Reducing Plastic Pollution by Recovery and Recycling: Evidence from a "Blue Economy" Project Impacting Policy-Making in Italy.

This paper recognizes the relevance of the Seabed Cleaning Project, created by the Angelo Vassallo Sindaco Pescatore Foundation in 2010, and presents it as a tool to achieve benefits in environmental, social, and economic terms, in line with the innovative framework of the Blue Economy. The project suggests a practical, feasible, and scalable solution to plastic pollution that can be obtained thanks to the activity performed by fishermen in cooperation with the municipality and local community (in a multi-level cooperation perspective). The data show a significant reduction of plastic on the seafloor thanks to the project, but further research is needed in order to collect further positive outcomes from different applications of the project. In 2022, the "Salva Mare" Law was approved in the Senate, extending the good practices proposed by the Foundation nationwide and showing how small gestures and concrete actions can provide significant improvements in pursuit of a healthier, safer, greener, and smarter city for the future.

A fully automatic deep learning algorithm to segment rectal Cancer on MR images: a multi-center study.

The aim of the study is to present and tune a fully automatic deep learning algorithm to segment colorectal cancers (CRC) on MR images, based on a U-Net structure. It is a multicenter study, including 3 different Italian institutions, that used 4 different MRI scanners. Two of them were used for training and tuning the systems, while the other two for the validation. The implemented algorithm consists of a pre-processing step to normalize and to highlight the tumoral area, followed by the CRC segmentation using different U-net structures. Automatic masks were compared with manual segmentations performed by three experienced radiologists, one at each center. The two best performing systems (called mdl2 and mdl3), obtained a median Dice Similarity Coefficient of 0.68(mdl2) - 0.69(mdl3), precision of 0.75(md/2) - 0.71(md/3), and recall of 0.69(mdl2) - 0.73(mdl3) on the validation set. Both systems reached high detection rates, 0.98 and 0.95, respectively, on the validation set. These encouraging results, if confirmed on larger dataset, might improve the management of patients with CRC, since it can be used as a fast and precise tool for further radiomics analyses. Clinical Relevance - To provide a reliable tool able to automatically segment CRC tumors that can be used as first step in future radiomics studies aimed at predicting response to chemotherapy and personalizing treatment.

Pancreas bifidum: an extremely rare cause of acute pancreatitis.

A wide spectrum of anomalies of the pancreas, the pancreatic ductal system and the biliary tree are commonly encountered at radiologic evaluation. Pancreas bifidum, also known as bifid pancreas or fish-tail pancreas, is an extremely rare congenital branching anomaly of the main pancreatic duct characterized by its duplication. These 2 separate ducts are laid from the pancreatic tail to neck and they generally join at the pancreas body-tail draining via the major papilla; the pancreatic parenchyma is also bifurcated with separated dorsal and caudal buds. The clinical impact of this condition is not well established: although some authors sustained that probably does not cause or contribute to abdominal pain or overt pancreatic diseases, others argued that could be considered as a possible cause of acute pancreatitis. We herewith describe the case of a 51-year-old woman presenting to our hospital with epigastric pain, nausea, and vomiting. Biochemical tests were suspicious for acute pancreatitis. Ultrasound examination was negative. MRI, including MR cholangiopancreatography revealed bifid pancreas characterized by duplication of the main pancreatic duct with 2 separate ducts that join at the pancreas head and draining via the minor papilla. On T2-weighted images the ventral bud of the pancreas was enlarged and characterized by slightly hyperintensity without peripancreatic fluid collections. The MRI findings were consistent with acute pancreatitis limited to the ventral bud of a bifid pancreas. Patient was treated with intravenous fluid resuscitation, pain control and institution of early enteral nutrition and discharged on the seventh day after admission.

MRI-based radiomics to predict response in locally advanced rectal cancer: comparison of manual and automatic segmentation on external validation in a multicentre study.

