Artificial Intelligence in Lung Cancer Imaging: From Data to Therapy.

Lung cancer remains a global health challenge, leading to substantial morbidity and mortality. While prevention and early detection strategies have improved, the need for precise diagnosis, prognosis, and treatment remains crucial. In this comprehensive review article, we explore the role of artificial intelligence (AI) in reshaping the management of lung cancer. AI may have different potential applications in lung cancer characterization and outcome prediction. Manual segmentation is a time-consuming task, with high inter-observer variability, that can be replaced by AI-based approaches, including deep learning models such as U-Net, BCDU-Net, and others, to quantify lung nodules and cancers objectively and to extract radiomics features for the characterization of the tissue. AI models have also demonstrated their ability to predict treatment responses, such as immunotherapy and targeted therapy, by integrating radiomic features with clinical data. Additionally, AI-based prognostic models have been developed to identify patients at higher risk and personalize treatment strategies. In conclusion, this review article provides a comprehensive overview of the current state of AI applications in lung cancer management, spanning from segmentation and virtual biopsy to outcome prediction. The evolving role of AI in improving the precision and effectiveness of lung cancer diagnosis and treatment underscores its potential to significantly impact clinical practice and patient outcomes.

Adoption of AI in Oncological Imaging: Ethical, Regulatory, and Medical-Legal Challenges.

Artificial Intelligence (AI) algorithms have shown great promise in oncological imaging, outperforming or matching radiologists in retrospective studies, signifying their potential for advanced screening capabilities. These AI tools offer valuable support to radiologists, assisting them in critical tasks such as prioritizing reporting, early cancer detection, and precise measurements, thereby bolstering clinical decision-making. With the healthcare landscape witnessing a surge in imaging requests and a decline in available radiologists, the integration of AI has become increasingly appealing. By streamlining workflow efficiency and enhancing patient care, AI presents a transformative solution to the challenges faced by oncological imaging practices. Nevertheless, successful AI integration necessitates navigating various ethical, regulatory, and medical-legal challenges. This review endeavors to provide a comprehensive overview of these obstacles, aiming to foster a responsible and effective implementation of AI in oncological imaging.

Radiomics and Artificial Intelligence in Renal Lesion Assessment.

Radiomics, the extraction and analysis of quantitative features from medical images, has emerged as a promising field in radiology with the potential to revolutionize the diagnosis and management of renal lesions. This comprehensive review explores the radiomics workflow, including image acquisition, feature extraction, selection, and classification, and highlights its application in differentiating between benign and malignant renal lesions. The integration of radiomics with artificial intelligence (AI) techniques, such as machine learning and deep learning, can help patients' management and allow the planning of the appropriate treatments. AI models have shown remarkable accuracy in predicting tumor aggressiveness, treatment response, and patient outcomes. This review provides insights into the current state of radiomics and AI in renal lesion assessment and outlines future directions for research in this rapidly evolving field.

18 F-FET PET/CT in Calcifying Pseudoneoplasm of the Neuraxis : First Case.

ABSTRACT: A 66-year-old man has been treated in a psychiatric department for 4-5 years for a depressive syndrome, which is associated with poor motor initiative, confusional state, and dysosmia. Dynamic 18 F-FET PET/CT showed only faint uptake of radiotracer just above the background on the left frontal calcific lesion. The time-activity curve of the neoplasms showed a descending pattern. After a left fronto-orbitary minicraniotomy surgery, histology examination concluded for a rare calcifying pseudoneoplasm of the neuraxis (CAPNON). To our knowledge, no data are available on the metabolic behavior of CAPNON in 18 F-FET PET/CT. This case highlighted that a faint uptake and descending pattern on dynamic 18 F-FET PET/CT may be helpful in suspected CAPNON before surgery.

Amplifying the Effects of Contrast Agents on Magnetic Resonance Images Using a Deep Learning Method Trained on Synthetic Data.

