Radiologia Medica最新文献

{"title":"Effect of slice thickness on quantitative analysis of interstitial lung disease: a retrospective volumetric chest CT study.","authors":"Marijan Pušeljić, Borut Mohorko, Tadej Počivavšek, Florentine Moazedi-Fürst, Johannes Schmid, Michael Fuchsjäger, Emina Talakić","doi":"10.1007/s11547-025-02023-w","DOIUrl":"https://doi.org/10.1007/s11547-025-02023-w","url":null,"abstract":"<p><strong>Introduction: </strong>High-resolution computed tomography (HRCT) is essential for evaluating interstitial lung disease (ILD). The effect of slice thickness on threshold-based quantification of individual ILD components remains underexplored. This study investigates the effect of slice thickness on ILD quantification using Lung CT Analyzer.</p><p><strong>Methods: </strong>Retrospective analysis of 53 ILD patients (mean age 64.3 ± 14.1 years) who underwent chest CT scans with HRCT (slice thickness ≤ 1.25 mm) and conventional CT (CCT, ≥ 2.5 mm) reconstructions. Quantitative lung volumes, functional parenchyma, emphysema, ground-glass opacity (GGO), consolidation and affected parenchyma were assessed. The effects of contrast media (CM) application and ILD pattern was assessed separately.</p><p><strong>Results: </strong>Emphysema volume was significantly higher in HRCT compared to CCT for the whole lung (766.9 ± 568.3 mL vs. 482.6 ± 454.4 mL, p < 0.001), the right lung (431.4 ± 314.6 mL vs. 270.2 ± 251.3 mL, p < 0.001), and the left lung (337.3 ± 259.9 mL vs. 228.0 ± 221.5 mL, p < 0.001). Consolidation volumes also differed significantly between HRCT and CCT for the whole lung (271.6 ± 128.4 mL vs. 252.0 ± 126.3 mL, p < 0.001), with similar findings for the right and left lung. Functional volume was underestimated in CCT reconstructions. No significant differences were observed for GGO volumes or overall affected parenchyma. CM application and ILD pattern had no significant interaction on the measurements.</p><p><strong>Conclusion: </strong>Slice thickness significantly affects the quantification of functional parenchyma, emphysema and consolidation, whereas GGO and the overall ILD extent remain unaffected.</p>","PeriodicalId":20817,"journal":{"name":"Radiologia Medica","volume":" ","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144151271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Virtual biopsy through CT imaging: can radiomics differentiate between subtypes of non-small cell lung cancer?","authors":"Federica Palmeri, Marta Zerunian, Michela Polici, Stefano Nardacci, Chiara De Dominicis, Bianca Allegra, Andrea Monterubbiano, Massimiliano Mancini, Riccardo Ferrari, Pasquale Paolantonio, Domenico De Santis, Andrea Laghi, Damiano Caruso","doi":"10.1007/s11547-025-02022-x","DOIUrl":"https://doi.org/10.1007/s11547-025-02022-x","url":null,"abstract":"<p><strong>Objective: </strong>This study evaluated the performance of CT radiomics in distinguishing between lung adenocarcinoma (ADC) and squamous cell carcinoma (SCC) at baseline imaging, exploring its potential as a noninvasive virtual biopsy.</p><p><strong>Materials and methods: </strong>A retrospective analysis was conducted, enrolling 330 patients between September 2015 and January 2023. Inclusion criteria were histologically proven ADC or SCC and baseline contrast-enhanced chest CT. Exclusion criteria included significant motion artifacts and nodules < 6 mm. Radiological features, including lung lobe affected, peripheral/central location, presence of emphysema, and T/N radiological stage, were assessed for each patient. Volumetric segmentation of lung cancers was performed on baseline CT scans at the portal-venous phase using 3DSlicer software (v5.2.2). A total of 107 radiomic features were extracted and selected using the least absolute shrinkage and selection operator (LASSO) and tenfold cross-validation. Multivariable logistic regression analysis was employed to develop three predictive models: radiological features-only, radiomics-only, and a combined model, with statistical significance set at p < 0.05. Additionally, an independent external validation cohort of 16 patients, meeting the same inclusion and exclusion criteria, was identified.