a quality analysis according to the average inter-item correlation. Results: The final SR version was built by including 16 items in the “Patient Clinical Data” section, 4 items in the “Clinical Evaluation” section, 8 items in the “Exam Technique” section, 22 items in the “Report” section, and 5 items in the “Conclusion” section. Overall, 55 items were included in the final version of the SR. The overall mean of the scores of the experts and the sum of scores for the structured report were 4.5 (range 1–5) and 631 (mean value 67.54, STD 7.53), respectively, in the first round. The items of the structured report with higher accordance in the first round were primary lesion features, lymph nodes, metastasis and conclusions. The overall mean of the scores of the experts and the sum of scores for staging in the structured report were 4.7 (range 4–5) and 807 (mean value 70.11, STD 4.81), respectively, in the second round. The Cronbach’s alpha (Cα) correlation coefficient was 0.89 in the first round and 0.92 in the second round for staging in the structured report. Conclusions: The wide implementation of SR is critical for providing referring physicians and patients with the best quality of service, and for providing researchers with the best quality of data in the context of the big data exploitation of the available clinical data. Implementation is complex, requiring mature technology to successfully address pending user-friendliness, organizational and interoperability challenges.

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“…Radiomics consists of the extraction of several parameters by radiological data that can provide information about tumor phenotype as well as the cancer microenvironment [152][153][154][155][156][157][158][159][160]. Radiomics, when combined with other data linked to patient outcome, can produce precise evidence-basedclinical-decision support systems.…”

Section: Radiomics Analysismentioning

confidence: 99%

Diagnostic evaluation and ablation treatments assessment in hepatocellular carcinoma

Granata

Grassi

Fusco³

et al. 2021

Infect Agents Cancer

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This article provides an overview of diagnostic evaluation and ablation treatment assessment in Hepatocellular Carcinoma (HCC). Only studies, in the English language from January 2010 to January 202, evaluating the diagnostic tools and assessment of ablative therapies in HCC patients were included. We found 173 clinical studies that satisfied the inclusion criteria.HCC may be noninvasively diagnosed by imaging findings. Multiphase contrast-enhanced imaging is necessary to assess HCC. Intravenous extracellular contrast agents are used for CT, while the agents used for MRI may be extracellular or hepatobiliary. Both gadoxetate disodium and gadobenate dimeglumine may be used in hepatobiliary phase imaging. For treatment-naive patients undergoing CT, unenhanced imaging is optional; however, it is required in the post treatment setting for CT and all MRI studies. Late arterial phase is strongly preferred over early arterial phase. The choice of modality (CT, US/CEUS or MRI) and MRI contrast agent (extracelllar or hepatobiliary) depends on patient, institutional, and regional factors. MRI allows to link morfological and functional data in the HCC evaluation. Also, Radiomics is an emerging field in the assessment of HCC patients.Postablation imaging is necessary to assess the treatment results, to monitor evolution of the ablated tissue over time, and to evaluate for complications. Post- thermal treatments, imaging should be performed at regularly scheduled intervals to assess treatment response and to evaluate for new lesions and potential complications.

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Radiomic features for prostate cancer grade detection through formal verification

Cited by 55 publications

References 31 publications

Prostate Cancer Radiogenomics—From Imaging to Molecular Characterization

Prostate Cancer Radiogenomics—From Imaging to Molecular Characterization

Structured Reporting of Lung Cancer Staging: A Consensus Proposal

Diagnostic evaluation and ablation treatments assessment in hepatocellular carcinoma

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