Background:Magnetic Resonance-guided Focused Ultrasound (MRgFUS) thalamotomy is an established incisionless treatment for medication-refractory essential tremor (ET). While staged-bilateral MRgFUS thalamotomy has recently gained clinical acceptance, detailed radiological and procedural comparisons between first- and second-side interventions remain limited. This study aimed to provide a comprehensive imaging-based and procedural characterisation of staged bilateral MRgFUS thalamotomy, with a specific focus on stereotactic targeting, sonication parameters, and lesion morphology. Materials and methods:In this retrospective-prospective, single-centre observational study, consecutive patients with ET undergoing staged bilateral MRgFUS thalamotomy were included. Procedural metrics, stereotactic targeting coordinates, and sonication parameters were compared between FUS1 and FUS2 procedures. MRI analyses were performed using a standardised protocol including morphological pre-treatment 3D T1-weighted imaging and post-treatment 3D T2-weighted imaging acquired within 24 h and at approximately 1 month to document the lesion. Treatment-related lesions were segmented using a semi-automated approach, with volumetric measurements independently obtained by two blinded raters; inter-rater agreement was assessed using intraclass correlation coefficients. Adverse events (AEs) were recorded as secondary outcomes. A systematic review of the literature on treatment strategy of staged bilateral MRgFUS thalamotomy was conducted. Results:Fifteen patients underwent staged bilateral MRgFUS thalamotomy. Most anatomical, procedural, and sonication-related parameters were comparable between FUS1 and FUS2. Final stereotactic targeting during FUS2 showed a small but consistent anterior and dorsal shift relative to FUS1. Lesion volumes measured at both 24 h and 1 month after the procedure did not differ significantly between FUS1 and FUS2, and inter-rater agreement for lesion volumetry was excellent across time points (ICC > 0.91). AEs after FUS2 were predominantly mild and transient. We found no significant differences in lesion volume or inter-side targeting displacement between patients with and without gait disturbances, the most common AE, persisting at 1 month after FUS2. Single-patient imaging analyses suggested heterogeneous spatial lesion configurations in patients with AEs persistent 6-12 months after FUS2. Conclusion:In this single-centre cohort, staged bilateral MRgFUS thalamotomy showed high procedural and radiological consistency between FUS1 and FUS2. MRI-based volumetric analyses show consistent lesion morphology across hemispheres, with small, safety-oriented refinements in second-side targeting, in line with the literature.

Introduction Autosomal dominant polycystic kidney disease (ADPKD) is the most common hereditary kidney disorder and a leading cause of end-stage kidney disease (ESKD). Current risk stratification relies on height-adjusted total kidney volume (ht-TKV), which captures cyst burden but not alterations in the noncystic parenchyma. Radiomics enables extraction of quantitative features reflecting tissue microstructure across both cystic and noncystic kidney compartments. We investigated whether radiomic features from apparent diffusion coefficient (ADC) maps improve prediction of renal function decline in ADPKD. Methods We analyzed 112 patients with ADPKD from the Bern ADPKD Registry with baseline magnetic resonance imaging (MRI, T1-, T2-, and diffusion-weighted). Kidneys were manually segmented, and radiomic features were extracted using PyRadiomics. Features were selected using least absolute shrinkage and selection operator, and models were developed using support vector machines with 20-fold cross-validation. The outcome was rapid renal function decline (estimated glomerular filtration rate [eGFR] ≤ -3 ml/min per 1.73 m2/year) over a median follow-up of 5.5 years. Results ADC-based radiomics provided the highest area under the curve (AUC: 0.82), compared with clinical (0.77) and T1/T2w models (0.77). Ensemble models integrating ADC, T1/T2w, and clinical data achieved the best AUC (0.85) with improved calibration. Net reclassification improvement (NRI) analyses demonstrated improved risk reclassification when ADC-derived information was incorporated into clinical and conventional MRI-based models. Conclusion ADC-derived radiomic features showed promising prognostic value for predicting renal function decline in ADPKD and provided incremental information beyond ht-TKV and current clinical standards when integrated with clinical and conventional MRI-based models, capturing both cystic and noncystic tissue alterations. If confirmed in independent cohorts, diffusion MRI-based radiomics may support earlier risk stratification and treatment decision-making in ADPKD.

In the era of precision medicine, increasing importance is given to machine learning (ML) applications. In breast cancer, advanced analyses, such as the radiomic process, characterise tumours and predict therapy responses. Breast magnetic resonance imaging (MRI) plays a key role in screening, staging, and treatment monitoring. Lesion segmentation on MRI is essential both to assess tumour growth and as a baseline for radiomic feature extraction. Manual segmentation is time-consuming and prone to inter-operator variability, limiting access to large labelled datasets and robust analyses. The use of ML for breast lesion segmentation on MRI has been investigated through a systematic review of PubMed, exploring studies published over the last 10 years. Results are compared in terms of performance, primarily using the Dice score. Early unsupervised methods achieved a mean Dice score of ∼0.75, surpassing traditional supervised methods (∼0.70). In contrast, deep learning (DL) approaches based on U-Net achieved higher average scores of 0.79. Further customised supervised DL approaches reached a mean Dice score of ∼0.83. However, there is still a gap in research on unsupervised DL techniques, which could help reduce bias and human variability. Future work may also explore multiparametric and multitechnique data, integrating more representative samples, including non-mass lesions.