</p><p><strong>Results: </strong>The final cohort comprised 200 ADC and 100 SCC patients (mean age 68 ± 10 years, 184 men). Two radiological and 21 radiomic features were selected (p < 0.001). The Radiological model achieved AUC 0.73 (95% CI 0.68-0.78, p < 0.001), 72.3% accuracy. The radiomics model achieved AUC 0.80 (95% CI 0.75-0.85, p < 0.001), 75.6% accuracy. The combined model achieved AUC 0.84 (95% CI 0.80-0.88, p < 0.001), 75.3% accuracy. External validation (n = 15) yielded AUC 0.78 (p = 0.05).</p><p><strong>Conclusion: </strong>The combined radiologic-radiomics model showed the best performance in differentiating ADC from SCC.</p>","PeriodicalId":20817,"journal":{"name":"Radiologia Medica","volume":" ","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144120726","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High-resolution quantitative mapping of extracellular pH by ratiometric MRI with iron chelates in a tumor mouse model.","authors":"Honglan Mi, Philipp Boehm-Sturm, Akvile Haeckel, Ying Li, Susanne Mueller, Fei Ni, Harald Kratz, Marco Foddis, Jing Xie, Eyk Schellenberger","doi":"10.1007/s11547-025-02020-z","DOIUrl":"https://doi.org/10.1007/s11547-025-02020-z","url":null,"abstract":"<p><strong>Purpose: </strong>The aim of this study was to generate quantitative extracellular pH maps of tumors using a combination of a pH-sensitive iron chelate-based contrast agent (IBCA) and a pH-insensitive IBCA for concentration measurement, which we termed ratiometric pH magnetic resonance imaging (RpH-MRI).</p><p><strong>Methods: </strong>The pH-sensitive IBCA of ethylenediamine-trans-cyclohexane diamine tetraacetic acid (Fe-en-tCDTA) was synthesized, along with the pH-insensitive IBCAs of trans-cyclohexane diamine tetraacetic acid (Fe-tCDTA) and diethylenetriamine-N,N,N',N″,N″-pentaacetic acid (Fe-DTPA). The pH-dependent T1 contrast effects of these chelates were compared in water and serum phantoms at 0.94 T, 3 T and 7 T. For in vivo pH mapping of tumors at 7 T, 4T1 breast cancer cells were inoculated subcutaneously into the flanks of the BALB/c mice. RpH-MRI was performed with two sequential intravenous applications: first a pH-insensitive IBCA, followed by the pH-sensitive IBCA at the same dose (0.25 or 0.5 mmol/kg) with an interval of either 30 or 60 min. Quantitative pH maps were generated by calculating T1, S₀, and relative maximum enhancement maps of the two injections, together with pH-dependent T1-relaxivity parameters derived from in vitro measurements of the pH-sensitive IBCA and pH-insensitive control IBCA.</p><p><strong>Results: </strong>The T1 relaxivity (r1) of Fe-en-tCDTA was highly pH dependent, being approximately 2.7 times higher at pH 5.5 than at neutral pH, whereas Fe-DTPA and Fe-tCDTA showed stable r1 values between pH 5.5-7.4. In vivo, the time to maximum signal intensity (TMI) of the tumors of Fe-DTPA as control was comparable to that of Fe-en-tCDTA (2.57 ± 1.34 min vs. 2.683 ± 0.89 min, p = 0.7596, paired t test, 4 mice, 7 tumors) as well as for Fe-tCDTA as control versus Fe-en-tCDTA (3.30 ± 1.17 min vs. 3.627 ± 1.12 min, p = 0.2101, paired t test, 7 mice, 13 tumors), suggesting similar pharmacokinetics. The concentration distribution at TMI of the control chelates was assumed to be the same as that of the second injected Fe-en-tCDTA. The dynamic contrast enhanced MRI curve of the first injection of Fe-DTPA returned to baseline after 20-30 min, whereas Fe-tCDTA took 30-60 min to reach baseline. Calculated core and rim pH values were 6.512 ± 0.182 and 6.742 ± 0.121, respectively (p < 0.0001, paired t test, 11 mice, 20 tumors) with core areas showing lower chelate concentrations but higher T1 relaxivity; the mean tumor-wide pH value was 6.632 ± 0.140.