BACKGROUND: Pathological complete response after neoadjuvant chemoradiotherapy in locally advanced rectal cancer (LARC) is achieved in 15-30% of cases. Our aim was to implement and externally validate a magnetic resonance imaging (MRI)-based radiomics pipeline to predict response to treatment and to investigate the impact of manual and automatic segmentations on the radiomics models. METHODS: Ninety-five patients with stage II/III LARC who underwent multiparametric MRI before chemoradiotherapy and surgical treatment were enrolled from three institutions. Patients were classified as responders if tumour regression grade was 1 or 2 and nonresponders otherwise. Sixty-seven patients composed the construction dataset, while 28 the external validation. Tumour volumes were manually and automatically segmented using a U-net algorithm. Three approaches for feature selection were tested and combined with four machine learning classifiers. RESULTS: Using manual segmentation, the best result reached an accuracy of 68% on the validation set, with sensitivity 60%, specificity 77%, negative predictive value (NPV) 63%, and positive predictive value (PPV) 75%. The automatic segmentation achieved an accuracy of 75% on the validation set, with sensitivity 80%, specificity 69%, and both NPV and PPV 75%. Sensitivity and NPV on the validation set were significantly higher (p = 0.047) for the automatic versus manual segmentation. CONCLUSION: Our study showed that radiomics models can pave the way to help clinicians in the prediction of tumour response to chemoradiotherapy of LARC and to personalise per-patient treatment. The results from the external validation dataset are promising for further research into radiomics approaches using both manual and automatic segmentations.

Focal nodular hyperplasia after oxaliplatin-based chemotherapy: A diagnostic challenge.

Chemotherapy could induce benign liver alterations presenting as diffuse or focal lesions mimicking metastases. Oxaliplatin-induced vascular liver injury is described in literature, but the association with FNH-like lesions has been reported in a limited number of cases. We herewith describe the case of a 67-year-old male, who had laparoscopic right-sided hemicolectomy, 8 years ago, because of colonic adenocarcinoma (pT3N0M0) and subsequent adjuvant chemotherapy (capecitabine + oxaliplatin), who referred to the ultrasound service of our Radiology Unit because of abdominal pain. Five-years follow-up was negative for metastases. Ultrasound examination showed 2 small hypoechoic hepatic nodules, in segment VIII and VII, confirmed at CT, suspected for metastases. FDG-PET was negative, and blood tumor markers were within normal ranges. For further evaluation we performed gadoxetic acid (Gd-EOB-DTPA)-enhanced MRI that showed hyperintensity of the nodules in the hepatobiliary phase with central small hypointensity due to a central scar. Considering the previous oxaliplatin-based chemotherapy the findings were compatible with FNHlike lesions and the diagnostic suspicion was confirmed at ultrasound-guided core needle biopsy. Knowledge of the possible occurrence of FNH-like lesions in oncologic setting, along with the detection of typical MRI appearance, is important for appropriate management and may avoid unnecessary biopsy or surgery and reduce patients' anxiety.

Bilateral breast metastases as the first manifestation of an occult pancreatic neuroendocrine tumor.

Breast metastases are uncommon findings compared to primary breast cancer and in particular bilateral secondary breast lesions from neuroendocrine tumor (NET)s are extremely rare with just less over 13 cases described in literature. We reported herewith the case of a 54-year-old woman who presented to our Breast Unit after noticing multiple, mobile, bilateral breast lumps. Imaging studies confirmed the presence of multiple, circumscribed, bilateral breast masses with slightly spiculated margins, classified as suspicious for malignancy (BI-RADS 4). A tru-cut biopsy was carried out on the largest lesion of each side and histopathologic and immunohistochemistry examination was consistent with metastases from pancreatic neuroendocrine tumor (PNET). Total-body CT revealed the presence of a mass located in the pancreatic body - tail with associated abdominal lymphadenopathies and multiple secondary nodules in bilateral breast and in the liver. Stage IV disease was diagnosed, patient did not undergo surgery and started LAR - octreotide therapy. Although rare, breast metastases from NETs represent an important diagnostic challenge for practitioners because of the difficulty to differentiate from a primary breast carcinoma or even from benign breast lesions. Clinicians should be aware of the possibility of bilateral breast metastases in differential diagnosis of breast lesions in order to ensure the correct diagnosis and the most appropriate management of these patients.

A rare case of adrenal ganglioneuroblastoma-intermixed in an adult and a review of literature.

Peripheral neuroblastic tumors are extremely rare in the adult with less just over 20 cases involving adrenal gland described in the literature. We reported herewith the case of a 22-year-old young male who presented with epigastric pain and diarrhea. Imaging studies documented a 3.5cm x 3cm x 4cm solid well-circumscribed right adrenal mass, of heterogeneous structure and with fine calcifications. The lesion turned negative at MIBG scintigraphy. A right robotic-assisted adrenalectomy was performed leading to complete excision of the lesion without complications. Histology was consistent with intermixed stroma-rich ganglioneuroblastoma. A wait-and-see strategy was considered adequate. Two years after diagnosis patient is alive disease-free. Although the definitive diagnosis of a peripheral neuroblastic tumor is obtained after histopathological analysis, CT, and MRI are helpful to further characterize masses and useful in pretreatment risk stratification. Clinicians should be aware of the possibility of GNB development in adult population and its malignant potential.