OBJECTIVES: Artificial intelligence (AI) methods can be applied to enhance contrast in diagnostic images beyond that attainable with the standard doses of contrast agents (CAs) normally used in the clinic, thus potentially increasing diagnostic power and sensitivity. Deep learning-based AI relies on training data sets, which should be sufficiently large and diverse to effectively adjust network parameters, avoid biases, and enable generalization of the outcome. However, large sets of diagnostic images acquired at doses of CA outside the standard-of-care are not commonly available. Here, we propose a method to generate synthetic data sets to train an "AI agent" designed to amplify the effects of CAs in magnetic resonance (MR) images. The method was fine-tuned and validated in a preclinical study in a murine model of brain glioma, and extended to a large, retrospective clinical human data set. MATERIALS AND METHODS: A physical model was applied to simulate different levels of MR contrast from a gadolinium-based CA. The simulated data were used to train a neural network that predicts image contrast at higher doses. A preclinical MR study at multiple CA doses in a rat model of glioma was performed to tune model parameters and to assess fidelity of the virtual contrast images against ground-truth MR and histological data. Two different scanners (3 T and 7 T, respectively) were used to assess the effects of field strength. The approach was then applied to a retrospective clinical study comprising 1990 examinations in patients affected by a variety of brain diseases, including glioma, multiple sclerosis, and metastatic cancer. Images were evaluated in terms of contrast-to-noise ratio and lesion-to-brain ratio, and qualitative scores. RESULTS: In the preclinical study, virtual double-dose images showed high degrees of similarity to experimental double-dose images for both peak signal-to-noise ratio and structural similarity index (29.49 dB and 0.914 dB at 7 T, respectively, and 31.32 dB and 0.942 dB at 3 T) and significant improvement over standard contrast dose (ie, 0.1 mmol Gd/kg) images at both field strengths. In the clinical study, contrast-to-noise ratio and lesion-to-brain ratio increased by an average 155% and 34% in virtual contrast images compared with standard-dose images. Blind scoring of AI-enhanced images by 2 neuroradiologists showed significantly better sensitivity to small brain lesions compared with standard-dose images (4.46/5 vs 3.51/5). CONCLUSIONS: Synthetic data generated by a physical model of contrast enhancement provided effective training for a deep learning model for contrast amplification. Contrast above that attainable at standard doses of gadolinium-based CA can be generated through this approach, with significant advantages in the detection of small low-enhancing brain lesions.

Development of Predictive Models for the Response of Vestibular Schwannoma Treated with Cyberknife®: A Feasibility Study Based on Radiomics and Machine Learning.

PURPOSE: to predict vestibular schwannoma (VS) response to radiosurgery by applying machine learning (ML) algorithms on radiomic features extracted from pre-treatment magnetic resonance (MR) images. METHODS: patients with VS treated with radiosurgery in two Centers from 2004 to 2016 were retrospectively evaluated. Brain T1-weighted contrast-enhanced MR images were acquired before and at 24 and 36 months after treatment. Clinical and treatment data were collected contextually. Treatment responses were assessed considering the VS volume variation based on pre- and post-radiosurgery MR images at both time points. Tumors were semi-automatically segmented and radiomic features were extracted. Four ML algorithms (Random Forest, Support Vector Machine, Neural Network, and extreme Gradient Boosting) were trained and tested for treatment response (i.e., increased or non-increased tumor volume) using nested cross-validation. For training, feature selection was performed using the Least Absolute Shrinkage and Selection Operator, and the selected features were used as input to separately build the four ML classification algorithms. To overcome class imbalance during training, Synthetic Minority Oversampling Technique was used. Finally, trained models were tested on the corresponding held out set of patients to evaluate balanced accuracy, sensitivity, and specificity. RESULTS: 108 patients treated with Cyberknife® were retrieved; an increased tumor volume was observed at 24 months in 12 patients, and at 36 months in another group of 12 patients. The Neural Network was the best predictive algorithm for response at 24 (balanced accuracy 73% ± 18%, specificity 85% ± 12%, sensitivity 60% ± 42%) and 36 months (balanced accuracy 65% ± 12%, specificity 83% ± 9%, sensitivity 47% ± 27%). CONCLUSIONS: radiomics may predict VS response to radiosurgery avoiding long-term follow-up as well as unnecessary treatment.

Lesion or Pseudolesion? A Comprehensive Description of Perfusion-Based Liver Alterations on Contrast-Enhanced Computed Tomography and Literature Review.