IntroductionMagnetic resonance–guided focused ultrasound (MRgFUS) thalamotomy of the ventralis intermediate (Vim) nucleus is an “incisionless” treatment for medically refractory essential tremor (ET). We present data on 49 consecutive cases of MRgFUS Vim thalamotomy followed-up for 3 years and review the literature on studies with longer follow-up data.MethodsA retrospective chart review of patients who underwent MRgFUS thalamotomy (January 2018–December 2020) at our institution was performed. Clinical Rating Scale for Tremor (CRST) and Quality of Life in Essential Tremor (QUEST) scores were obtained pre-operatively and at each follow-up with an assessment of side effects. Patients had post-operative magnetic resonance imaging within 24 h and at 1 month to figure out lesion location, size, and extent. The results of studies with follow-up ≥3 years were summarized through a literature review.ResultsThe CRST total (baseline: 58.6 ± 17.1, 3-year: 40.8 ± 18.0) and subscale scores (A + B, baseline: 23.5 ± 6.3, 3-year: 12.8 ± 7.9; C, baseline: 12.7 ± 4.3, 3-year: 5.8 ± 3.9) and the QUEST score (baseline: 38.0 ± 14.8, 3-year: 18.7 ± 13.3) showed significant improvement that was stable during the 3-year follow-up. Three patients reported tremor recurrence and two were satisfactorily retreated. Side effects were reported by 44% of patients (severe: 4%, mild and transient: 40%). The improvement in tremor and quality of life in our cohort was consistent with the literature.ConclusionWe confirmed the effectiveness and safety of MRgFUS Vim thalamotomy in medically refractory ET up to 3 years.

Purpose: The young working group of the Italian Association of Medical and Health Physics (AIFM) designed a survey to assess the current situation of the under 35 AIFM members. Methods: An online survey including 65 questions was designed to gather personal information, educational issues, working and research experience, and to evaluate the AIFM activities. The survey was distributed to the under 35 members between November 2022 and February 2023, through the young AIFM mailing list and social media. Results: 160 answers from 230 affiliates (70%, 31 years median age) were obtained. The results highlighted that 87% of the respondents had a fixed term/permanent employment, mainly in public hospitals (58%). Regarding Medical Physicists (MPs) training, 54% of the students left their region of origin due to the training plan (40%) and the availability of scholarships (25%) in the chosen university. Most of the respondents have no Radiation Protection Expert title, while the remaining 20%, 6%, and 3% are qualified to the first, second, and third level, respectively. Several young MPs (62.2%) were involved in research activities; however, only 28% had teaching experience, mainly within their workplace (20%, safety courses), during AIFM courses (4%), or university lectures (3%). Conclusions: This survey reported the current situation of the under 35 AIFM members, highlighting the "brain drain" phenomenon from the south to the north of Italy, mainly due to the lack of post-graduate schools, scholarships, and job opportunities. The obtained results will help the future working program of the AIFM.

Artificial intelligence (AI) is a fast-moving technology that enables machines to perform tasks that could previously be done only by humans. The current debate is now whether machines will outperform humans, and therefore substitute them in critical tasks. In this paper, an attempt will be made to identify the most used AI techniques in diagnostic imaging, providing examples and identifying potential pitfalls.

A large GTV size, coupled with an incomplete (<70%) covering of GTV from the SIB, highly increases the risk of LF: 62% actuarial probability in the HiR group vs 7% in the LR/IR groups. Full coverage of the GTV with SIB would be of clinical relevance for pts with large macroscopic tumors. The Geometric Score could be used to select pts that would effectively benefit from online tumor tracking (e.g., with an MRI-Linac), allowing a reduction of the SIP volume and a consequent decrease in the amount of GTV left uncovered by the SIB.

The aim of this study was to compare Orthopantomograms (OPT) and Computed Tomography (CT) with Cone Beam Computed Tomography (CBCT) in patients with Medication-Related OsteoNecrosis of the Jaws (MRONJ). The study included 25 patients (6 males and 19 females) with MRONJ who had a history of long-term bisphosphonate therapy or one of the recently re-entered MRONJ drugs and underwent OPT, CT and/or CBCT for determination of the extent of disease. We excluded patients with maxillary neoplasia. Considering the presence of early and late signs, OPT was diagnostic in 6 out of 17 cases (35%), while CT and CBCT were diagnostic in 25 out of 25 cases (100%). Analysing the different radiant doses delivered by the selected radiological methods on a phantom, it was found that a more significant effective dose was spread by CT (2.6 mSv) than CBCT (0.164 mSv) or OPT (0.02 mSv). CBCT, from our experience, is a candidate to replace OPT in the first diagnostic step in patients with suspected MRONJ, generating less effective doses and artefacts from metal components than CT.

