La radiologia medica最新文献

{"title":"Cryoablation for the treatment of breast cancer: immunological implications and future perspectives. Utopia or reality?","authors":"","doi":"10.1007/s11547-024-01769-z","DOIUrl":"https://doi.org/10.1007/s11547-024-01769-z","url":null,"abstract":"<h3>Abstract</h3> <p>Cryoablation is a minimally invasive technique currently employed in breast cancer care, that uses freeze and thaw cycles to treat benign breast lesions, small breast cancers or focal sites of metastatic disease in patients not eligible for surgery. The final goal of this procedure is to destroy breast cancer cells using extreme cold. In addition, several studies have shown that this technique seems to have an enhancing effect on the immune response, especially by increasing the expression of tumor neoantigens specific to tumor cells, which are then attacked and destroyed. Exploiting this effect, cryoablation in combination with immunotherapy could be the key to treating early-stage breast cancers or patients who are unsuitable for surgery. According to some recent studies, there are other potential tools that could be used to enhance the therapeutic effect of cryoablation, such as FE3O4 nanoparticles or the manipulation of aquaporin expression. The aim of this narrative review is to summarize the current evidence regarding the use, indications, advantages and disadvantages of cryoablation in the treatment of breast cancer.</p>","PeriodicalId":501689,"journal":{"name":"La radiologia medica","volume":"40 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139646513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LNAS: a clinically applicable deep-learning system for mediastinal enlarged lymph nodes segmentation and station mapping without regard to the pathogenesis using unenhanced CT images","authors":"","doi":"10.1007/s11547-023-01747-x","DOIUrl":"https://doi.org/10.1007/s11547-023-01747-x","url":null,"abstract":"<h3>Abstract</h3> <span> <h3>Background</h3> <p>The accurate identification and evaluation of lymph nodes by CT images is of great significance for disease diagnosis, treatment, and prognosis.</p> </span> <span> <h3>Purpose</h3> <p>To assess the lymph nodes’ segmentation, size, and station by artificial intelligence (AI) for unenhanced chest CT images and evaluate its value in clinical scenarios.</p> </span> <span> <h3>Material and methods</h3> <p>This retrospective study proposed an end-to-end Lymph Nodes Analysis System (LNAS) consisting of three models: the Lymph Node Segmentation model (LNS), the Mediastinal Organ Segmentation model (MOS), and the Lymph Node Station Registration model (LNR). We selected a healthy chest CT image as the template image and annotated 14 lymph node station masks according to the IASLC to build the lymph node station mapping template. The exact contours and stations of the lymph nodes were annotated by two junior radiologists and reviewed by a senior radiologist. Patients aged 18 and above, who had undergone unenhanced chest CT and had at least one suspicious enlarged mediastinal lymph node in imaging reports, were included. Exclusions were patients who had thoracic surgeries in the past 2 weeks or artifacts on CT images affecting lymph node observation by radiologists. The system was trained on 6725 consecutive chest CTs that from Tianjin Medical University General Hospital, among which 6249 patients had suspicious enlarged mediastinal lymph nodes. A total of 519 consecutive chest CTs from Qilu Hospital of Shandong University (Qingdao) were used for external validation. The gold standard for each CT was determined by two radiologists and reviewed by one senior radiologist.</p> </span> <span> <h3>Results</h3> <p>The patient-level sensitivity of the LNAS system reached of 93.94% and 92.89% in internal and external test dataset, respectively. And the lesion-level sensitivity (recall) reached 89.48% and 85.97% in internal and external test dataset. For man–machine comparison, AI significantly apparently shortened the average reading time (<em>p</em> &lt; 0.001) and had better lesion-level and patient-level sensitivities.</p> </span> <span> <h3>Conclusion</h3> <p>AI improved the sensitivity lymph node segmentation by radiologists with an advantage in reading time.</p> </span>","PeriodicalId":501689,"journal":{"name":"La radiologia medica","volume":"108 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138715279","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Residual networks models detection of atrial septal defect from chest radiographs","authors":"Gang Luo, Zhixin Li, Wen Ge, Zhixian Ji, Sibo Qiao, Silin Pan","doi":"10.1007/s11547-023-01744-0","DOIUrl":"https://doi.org/10.1007/s11547-023-01744-0","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Object</h3><p>The purpose of this study was to explore a machine learning-based residual networks (ResNets) model to detect atrial septal defect (ASD) on chest radiographs.