ABSTRACT: Pseudolesions on contrast-enhanced computed tomography represent a diagnostic challenge for radiologists because they could be difficult to distinguish from true space-occupying lesions. This article aims to provide a detailed overview of these entities based on radiological criteria (hyperattenuation or hypoattenuation, localization, morphology), as well as a brief review of the hepatic vascular anatomy and pathophysiological process. Relevant examples from hospital case series are reported as helpful hints to assist radiologists in recognizing and correctly diagnosing these abnormalities.

The Network That Unites a Qualitative Study on Clinical Psychological Intervention for Women with a History of Breast Cancer and Chronic Pain.

The aim of this qualitative research is to deepen the knowledge in the field of psycho-oncology and the consequences of chronic and persistent pain by listening to patients' experiences, their emotions and difficulties in facing this hard condition, and assessing their perception of the role of the psychologist in pain management. In this qualitative study, a semistructured interview was used, designed from three research questions: chronic pain and quality of life; chronic pain and psychological well-being; and the role and perception of the psychologist in pain management. The sample consists of 29 women who suffered or have recovered from breast carcinoma, and who currently report having chronic pain due either to the presence of the cancer or as a result of surgery or treatment. Three themes emerged from the thematic analysis: quality of life and psychological well-being, relational well-being, and perception and role of the psychologist. Two subthemes have been identified for each theme: common features of chronic pain and consequences and resilience for the first theme; not feeling understood and willingness to protect loved ones for the second theme; and improvements perceived by users and reasons for not making use of the service for the last theme. In conclusion, the results obtained from the literature and those from the analysis of the interviews are discussed and compared, and reflections are made on possible future implications.

Quantification of epicardial adipose tissue in obese patients using an open-bore MR scanner.

BACKGROUND: Our aim was to evaluate the reproducibility of epicardial adipose tissue (EAT) volume, measured on scans performed using an open-bore magnetic resonance scanner. METHODS: Consecutive patients referred for bariatric surgery, aged between 18 and 65 years who agreed to undergo cardiac imaging (MRI), were prospectively enrolled. All those with cardiac pathology or contraindications to MRI were excluded. MRI was performed on a 1.0-T open-bore scanner, and EAT was segmented on all scans at both systolic and diastolic phase by two independent readers (R1 with four years of experience and R2 with one year). Data were reported as median and interquartile range; agreement and differences were appraised with Bland-Altman analyses and Wilcoxon tests, respectively. RESULTS: Fourteen patients, 11 females (79%) aged 44 (41-50) years, underwent cardiac MRI. For the first and second readings, respectively, EAT volume was 86 (78-95) cm3 and 85 (79-91) cm3 at systole and 82 (74-95) cm3 and 81 (75-94) cm3 at diastole for R1, and 89 (79-99) cm3 and 93 (84-98) cm3 at systole and 92 (85-103) cm3 and 93 (82-94) cm3 at diastole for R2. R1 had the best reproducibility at diastole (bias 0.3 cm3, standard deviation of the differences (SD) 3.3 cm3). R2 had the worst reproducibility at diastole (bias 3.9 cm3, SD 12.1 cm3). The only significant difference between systole and diastole was at the first reading by R1 (p = 0.016). The greatest bias was that of inter-reader reproducibility at diastole (-9.4 cm3). CONCLUSIONS: Reproducibility was within clinically acceptable limits in most instances.

Development and Validation of an AI-driven Mammographic Breast Density Classification Tool Based on Radiologist Consensus.

Mammographic breast density (BD) is commonly visually assessed using the Breast Imaging Reporting and Data System (BI-RADS) four-category scale. To overcome inter- and intraobserver variability of visual assessment, the authors retrospectively developed and externally validated a software for BD classification based on convolutional neural networks from mammograms obtained between 2017 and 2020. The tool was trained using the majority BD category determined by seven board-certified radiologists who independently visually assessed 760 mediolateral oblique (MLO) images in 380 women (mean age, 57 years ± 6 [SD]) from center 1; this process mimicked training from a consensus of several human readers. External validation of the model was performed by the three radiologists whose BD assessment was closest to the majority (consensus) of the initial seven on a dataset of 384 MLO images in 197 women (mean age, 56 years ± 13) obtained from center 2. The model achieved an accuracy of 89.3% in distinguishing BI-RADS a or b (nondense breasts) versus c or d (dense breasts) categories, with an agreement of 90.4% (178 of 197 mammograms) and a reliability of 0.807 (Cohen κ) compared with the mode of the three readers. This study demonstrates accuracy and reliability of a fully automated software for BD classification. Keywords: Mammography, Breast, Convolutional Neural Network (CNN), Deep Learning Algorithms, Machine Learning Algorithms Supplemental material is available for this article. © RSNA, 2022.