</p><p><strong>Conclusion: </strong>Our results demonstrate the potential of high-resolution RpH-MRI based on pH-sensitive and pH-insensitive IBCAs for mapping tumor extracellular pH and concentration distribution.</p>","PeriodicalId":20817,"journal":{"name":"Radiologia Medica","volume":" ","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144111611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction: Transarterial embolization for acute lower gastrointestinal bleeding: a retrospective bicentric study.","authors":"Francesco Tiralongo, Daniele Perini, Luca Crimi, Makoto Taninokuchi Tomassoni, Lorenzo Braccischi, Davide Giuseppe Castiglione, Francesco Modestino, Francesco Vacirca, Daniele Falsaperla, Federica Maria Rosaria Libra, Stefano Palmucci, Pietro Valerio Foti, Francesco Lionetti, Cristina Mosconi, Antonio Basile","doi":"10.1007/s11547-025-02024-9","DOIUrl":"https://doi.org/10.1007/s11547-025-02024-9","url":null,"abstract":"","PeriodicalId":20817,"journal":{"name":"Radiologia Medica","volume":" ","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144111573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accelerated deep learning-based function assessment in cardiovascular magnetic resonance.","authors":"Domenico De Santis, Federica Fanelli, Luca Pugliese, Giovanna Grazia Bona, Tiziano Polidori, Curzio Santangeli, Michela Polici, Antonella Del Gaudio, Giuseppe Tremamunno, Marta Zerunian, Andrea Laghi, Damiano Caruso","doi":"10.1007/s11547-025-02019-6","DOIUrl":"https://doi.org/10.1007/s11547-025-02019-6","url":null,"abstract":"<p><strong>Purpose: </strong>To evaluate diagnostic accuracy and image quality of deep learning (DL) cine sequences for LV and RV parameters compared to conventional balanced steady-state free precession (bSSFP) cine sequences in cardiovascular magnetic resonance (CMR).</p><p><strong>Material and methods: </strong>From January to April 2024, patients with clinically indicated CMR were prospectively included. LV and RV were segmented from short-axis bSSFP and DL cine sequences. LV and RV end-diastolic volume (EDV), end-systolic volume (EDV), stroke volume (SV), ejection fraction, and LV end-diastolic mass were calculated. The acquisition time of both sequences was registered. Results were compared with paired-samples t test or Wilcoxon signed-rank test. Agreement between DL cine and bSSFP was assessed using Bland-Altman plots. Image quality was graded by two readers based on blood-to-myocardium contrast, endocardial edge definition, and motion artifacts, using a 5-point Likert scale (1 = insufficient quality; 5 = excellent quality).</p><p><strong>Results: </strong>Sixty-two patients were included (mean age: 47 ± 17 years, 41 men). No significant differences between DL cine and bSSFP were found for all LV and RV parameters (P ≥ .176). DL cine was significantly faster (1.35 ± .55 m vs 2.83 ± .79 m; P < .001). The agreement between DL cine and bSSFP was strong, with bias ranging from 45 to 1.75% for LV and from - 0.38 to 2.43% for RV. Among LV parameters, the highest agreement was obtained for ESV and SV, which fell within the acceptable limit of agreement (LOA) in 84% of cases. EDV obtained the highest agreement among RV parameters, falling within the acceptable LOA in 90% of cases. Overall image quality was comparable (median: 5, IQR: 4-5; P = .330), while endocardial edge definition of DL cine (median: 4, IQR: 4-5) was lower than bSSFP (median: 5, IQR: 4-5; P = .002).</p><p><strong>Conclusion: </strong>DL cine allows fast and accurate quantification of LV and RV parameters and comparable image quality with conventional bSSFP.</p>","PeriodicalId":20817,"journal":{"name":"Radiologia Medica","volume":" ","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144086583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The \"purfling sign\": a new imaging marker for the diagnosis of primary CNS lymphoma.","authors":"Lei Niu, Jiping Zhao, Chongfeng Duan, Weiwei Fu, Yingchao Liu, Xuejun Liu, Shuangshuang Song","doi":"10.1007/s11547-025-01970-8","DOIUrl":"https://doi.org/10.1007/s11547-025-01970-8","url":null,"abstract":"<p><strong>Objectives: </strong>To investigate whether the \"purfling sign,\" a new imaging marker, could distinguish primary central nervous system lymphoma (PCNSL) from brain gliomas, and its diagnosis value for preoperative identification of PCNSL.