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>This retrospective study included chest radiographs consecutively collected at our hospital from June 2017 to May 2022. Qualified chest radiographs were obtained from patients who had finished echocardiography. These chest radiographs were labeled as positive or negative for ASD based on the echocardiographic reports and were divided into training, validation, and test dataset. Six ResNets models were employed to examine and compare by using the training dataset and was tuned using the validation dataset. The area under the curve, recall, precision and F1-score were taken as the evaluation metrics for classification result in the test dataset. Visualizing regions of interest for the ResNets models using heat maps.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>This study included a total of 2105 chest radiographs of children with ASD (mean age 4.14 ± 2.73 years, 54% male), patients were randomly assigned to training, validation, and test dataset with an 8:1:1 ratio. Healthy children’s images were supplemented to three datasets in a 1:1 ratio with ASD patients. Following the training, ResNet-10t and ResNet-18D have a better estimation performance, with precision, recall, accuracy, F1-score, and the area under the curve being (0.92, 0.93), (0.91, 0.91), (0.90, 0.90), (0.91, 0.91) and (0.97, 0.96), respectively. Compared to ResNet-18D, ResNet-10t was more focused on the distribution of the heat map of the interest region for most chest radiographs from ASD patients.</p><h3 data-test=\"abstract-sub-heading\">Conclusion</h3><p>The ResNets model is feasible for identifying ASD through children’s chest radiographs. ResNet-10t stands out as the preferable estimation model, providing exceptional performance and clear interpretability.</p>","PeriodicalId":501689,"journal":{"name":"La radiologia medica","volume":"18 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138633238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The role of an artificial intelligence software in clinical senology: a mammography multi-reader study","authors":"Enrica Bassi, Anna Russo, Eugenio Oliboni, Federico Zamboni, Cecilia De Santis, Giancarlo Mansueto, Stefania Montemezzi, Giovanni Foti","doi":"10.1007/s11547-023-01751-1","DOIUrl":"https://doi.org/10.1007/s11547-023-01751-1","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Purpose</h3><p>To evaluate the diagnostic role of a dedicated AI software in detecting anomalous breast findings on mammography and tomosynthesis images in the clinical setting, stand-alone and as aid of four readers.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>A total of 210 patients with complete clinical and radiologic records were retrospectively analyzed. Pathology was used as the reference standard for patients undergoing surgery or biopsy, and a 1-year follow-up was used to confirm no change in the remaining patients.</p><p>The image evaluation was performed by four readers with different levels of experience (a junior and three senior breast radiologists) using a 5-point Likert scale moving from 1 (definitively no cancer) to 5 (definitively cancer).</p><p>The positivity of mammograms was assessed on the presence of any breast lesion (masses, architectural distortions, asymmetries, calcifications), including malignant and benign ones. A multi-reader multi-case analysis was performed. A <i>p</i> value &lt; 0.05 was considered statistically significant.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>The stand-alone AI system achieved an accuracy of 71% (69% sensitivity and 73% specificity), which is overall lower than the value achieved by readers without AI. However, with the aid of AI, a significant increase of accuracy (<i>p</i> value = 0.004) and specificity (<i>p</i> value = 0.04) was achieved for the less experienced radiologist and a senior one.</p><h3 data-test=\"abstract-sub-heading\">Conclusion</h3><p>The use of AI software as a second reader for breast lesions assessment could play a crucial role in the clinical setting, by increasing sensitivity and specificity, especially for less experienced radiologists.</p>","PeriodicalId":501689,"journal":{"name":"La radiologia medica","volume":"84 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138633236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}