CT-derived pulmonary vascular metrics and clinical outcome in COVID-19 patients.

To assess pulmonary vascular metrics on chest CT of COVID-19 patients, and their correlation with pneumonia extent (PnE) and outcome, we analyzed COVID-19 patients with an available previous chest CT, excluding those performed for cardiovascular disease. From February 21 to March 21, 2020, of 672 suspected COVID-19 patients from two centers who underwent CT, 45 RT-PCR-positives (28 males, median age 75, IQR 66-81 years) with previous CTs performed a median 36 months before (IQR 12-72 months) were included. We assessed PnE, pulmonary artery (PA) diameter, ascending aorta (Ao) diameter, and PA/Ao ratio. Most common presentations were fever and dyspnea (15/45) and fever alone (13/45). Outcome was available for 41/45 patients, 15/41 dead and 26/41 discharged. Ground-glass opacities (GGOs) alone were found in 29/45 patients, GGOs with consolidations in 15/45, consolidations alone in 1/45. All but one patient had bilateral pneumonia, 9/45 minimal, 22/45 mild, 9/45 moderate, and 5/45 severe PnE. PA diameter (median 31 mm, IQR 28-33 mm) was larger than before (26 mm, IQR 25-29 mm) (P<0.001), PA/Ao ratio (median 0.83, IQR 0.76-0.92) was higher than before (0.76, IQR 0.72-0.82) (P<0.001). Patients with adverse outcome (death) had higher PA diameter (P=0.001), compared to discharged ones. Only weak correlations were found between ΔPA or ΔPA/Ao and PnE (ρ≤0.453, P≤0.032), with 4/45 cases with moderate-severe PnE and minimal increase in PA metrics. In conclusion, enlarged PA diameter was associated to death in COVID-19 patients, a finding deserving further investigation as a potential driver of therapy decision-making.

Assessment of myocardial extracellular volume on body computed tomography in breast cancer patients treated with anthracyclines.

BACKGROUND: Cancer treatment with anthracyclines may lead to an increased incidence of cardiac disease due to cardiotoxicity, as they may cause irreversible myocardial fibrosis. So far, the proposed methods for screening patients for cardiotoxicity have led to only limited success, while the analysis of myocardial extracellular volume (mECV) at cardiac magnetic resonance (CMR) has shown promising results, albeit requiring a dedicated exam. Recent studies have found strong correlations between mECV values obtained through computed tomography (CT), and those derived from CMR. Thus, our purpose was to evaluate the feasibility of estimating mECV on thoracic contrast-enhanced CT performed for staging or follow-up in breast cancer patients treated with anthracyclines, and, if feasible, to assess if a rise in mECV is associated with chemotherapy, and persistent over time. METHODS: After ethics committee approval, female patients with breast cancer who had undergone at least 2 staging or follow-up CT examinations at our institution, one before and one shortly after the end of chemotherapy including anthracyclines were retrospectively evaluated. Patients without available haematocrit, with artefacts in CT images, or who had undergone radiation therapy of the left breast were excluded. Follow-up CT examinations at longer time intervals were also analysed, when available. mECV was calculated on scans obtained at 1, and 7 min after contrast injection. RESULTS: Thirty-two female patients (aged 57±13 years) with pre-treatment haematocrit 38%±4%, and ejection fraction 64%±6% were analysed. Pre-treatment mECV was 27.0%±2.9% at 1 min, and 26.4%±3.8% at 7 min, similar to values reported for normal subjects in the literature. Post-treatment mECV (median interval: 89 days after treatment) was 31.1%±4.9%, and 30.0%±5.1%, respectively, values significantly higher than pre-treatment values at all times (P<0.005). mECV at follow-up (median interval: 135 days after post-treatment CT) was 31.0%±4.5%, and 27.7%±3.7%, respectively, without significant differences (P>0.548) when compared to post-treatment values. CONCLUSIONS: mECV values from contrast-enhanced CT scans could play a role in the assessment of myocardial condition in breast cancer patients undergoing anthracycline-based chemotherapy. CT-derived ECV could be an imaging biomarker for the monitoring of therapy-related cardiotoxicity, allowing for potential secondary prevention of cardiac damage, using data derived from an examination that could be already part of patients' clinical workflow.