</p><p><strong>Methods: </strong>Contrast-enhanced MR imaging features of 161 PCNSL and 161 glioma were evaluated by 2 independent neuroradiologists: (1) the presence/absence of the \"purfling sign\"; (2) the presence/absence of lesion necrosis and cystic changes; and (3) the heterogeneity of tumor parenchymal enhancement. Inter-rater agreement was assessed with Cohen's kappa (κ), and the diagnostic performance of the \"purfling sign\" in identifying PCNSL was investigated. Three separate institutional validation cohort (including 177 PCNSL and 177 glioma patients) was analyzed to validate the diagnostic performance of the \"purfling sign.\"</p><p><strong>Results: </strong>Among the test set, the inter-rater agreement of the \"purfling sign\" was high (κ = 0.907), while that for the other features was only good [κ = 0.663-0.691]. The purfling sign was present in 89 (55.28%) lymphoma and 13 (8.07%) glioma cases with a specificity of 91.93%, a sensitivity of 55.28%, a positive predictive value (PPV) of 87.25%, and a negative predictive value (NPV) of 67.27% for the diagnosis of PCNSL. Furthermore, the tumors presenting with the \"target sign\" were all PCNSL (16/16,100%), with a specificity and PPV of 100%. Analysis with the validation cohort, 85.09% cases with a positive \"purfling sign\" were PCNSL (p < 0.0001; PPV = 85.09%, NPV = 66.67%, specificity = 90.40%, sensitivity = 54.80%).</p><p><strong>Conclusions: </strong>With a robust inter-rater agreement, our study found that the \"purfling sign\" on enhanced MR represents a high specific imaging marker for the preoperative diagnosis of PCNSL. This noninvasive marker may aid in the guidance of the clinical diagnosis and treatment processes of PCNSL.</p>","PeriodicalId":20817,"journal":{"name":"Radiologia Medica","volume":" ","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144022079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The added value of artificial intelligence using Quantib Prostate for the detection of prostate cancer at multiparametric magnetic resonance imaging.","authors":"Tommaso Russo, Leonardo Quarta, Francesco Pellegrino, Michele Cosenza, Enrico Camisassa, Salvatore Lavalle, Giovanni Apostolo, Paolo Zaurito, Simone Scuderi, Francesco Barletta, Clara Marzorati, Armando Stabile, Francesco Montorsi, Francesco De Cobelli, Giorgio Brembilla, Giorgio Gandaglia, Alberto Briganti","doi":"10.1007/s11547-025-02017-8","DOIUrl":"https://doi.org/10.1007/s11547-025-02017-8","url":null,"abstract":"<p><strong>Purpose: </strong>Artificial intelligence (AI) has been proposed to assist radiologists in reporting multiparametric magnetic resonance imaging (mpMRI) of the prostate. We evaluate the diagnostic performance of radiologists with different levels of experience when reporting mpMRI with the support of available AI-based software (Quantib Prostate).</p><p><strong>Material and methods: </strong>This is a single-center study (NCT06298305) involving 110 patients. Those with a positive mpMRI (PI-RADS ≥ 3) underwent targeted plus systematic biopsy (TBx plus SBx), while those with a negative mpMRI but a high clinical suspicion of prostate cancer (PCa) underwent SBx. Three readers with different levels of experience, identified as R1, R2, and R3 reviewed all mpMRI. Inter-reader agreement among the three readers with or without the assistance of Quantib Prostate as well as sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy for the detection of clinically significant PCa (csPCa) were assessed.