As opposed to external beam radiation therapy (EBRT), treatment planning systems (TPS) dedicated to intraoperative radiation therapy (IORT) were not subject to radical modifications in the last two decades. However, new treatment regimens such as ultrahigh dose rates and combination with multiple treatment modalities, as well as the prospected availability of dedicated in-room imaging, call for important new features in the next generation of treatment planning systems in IORT. Dosimetric accuracy should be guaranteed by means of advanced dose calculation algorithms, capable of modelling complex scattering phenomena and accounting for the non-tissue equivalent materials used to shape and compensate electron beams. Kilovoltage X-ray based IORT also presents special needs, including the correct description of extremely steep dose gradients and the accurate simulation of applicators. TPSs dedicated to IORT should also allow real-time imaging to be used for treatment adaptation at the time of irradiation. Other features implemented in TPSs should include deformable registration and capability of radiobiological planning, especially if unconventional irradiation schemes are used. Finally, patient safety requires that the multiple features be integrated in a comprehensive system in order to facilitate control of the whole process.

In the artificial intelligence era, machine learning (ML) techniques have gained more and more importance in the advanced analysis of medical images in several fields of modern medicine. Radiomics extracts a huge number of medical imaging features revealing key components of tumor phenotype that can be linked to genomic pathways. The multi-dimensional nature of radiomics requires highly accurate and reliable machine-learning methods to create predictive models for classification or therapy response assessment. Multi-parametric breast magnetic resonance imaging (MRI) is routinely used for dense breast imaging as well for screening in high-risk patients and has shown its potential to improve clinical diagnosis of breast cancer. For this reason, the application of ML techniques to breast MRI, in particular to multi-parametric imaging, is rapidly expanding and enhancing both diagnostic and prognostic power. In this review we will focus on the recent literature related to the use of ML in multi-parametric breast MRI for tumor classification and differentiation of molecular subtypes. Indeed, at present, different models and approaches have been employed for this task, requiring a detailed description of the advantages and drawbacks of each technique and a general overview of their performances.

BACKGROUND Surgical resection after neoadjuvant treatment is the main driver for improved survival in locally advanced pancreatic cancer (LAPC). However, the diagnostic performance of computed tomography (CT) imaging to evaluate the residual tumour burden at restaging after neoadjuvant therapy is low due to the difficulty in distinguishing neoplastic tissue from fibrous scar or inflammation. In this context, radiomics has gained popularity over conventional imaging as a complementary clinical tool capable of providing additional, unprecedented information regarding the intratumor heterogeneity and the residual neoplastic tissue, potentially serving in the therapeutic decision-making process. AIM To assess the capability of radiomic features to predict surgical resection in LAPC treated with neoadjuvant chemotherapy and radiotherapy. METHODS Patients with LAPC treated with intensive chemotherapy followed by ablative radiation therapy were retrospectively reviewed. One thousand six hundred and fifty-five radiomic features were extracted from planning CT inside the gross tumour volume. Both extracted features and clinical data contribute to create and validate the predictive model of resectability status. Patients were repeatedly divided into training and validation sets. The discriminating performance of each model, obtained applying a LASSO regression analysis, was assessed with the area under the receiver operating characteristic curve (AUC). The validated model was applied to the entire dataset to obtain the most significant features. RESULTS Seventy-one patients were included in the analysis. Median age was 65 years and 57.8% of patients were male. All patients underwent induction chemotherapy followed by ablative radiotherapy, and 19 (26.8%) ultimately received surgical resection. After the first step of variable selections, a predictive model of resectability was developed with a median AUC for training and validation sets of 0.862 (95%CI: 0.792-0.921) and 0.853 (95%CI: 0.706-0.960), respectively. The validated model was applied to the entire dataset and 4 features were selected to build the model with predictive performance as measured using AUC of 0.944 (95%CI: 0.892-0.996). CONCLUSION The present radiomic model could help predict resectability in LAPC after neoadjuvant chemotherapy and radiotherapy, potentially integrating clinical and morphological parameters in predicting surgical resection.

METHODS AND MATERIALS:From July 2006 to December 2015, 295 patients suitable for breast-conserving therapy entered a single-arm phase II study and were treated with IOERT as radical treatment. Inclusion criteria were age >50, postmenopausal status, cT1N0M0 stage, grade G1-G2, positive estrogen receptor status; unicentric and unifocal disease, histologically proven invasive ductal carcinoma no previous breast irradiation, good performance status.RESULTS:With a median follow-up of 7.1 years (95% CI, 6.5;7.4) 6 women (2.0%) experienced a true local recurrence (reappearance of the tumour in the same quadrant). Five-year overall survival and local recurrence-free survival were 96% (95% CI, 92.9;97.8) and 94.9% (95% CI, 91.6;97.0) respectively.CONCLUSION:Our trial suggests that, in highly selected early stage breast cancers, a single-dose IOERT can be safely delivered with excellent results and very low long-term recurrence rates.