Image quality of hip MR arthrography with intra-articular injection of hyaluronic acid versus gadolinium-based contrast agent in patients with femoroacetabular impingement.

OBJECTIVE: To compare image quality of magnetic resonance arthrography (MRA) of the hip with intra-articular injection of high-viscosity hyaluronic acid (HA-MRA) versus Gd-based contrast agent (Gd-MRA) in patients with femoroacetabular impingement (FAI). MATERIALS AND METHODS: Design: single-centre, observational, retrospective, inter-individual, and cross-sectional. FAI patients who underwent HA-MRA (3 mL of high-viscosity HA plus 17 mL of saline) were compared with 37 age- and sex-matched FAI patients who underwent Gd-MRA (20 mL of 2 mmol/L solution of gadopentetate dimeglumine). Two independent blinded radiologists assessed image quality for all sequences (two-dimensional proton density, non-fat-sat axial, fat-sat coronal and sagittal; three-dimensional dual-echo steady state), using a 5-point Likert scale considering separately labrum, cartilage, round ligament, transverse ligament, and capsule. Pearson χ2 and Cohen κ were used. RESULTS: The HA-MRA group was composed of 37 patients (23 males, 14 females; median age 38 years), the Gd-MRA group of 37 patients (21 males, 16 females; median age 38 years), without significant difference for age (p = 0.937) and sex (p = 0.636). Image quality did not differ between the two readers for any structure: labrum (p ≥ 0.340), cartilage (p ≥ 0.198), round ligament (p ≥ 0.255), transverse ligament (p ≥ 0.806), and capsule (p ≥ 0.314). Inter-reader agreement (κ) ranged from 0.785 to 1.000. CONCLUSIONS: HA-MRA provided an image quality not significantly different from that of Gd-MRA. This may open the possibility of combining MRA and viscosupplementation in one single procedure.

A machine learning approach for differentiating malignant from benign enhancing foci on breast MRI.

BACKGROUND: Differentiate malignant from benign enhancing foci on breast magnetic resonance imaging (MRI) through radiomic signature. METHODS: Forty-five enhancing foci in 45 patients were included in this retrospective study, with needle biopsy or imaging follow-up serving as a reference standard. There were 12 malignant and 33 benign lesions. Eight benign lesions confirmed by over 5-year negative follow-up and 15 malignant histopathologically confirmed lesions were added to the dataset to provide reference cases to the machine learning analysis. All MRI examinations were performed with a 1.5-T scanner. One three-dimensional T1-weighted unenhanced sequence was acquired, followed by four dynamic sequences after intravenous injection of 0.1 mmol/kg of gadobenate dimeglumine. Enhancing foci were segmented by an expert breast radiologist, over 200 radiomic features were extracted, and an evolutionary machine learning method ("training with input selection and testing") was applied. For each classifier, sensitivity, specificity and accuracy were calculated as point estimates and 95% confidence intervals (CIs). RESULTS: A k-nearest neighbour classifier based on 35 selected features was identified as the best performing machine learning approach. Considering both the 45 enhancing foci and the 23 additional cases, this classifier showed a sensitivity of 27/27 (100%, 95% CI 87-100%), a specificity of 37/41 (90%, 95% CI 77-97%), and an accuracy of 64/68 (94%, 95% CI 86-98%). CONCLUSION: This preliminary study showed the feasibility of a radiomic approach for the characterisation of enhancing foci on breast MRI.

Blood-threshold CMR volume analysis of functional univentricular heart.