</p><p><strong>Results: </strong>102 patients underwent prostate biopsy and the csPCa detection rate was 47%. Using Quantib Prostate resulted in an increased number of lesions identified for R3 (101 vs. 127). Inter-reader agreement slightly increased when using Quantib Prostate from 0.37 to 0.41 without vs. with Quantib Prostate, respectively. PPV, NPV and diagnostic accuracy (measured by the area under the curve [AUC]) of R3 improved (0.51 vs. 0.55, 0.65 vs.0.82 and 0.56 vs. 0.62, respectively). Conversely, no changes were observed for R1 and R2.</p><p><strong>Conclusions: </strong>Using Quantib Prostate did not enhance the detection rate of csPCa for readers with some experience in prostate imaging. However, for an inexperienced reader, this AI-based software is demonstrated to improve the performance.</p><p><strong>Trial registration: </strong>Name of registry: clinicaltrials.gov.</p><p><strong>Trial registration number: </strong>NCT06298305. Date of registration: 2022-09.</p>","PeriodicalId":20817,"journal":{"name":"Radiologia Medica","volume":" ","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143995747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The use of machine learning in transarterial chemoembolisation/transarterial embolisation for patients with intermediate-stage hepatocellular carcinoma: a systematic review.","authors":"Lakshya Soni, Jasen Soopramanien, Amish Acharya, Hutan Ashrafian, Stamatia Giannarou, Nicos Fotiadis, Ara Darzi","doi":"10.1007/s11547-025-02013-y","DOIUrl":"https://doi.org/10.1007/s11547-025-02013-y","url":null,"abstract":"<p><p>Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related deaths worldwide. Intermediate-stage HCC is often treated with either transcatheter arterial chemoembolisation (TACE) or transcatheter arterial embolisation (TAE). Integrating machine learning (ML) offers the possibility of improving treatment outcomes through enhanced patient selection. This systematic review evaluates the effectiveness of ML models in improving the precision and efficacy of both TACE and TAE for intermediate-stage HCC. A comprehensive search of PubMed, EMBASE, Web of Science, and Cochrane Library databases was conducted for studies applying ML models to TACE and TAE in patients with intermediate-stage HCC. Seven studies involving 4,017 patients were included. All included studies were from China. Various ML models, including deep learning and radiomics, were used to predict treatment response, yielding a high predictive accuracy (AUC 0.90). However, study heterogeneity limited comparisons. While ML shows potential in predicting treatment outcomes, further research with standardised protocols and larger, multi-centre trials is needed for clinical integration.</p>","PeriodicalId":20817,"journal":{"name":"Radiologia Medica","volume":" ","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144027592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Personalized screening based on risk and density: prevalence data from the RIBBS study.","authors":"Francesca Caumo, Gisella Gennaro, Alessandra Ravaioli, Enrica Baldan, Elisabetta Bezzon, Silvia Bottin, Paolo Carlevaris, Lina Ciampani, Alessandro Coran, Chiara Dal Bosco, Sara Del Genio, Alessia Dalla Pietà, Fabio Falcini, Federico Maggetto, Giuseppe Manco, Tiziana Masiero, Maria Petrioli, Ilaria Polico, Tiziana Pisapia, Martina Zemella, Manuel Zorzi, Stefania Zovato, Lauro Bucchi","doi":"10.1007/s11547-025-01981-5","DOIUrl":"10.1007/s11547-025-01981-5","url":null,"abstract":"<p><strong>Purpose: </strong>To present the prevalence screening results of the RIsk-Based Breast Screening (RIBBS) study (ClinicalTrials.gov NCT05675085), a quasi-experimental population-based study evaluating a personalized screening model for women aged 45-49. This model uses digital breast tomosynthesis (DBT) and stratifies participants by risk and breast density, incorporating tailored screening intervals with or without supplemental imaging (ultrasound, US, and breast MRI), with the goal of reducing advanced breast cancer (BC) incidence compared to annual digital mammography (DM).