Computer-Aided Diagnosis Improves the Detection of Clinically Significant Prostate Cancer on Multiparametric-MRI: A Multi-Observer Performance Study Involving Inexperienced Readers.

Recently, Computer Aided Diagnosis (CAD) systems have been proposed to help radiologists in detecting and characterizing Prostate Cancer (PCa). However, few studies evaluated the performances of these systems in a clinical setting, especially when used by non-experienced readers. The main aim of this study is to assess the diagnostic performance of non-experienced readers when reporting assisted by the likelihood map generated by a CAD system, and to compare the results with the unassisted interpretation. Three resident radiologists were asked to review multiparametric-MRI of patients with and without PCa, both unassisted and assisted by a CAD system. In both reading sessions, residents recorded all positive cases, and sensitivity, specificity, negative and positive predictive values were computed and compared. The dataset comprised 90 patients (45 with at least one clinically significant biopsy-confirmed PCa). Sensitivity significantly increased in the CAD assisted mode for patients with at least one clinically significant lesion (GS > 6) (68.7% vs. 78.1%, p = 0.018). Overall specificity was not statistically different between unassisted and assisted sessions (94.8% vs. 89.6, p = 0.072). The use of the CAD system significantly increases the per-patient sensitivity of inexperienced readers in the detection of clinically significant PCa, without negatively affecting specificity, while significantly reducing overall reporting time.

Impact of inter-reader contouring variability on textural radiomics of colorectal liver metastases.

BACKGROUND: Radiomics is expected to improve the management of metastatic colorectal cancer (CRC). We aimed at evaluating the impact of liver lesion contouring as a source of variability on radiomic features (RFs). METHODS: After Ethics Committee approval, 70 liver metastases in 17 CRC patients were segmented on contrast-enhanced computed tomography scans by two residents and checked by experienced radiologists. RFs from grey level co-occurrence and run length matrices were extracted from three-dimensional (3D) regions of interest (ROIs) and the largest two-dimensional (2D) ROIs. Inter-reader variability was evaluated with Dice coefficient and Hausdorff distance, whilst its impact on RFs was assessed using mean relative change (MRC) and intraclass correlation coefficient (ICC). For the main lesion of each patient, one reader also segmented a circular ROI on the same image used for the 2D ROI. RESULTS: The best inter-reader contouring agreement was observed for 2D ROIs according to both Dice coefficient (median 0.85, interquartile range 0.78-0.89) and Hausdorff distance (0.21 mm, 0.14-0.31 mm). Comparing RF values, MRC ranged 0-752% for 2D and 0-1567% for 3D. For 24/32 RFs (75%), MRC was lower for 2D than for 3D. An ICC > 0.90 was observed for more RFs for 2D (53%) than for 3D (34%). Only 2/32 RFs (6%) showed a variability between 2D and circular ROIs higher than inter-reader variability. CONCLUSIONS: A 2D contouring approach may help mitigate overall inter-reader variability, albeit stable RFs can be extracted from both 3D and 2D segmentations of CRC liver metastases.

A Convolutional Neural Network based system for Colorectal cancer segmentation on MRI images.

The aim of the study is to present a new Convolutional Neural Network (CNN) based system for the automatic segmentation of the colorectal cancer. The algorithm implemented consists of several steps: a pre-processing to normalize and highlights the tumoral area, the classification based on CNNs, and a post-processing aimed at reducing false positive elements. The classification is performed using three CNNs: each of them classifies the same regions of interest acquired from three different MR sequences. The final segmentation mask is obtained by a majority voting. Performances were evaluated using a semi-automatic segmentation revised by an experienced radiologist as reference standard. The system obtained Dice Similarity Coefficient (DSC) of 0.60, Precision (Pr) of 0.76 and Recall (Re) of 0.55 on the testing set. After applying the leave-one-out validation, we obtained a median DSC=0.58, Pr=0.74, Re=0.54. The promising results obtained by this system, if validated on a larger dataset, could strongly improve personalized medicine.

Radiomics predicts response of individual HER2-amplified colorectal cancer liver metastases in patients treated with HER2-targeted therapy.