PURPOSE: To validate a blood-threshold (BT) segmentation software for cardiac magnetic resonance (CMR) cine images in patients with functional univentricular heart (FUH). MATERIALS AND METHODS: We evaluated retrospectively 44 FUH patients aged 25 ± 8 years (mean ± standard deviation). For each patient, the epicardial contour of the single ventricle was manually segmented on cine images by two readers and an automated BT algorithm was independently applied to calculate end-diastolic volume (EDV), end-systolic volume (ESV), stroke volume (SV), ejection fraction (EF), and cardiac mass (CM). Aortic flow analysis (AFA) was performed on through-plane images to obtain forward volumes and used as a benchmark. Reproducibility was tested in a subgroup of 24 randomly selected patients. Wilcoxon, Spearman, and Bland-Altman statistics were used. RESULTS: No significant difference was found between SV (median 57.7 ml; interquartile range 47.9-75.6) and aortic forward flow (57.4 ml; 48.9-80.4) (p = 0.123), with a high correlation (r = 0.789, p < 0.001). Intra-reader reproducibility was 86% for SV segmentation, and 96% for AFA. Inter-reader reproducibility was 85 and 96%, respectively. CONCLUSION: The BT segmentation provided an accurate and reproducible assessment of heart function in FUH patients.

Phase II Study of Everolimus in Patients With Thymoma and Thymic Carcinoma Previously Treated With Cisplatin-Based Chemotherapy.

Purpose No effective salvage treatments are available for patients with advanced/recurrent thymoma (T) or thymic carcinoma (TC) who have progressed after platinum-based chemotherapy. This study evaluated the activity of everolimus in patients with advanced/recurrent T or TC previously treated with cisplatin-containing chemotherapy. Patients and Methods This was a single-arm, single-stage, open-label, multicenter, phase II trial. Patients received oral everolimus 10 mg/d until disease progression, unacceptable toxicity, or patient refusal. A Fleming phase II trial was designed. The null hypothesis of a true disease control rate (DCR) of 40% was tested against a one-sided alternative of a true DCR of 60% (α = ß = 0.10): If disease control were achieved in ≥ 21 of the first 41 evaluable patients, everolimus could be recommended for further evaluation. Progression-free survival, overall survival, and safety were also evaluated. Results From 2011 to 2013, 51 patients were enrolled (T, n = 32; TC, n = 19). Complete remission was observed in one patient with TC, partial response in five patients (T, n = 3; TC, n = 2), and stable disease in 38 patients (T, n = 27; TC, n= 11), with a DCR of 88% (T,: 93.8%; TC, 77.8%). With a median follow up of 25.7 months, median progression-free survival was 10.1 months (T,: 16.6 months; TC, 5.6 months), and median overall survival was 25.7 months (T, not reached; TC, 14.7 months). Fourteen patients had a serious drug-related adverse event; of these patients, nine permanently discontinued treatment. Three patients died of pneumonitis while in the study. Immunohistochemical positivity for p4E-BP1 or insulin-like growth factor-1 receptor was statistically significantly related to a shorter survival. Conclusion Everolimus may induce durable disease control in a high percentage of patients with T or TC, albeit with a potential high risk of fatal pneumonitis.

Carbon dioxide (CO2) angiography as an option for endovascular abdominal aortic aneurysm repair (EVAR) in patients with chronic kidney disease (CKD).

To assess feasibility, efficacy and safety of carbon dioxide (CO2) digital subtraction angiography (DSA) to guide endovascular aneurysm repair (EVAR) in a cohort of patients with chronic kidney disease (CKD). After Ethical Committee approval, the records of 13 patients (all male, mean age 74.6 ± 8.0 years) with CKD, who underwent EVAR to exclude an abdominal aortic aneurysm (AAA) under CO2 angiography guidance, were reviewed. The AAA to be excluded had a mean diameter of 52.0 ± 8.0 mm. CO2 angiography was performed by automatic (n = 7) or hand (n = 6) injection. The endograft was correctly placed and the AAA was excluded in all cases, without any surgical conversions. Two patients (15.4%) had an endoleak: one type-Ia, detected by CO2-DSA and effectively treated with prosthesis dilatation; one type-III, detected by CO2-DSA, confirmed using 10 ml of ICM, and conservatively managed. In one patient, CO2 angiograms were considered of too low quality for guiding the procedure and 200 ml of ICM were administered. Overall, 11 patients (84.6%) underwent a successful EVAR under the guidance of the sole CO2 angiography. No patients suffered from major complications, including those typically CO2-related. Two patients suffered from abdominal pain during the procedure secondary to a transient splanchnic perfusion's reduction due to CO2, and one patient had a worsening of renal function probably caused by a cholesterol embolization during the procedure. In patients with CKD, EVAR under CO2 angiography guidance is feasible, effective, and safe.