</p><p><strong>Materials and methods: </strong>An interventional cohort of 10,269 women aged 45 was enrolled (January 2020-December 2021. Participants underwent DBT and completed a BC risk questionnaire. Volumetric breast density and lifetime risk were used to assign five subgroups to tailored screening regimens: low-risk low-density (LR-LD), low-risk high-density (LR-HD), intermediate-risk low-density (IR-LD), intermediate-risk high-density (IR-HD), and high-risk (HR). Screening performance was compared with an observational control cohort of 43,838 women undergoing annual DM.</p><p><strong>Results: </strong>Compared to LR-LD, intermediate-risk groups showed a 4.9- (IR-LD) and 4.6-fold (IR-HD) higher prevalence of BC, driven by a 7.1- and 7.1-fold higher prevalence of pT1c tumors. The interventional cohort had lower recall rate (rate ratio, 0.5), higher surgery rate (1.9) and increased prevalence of DCIS (2.9), pT1c (2.3) and grade 3 tumors (2.4), compared to controls.</p><p><strong>Conclusion: </strong>The prevalence screening demonstrated the feasibility of using DBT and -in high-density subgroups- supplemental US. The stratification criteria effectively identified subpopulations with different BC prevalence. Increasing the detection rate of pT1c tumors is not sufficient but necessary to achieve a reduction in advanced BC incidence.</p>","PeriodicalId":20817,"journal":{"name":"Radiologia Medica","volume":" ","pages":"740-752"},"PeriodicalIF":9.7,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143674318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Role of radiomics in predicting early disease recurrence in locally advanced breast cancer patients: integration of radiomic features and RECIST criteria.","authors":"Charlotte Trombadori, Edda Boccia, Elena Huong Tran, Antonio Franco, Armando Orlandi, Gianluca Franceschini, Luisa Carbognin, Alba Di Leone, Valeria Masiello, Fabio Marazzi, Antonella Palazzo, Ida Paris, Roberta Dattoli, Antonino Mulè, Nikola Dino Capocchiano, Diana Giannarelli, Riccardo Masetti, Paolo Belli, Luca Boldrini, Anna D'Angelo, Alessandra Fabi","doi":"10.1007/s11547-025-01984-2","DOIUrl":"10.1007/s11547-025-01984-2","url":null,"abstract":"<p><strong>Background: </strong>Breast cancer (BC) is a major global health issue with significant heterogeneity among its subtypes. Neoadjuvant treatment (NAT) has been extended to include early BC patients, particularly those with HER2 + and triple-negative subtypes, to achieve pathological complete response and improve long-term outcomes. However, disease recurrence remains a challenge, highlighting the need for predictive biomarkers. This study evaluates the role of radiomics from pre-treatment breast MRI, integrated with clinical and radiological variables, in predicting early disease recurrence (EDR) after NAT.</p><p><strong>Methods: </strong>A retrospective analysis was conducted on 238 BC patients treated with NAT and assessed using pre- and post-treatment breast MRI. Radiomic features were extracted and combined with clinical and radiological data to develop predictive models for EDR. Models were evaluated using AUC, accuracy, sensitivity, and specificity metrics.</p><p><strong>Results: </strong>The radiological-radiomic model, which integrated pre-treatment MRI radiomics with RECIST response data, demonstrated the highest predictive performance for EDR (AUC 0.77, sensitivity 0.85). Internal validation confirmed the robustness of the model.</p><p><strong>Conclusion: </strong>Combining radiomic features from pre-NAT MRI with RECIST response evaluation from post-NAT MRI enhances the prediction of EDR in BC patients, supporting precision medicine in treatment strategies and follow-up planning. Further validation on larger cohorts is needed to confirm these findings.</p>","PeriodicalId":20817,"journal":{"name":"Radiologia Medica","volume":" ","pages":"753-765"},"PeriodicalIF":9.7,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143674320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}