MRI-based radiomics signature for localized prostate cancer: a new clinical tool for cancer aggressiveness prediction? Sub-study of prospective phase II trial on ultra-hypofractionated radiotherapy (AIRC IG-13218)

Gugliandolo, Simone Giovanni; Pepa, Matteo; Isaksson, Lars Johannes; Marvaso, Giulia; Raimondi, Sara; Botta, Francesca; Gandini, Sara; Ciardo, D.; Volpe, Stefania; Riva, Giulia; Rojas, D.P.; Zerini, Dario; Pricolo, Paola; Alessi, S.; Petralia, Giuseppe; Summers, Paul; Mistretta, Francesco A.; Luzzago, Stefano; Cattani, Federica; Cobelli, Ottavio De; Cassano, Enrico; Cremonesi, Marta; Bellomi, Massimo; Orecchia, Roberto; Jereczek‐Fossa, Barbara Alicja

doi:10.1007/s00330-020-07105-z

Cited by 37 publications

(61 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Resultsmentioning

confidence: 99%

Prostate Cancer Radiogenomics—From Imaging to Molecular Characterization

Ferro

Cobelli

Vartolomei

et al. 2021

IJMS

Self Cite

View full text Add to dashboard Cite

show abstract

“…Interestingly, they proposed different discretization schemas for different texture index groups, namely, the FBS for model-based first-and second-order features, and in the case of the second-order feature-based signatures the FBN is recommended. Nevertheless, in recent studies it is not uncommon that the discretization method used remains undisclosed [34][35][36][37]. Because of these contradictory results reported in studies, it is important to investigate the role of discretization methods in the radiomic features obtained for MRI images.…”

Section: Introductionmentioning

confidence: 99%

Effect of grey-level discretization on texture feature on different weighted MRI images of diverse disease groups

et al. 2021

View full text Add to dashboard Cite

Purpose Many studies of MRI radiomics do not include the discretization method used for the analyses, which might indicate that the discretization methods used are considered irrelevant. Our goals were to compare three frequently used discretization methods (lesion relative resampling (LRR), lesion absolute resampling (LAR) and absolute resampling (AR)) applied to the same data set, along with two different lesion segmentation approaches. Methods We analyzed the effects of altering bin widths or bin numbers for the three different sampling methods using 40 texture indices (TIs). The impact was evaluated on brain MRI studies obtained for 71 patients divided into three different disease groups: multiple sclerosis (MS, N = 22), ischemic stroke (IS, N = 22), cancer patients (N = 27). Two different MRI acquisition protocols were considered for all patients, a T2- and a post-contrast 3D T1-weighted MRI sequence. Elliptical and manually drawn VOIs were employed for both imaging series. Three different types of gray-level discretization methods were used: LRR, LAR and AR. Hypothesis tests were done among all diseased and control areas to compare the TI values in these areas. We also did correlation analyses between TI values and lesion volumes. Results In general, no significant differences were reported in the results when employing the AR and LAR discretization methods. It was found that employing 38 TIs introduced variation in the results when the number of bin parameters was altered, suggesting that both the degree and direction of monotonicity between each TI value and binning parameters were characteristic for each TI. Furthermore, while TIs were changing with altering binning values, no changes correlated to neither disease nor the MRI sequence. We found that most indices correlated weakly with the volume, while the correlation coefficients were independent of both diseases analyzed and MR contrast. Several cooccurrence-matrix based texture parameters show a definite higher correlation when employing the LRR discretization method However, with the best correlations obtained for the manually drawn VOI. Hypothesis tests among all disease and control areas (co-lateral hemisphere) revealed that the AR or LAR discretization techniques provide more suitable texture features than LRR. In addition, the manually drawn segmentation gave fewer significantly different TIs than the ellipsoid segmentations. In addition, the amount of TIs with significant differences was increasing with increasing the number of bins, or decreasing bin widths. Conclusion Our findings indicate that the AR discretization method may offer the best texture analysis in MR image assessments. Employing too many bins or too large bin widths might reduce the selection of TIs that can be used for differential diagnosis. In general, more statistically different TIs were observed for elliptical segmentations when compared to the manually drawn VOIs. In the texture analysis of MR studies, studies and publications should report on all important parameters and methods related to data collection, corrections, normalization, discretization, and segmentation.

show abstract

“…To date, risk-assessment of PCa recurrence is based on clinical parameters (i.e., GS, PSA level, cancer grading and tumor stage) and no objective and accurate tools to stratify cancer patient into low- and intermediate-risk is currently available. In that context, Radiomics is a promising tool to support clinical management of these patients and achieved good results in stratifying patients according to risk of recurrence [ 38 , 39 , 40 ]. Recently, Gugliandolo S.G. et al [ 38 ] obtained a radiomic signature, from 65 mpMRI (T2w images) of localized PCa, to distinguish low- from intermediate-risk patients.…”

Section: Prostate Cancermentioning

confidence: 99%

“…In that context, Radiomics is a promising tool to support clinical management of these patients and achieved good results in stratifying patients according to risk of recurrence [ 38 , 39 , 40 ]. Recently, Gugliandolo S.G. et al [ 38 ] obtained a radiomic signature, from 65 mpMRI (T2w images) of localized PCa, to distinguish low- from intermediate-risk patients. Texture features were the main predictive parameters of Gleason Score, PI-RADS and risk-classification, while intensity domain was strictly linked to T-stage, extracapsular extension score and risk-classification (AUC ranging from 0.74 to 0.94).…”

Section: Prostate Cancermentioning

confidence: 99%

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

Polici

Zerunian

Pucciarelli

et al. 2021

Cancers

View full text Add to dashboard Cite

Radiomics has the potential to play a pivotal role in oncological translational imaging, particularly in cancer detection, prognosis prediction and response to therapy evaluation. To date, several studies established Radiomics as a useful tool in oncologic imaging, able to support clinicians in practicing evidence-based medicine, uniquely tailored to each patient and tumor. Mineable data, extracted from medical images could be combined with clinical and survival parameters to develop models useful for the clinicians in cancer patients’ assessment. As such, adding Radiomics to traditional subjective imaging may provide a quantitative and extensive cancer evaluation reflecting histologic architecture. In this Part II, we present an overview of radiomic applications in thoracic, genito-urinary, breast, neurological, hematologic and musculoskeletal oncologic applications.

show abstract

MRI-based radiomics signature for localized prostate cancer: a new clinical tool for cancer aggressiveness prediction? Sub-study of prospective phase II trial on ultra-hypofractionated radiotherapy (AIRC IG-13218)

Cited by 37 publications

References 31 publications

Prostate Cancer Radiogenomics—From Imaging to Molecular Characterization

Prostate Cancer Radiogenomics—From Imaging to Molecular Characterization

Effect of grey-level discretization on texture feature on different weighted MRI images of diverse disease groups

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

Contact Info

Product

Resources

About