Technical success, technique efficacy and complications of minimally-invasive imaging-guided percutaneous ablation procedures of breast cancer: A systematic review and meta-analysis.

OBJECTIVES: To systematically review studies concerning imaging-guided minimally-invasive breast cancer treatments. METHODS: An online database search was performed for English-language articles evaluating percutaneous breast cancer ablation. Pooled data and 95% confidence intervals (CIs) were calculated. Technical success, technique efficacy, minor and major complications were analysed, including ablation technique subgroup analysis and effect of tumour size on outcome. RESULTS: Forty-five studies were analysed, including 1,156 patients and 1,168 lesions. Radiofrequency (n=577; 50%), microwaves (n=78; 7%), laser (n=227; 19%), cryoablation (n=156; 13%) and high-intensity focused ultrasound (HIFU, n=129; 11%) were used. Pooled technical success was 96% (95%CI 94-97%) [laser=98% (95-99%); HIFU=96% (90-98%); radiofrequency=96% (93-97%); cryoablation=95% (90-98%); microwave=93% (81-98%)]. Pooled technique efficacy was 75% (67-81%) [radiofrequency=82% (74-88); cryoablation=75% (51-90); laser=59% (35-79); HIFU=49% (26-74)]. Major complications pooled rate was 6% (4-8). Minor complications pooled rate was 8% (5-13%). Differences between techniques were not significant for technical success (p=0.449), major complications (p=0.181) or minor complications (p=0.762), but significant for technique efficacy (p=0.009). Tumour size did not impact on variables (p>0.142). CONCLUSIONS: Imaging-guided percutaneous ablation techniques of breast cancer have a high rate of technical success, while technique efficacy remains suboptimal. Complication rates are relatively low. KEY POINTS: • Imaging-guided ablation techniques for breast cancer are 96% technically successful. • Overall technique efficacy rate is 75% but largely inhomogeneous among studies. • Overall major and minor complication rates are low (6-8%).

1H- and 31P-myocardial magnetic resonance spectroscopy in non-obstructive hypertrophic cardiomyopathy patients and competitive athletes.

PURPOSE: The clinical differentiation between athlete's heart and mild forms of non-obstructive hypertrophic cardiomyopathy (HCM) is crucial. We hypothesized that differences do exist between the myocardial metabolism of patients with non-obstructive HCM and competitive athletes (CAs). Our aim was to evaluate myocardial metabolism with 31P-MRS and 1H-MRS in HCM patients and CAs. MATERIALS AND METHODS: After Ethics Committee approval, 15 CAs and 7 HCM patients were prospectively enrolled. They underwent a 1.5-T cardiac MR including electrocardiographically triggered cine images, single-voxel 1H-MRS and multivoxel 31P-MRS. 1H-MRS was performed after imaging using standard coil with the patient in the supine position; thereafter, 31P-MRS was performed using a dedicated coil, in the prone position. Data were reported as median and interquartile range. Mann-Whitney U test was used. RESULTS: In CAs, left ventricular mass index was 72 (66-83) g/m2, septal thickness 10 (10-11) mm, end diastolic volume index 95 (85-102) ml/m2, end systolic volume index 30 (28-32) ml/m2 and ejection fraction 68% (65-69%); in HCM patients, 81 (76-111) g/m2 (P = 0.052), 18 (15-21) mm (P = 0.003), 73 (58-76) ml/m2 (P = 0.029), 20 (16-34) ml/m2 (P = 0.274) and 68% (55-73%) (P = 1.000), respectively. At 1H-MRS, total lipids were 35 (0-183) arbitrary units (au) for CA and 763 (155-1994) au for HCM patients (P = 0.046). At 31P-MRS, PCr/γATP was 5 (4-6) au for CA and 4 (2-5) au for HCM patients (P = 0.230). Examination time was 20 min for imaging only, 5 min for 1H-MRS and 15 min for 31P-MRS. CONCLUSIONS: We observed a significant increase of myocardial lipids, but a preserved PCr/γATP ratio in the metabolism of HCM patients compared with competitive CAs.