The aim of our study was to develop and validate a machine learning algorithm to predict response of individual HER2-amplified colorectal cancer liver metastases (lmCRC) undergoing dual HER2-targeted therapy. Twenty-four radiomics features were extracted after 3D manual segmentation of 141 lmCRC on pretreatment portal CT scans of a cohort including 38 HER2-amplified patients; feature selection was then performed using genetic algorithms. lmCRC were classified as nonresponders (R-), if their largest diameter increased more than 10% at a CT scan performed after 3 months of treatment, responders (R+) otherwise. Sensitivity, specificity, negative (NPV) and positive (PPV) predictive values in correctly classifying individual lesion and overall patient response were assessed on a training dataset and then validated on a second dataset using a Gaussian naïve Bayesian classifier. Per-lesion sensitivity, specificity, NPV and PPV were 89%, 85%, 93%, 78% and 90%, 42%, 73%, 71% respectively in the testing and validation datasets. Per-patient sensitivity and specificity were 92% and 86%. Heterogeneous response was observed in 9 of 38 patients (24%). Five of nine patients were carriers of nonresponder lesions correctly classified as such by our radiomics signature, including four of seven harboring only one nonresponder lesion. The developed method has been proven effective in predicting behavior of individual metastases to targeted treatment in a cohort of HER2 amplified patients. The model accurately detects responder lesions and identifies nonresponder lesions in patients with heterogeneous response, potentially paving the way to multimodal treatment in selected patients. Further validation will be needed to confirm our findings.

A cloud-based computer-aided detection system improves identification of lung nodules on computed tomography scans of patients with extra-thoracic malignancies.

OBJECTIVES: To compare unassisted and CAD-assisted detection and time efficiency of radiologists in reporting lung nodules on CT scans taken from patients with extra-thoracic malignancies using a Cloud-based system. MATERIALS AND METHODS: Three radiologists searched for pulmonary nodules in patients with extra-thoracic malignancy who underwent CT (slice thickness/spacing 2 mm/1.7 mm) between September 2015 and March 2016. All nodules detected by unassisted reading were measured and coordinates were uploaded on a cloud-based system. CAD marks were then reviewed by the same readers using the cloud-based interface. To establish the reference standard all nodules ≥ 3 mm detected by at least one radiologist were validated by two additional experienced radiologists in consensus. Reader detection rate and reporting time with and without CAD were compared. The study was approved by the local ethics committee. All patients signed written informed consent. RESULTS: The series included 225 patients (age range 21-90 years, mean 62 years), including 75 patients having at least one nodule, for a total of 215 nodules. Stand-alone CAD sensitivity for lesions ≥ 3 mm was 85% (183/215, 95% CI: 82-91); mean false-positive rate per scan was 3.8. Sensitivity across readers in detecting lesions ≥ 3 mm was statistically higher using CAD: 65% (95% CI: 61-69) versus 88% (95% CI: 86-91, p<0.01). Reading time increased by 11% using CAD (296 s vs. 329 s; p<0.05). CONCLUSION: In patients with extra-thoracic malignancies, CAD-assisted reading improves detection of ≥ 3-mm lung nodules on CT, slightly increasing reading time. KEY POINTS: • CAD-assisted reading improves the detection of lung nodules compared with unassisted reading on CT scans of patients with primary extra-thoracic tumour, slightly increasing reading time. • Cloud-based CAD systems may represent a cost-effective solution since CAD results can be reviewed while a separated cloud back-end is taking care of computations. • Early identification of lung nodules by CAD-assisted interpretation of CT scans in patients with extra-thoracic primary tumours is of paramount importance as it could anticipate surgery and extend patient life expectancy.

Multiparametric prostate MRI: technical conduct, standardized report and clinical use.

Multiparametric prostate MRI (mp-MRI) is an emerging imaging modality for diagnosis, characterization, staging, and treatment planning of prostate cancer (PCa). The technique, results reporting, and its role in clinical practice have been the subject of significant development over the last decade. Although mp-MRI is not yet routinely used in the diagnostic pathway, almost all urological guidelines have emphasized the potential role of mp-MRI in several aspects of PCa management. Moreover, new MRI sequences and scanning techniques are currently under evaluation to improve the diagnostic accuracy of mp-MRI. This review presents an overview of mp-MRI, summarizing the technical applications, the standardized reporting systems used, and their current roles in various stages of PCa management. Finally, this critical review also reports the main limitations and future perspectives of the technique.