Acrometastases to the Hand: A Systematic Review.

Background and Objectives: The term acrometastases (AM) refers to secondary lesions sited distally to the elbow and knee, representing 0.1% of all bony metastases. By frequency, pulmonary cancer and gastrointestinal and genitourinary tract neoplasms are the most responsible for the reported AM. Improvements in oncologic patient care favor an increase in the incidence of such rare cases. We performed a systematic review of acrometastases to the hand to provide further insight into the management of these fragile patients. We also present a peculiar case of simultaneous acrometastasis to the ring finger and pathological vertebral fracture. Material and Methods: A literature search according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement was conducted using the PubMed, Google Scholar, and Scopus databases in December 2020 on metastasis to the hand and wrist, from 1986 to 2020. MeSH terms included acrometastasis, carpal metastasis, hand metastasis, finger metastasis, phalangeal metastasis, and wrist metastasis. Results: In total, 215 studies reporting the follow-up of 247 patients were analyzed, with a median age of 62 years (range 10-91 years). Overall, 162 out of 247 patients were males (65.6%) and 85 were females (34.4%). The median reported follow-up was 5 months (range 0.5-39). The median time from primary tumor diagnosis to acrometastasis was 24 months (range 0.7-156). Acrometastases were located at the finger/phalanx (68.4%), carpal (14.2%), metacarpal (14.2%), or other sites (3.2%). The primary tumors were pulmonary in 91 patients (36.8%). The average interval from primary tumor diagnosis to acrometastasis varied according to the primary tumor type from 2 months (in patients with mesenchymal tumors) to 64.0 months (in patients with breast cancer). Conclusions: Acrometastases usually develop in the late stage of oncologic disease and are associated with short life expectancy. Their occurrence can no longer be considered rare; physicians should thus be updated on their surgical management and their impact on prognosis and survival.

A preliminary PET radiomics study of brain metastases using a fully automatic segmentation method.

BACKGROUND: Positron Emission Tomography (PET) is increasingly utilized in radiomics studies for treatment evaluation purposes. Nevertheless, lesion volume identification in PET images is a critical and still challenging step in the process of radiomics, due to the low spatial resolution and high noise level of PET images. Currently, the biological target volume (BTV) is manually contoured by nuclear physicians, with a time expensive and operator-dependent procedure. This study aims to obtain BTVs from cerebral metastases in patients who underwent L-[11C]methionine (11C-MET) PET, using a fully automatic procedure and to use these BTVs to extract radiomics features to stratify between patients who respond to treatment or not. For these purposes, 31 brain metastases, for predictive evaluation, and 25 ones, for follow-up evaluation after treatment, were delineated using the proposed method. Successively, 11C-MET PET studies and related volumetric segmentations were used to extract 108 features to investigate the potential application of radiomics analysis in patients with brain metastases. A novel statistical system has been implemented for feature reduction and selection, while discriminant analysis was used as a method for feature classification. RESULTS: For predictive evaluation, 3 features (asphericity, low-intensity run emphasis, and complexity) were able to discriminate between responder and non-responder patients, after feature reduction and selection. Best performance in patient discrimination was obtained using the combination of the three selected features (sensitivity 81.23%, specificity 73.97%, and accuracy 78.27%) compared to the use of all features. Secondly, for follow-up evaluation, 8 features (SUVmean, SULpeak, SUVmin, SULpeak prod-surface-area, SUVmean prod-sphericity, surface mean SUV 3, SULpeak prod-sphericity, and second angular moment) were selected with optimal performance in discriminant analysis classification (sensitivity 86.28%, specificity 87.75%, and accuracy 86.57%) outperforming the use of all features. CONCLUSIONS: The proposed system is able i) to extract 108 features for each automatically segmented lesion and ii) to select a sub-panel of 11C-MET PET features (3 and 8 in the case of predictive and follow-up evaluation), with valuable association with patient outcome. We believe that our model can be useful to improve treatment response and prognosis evaluation, potentially allowing the personalization of cancer treatment plans.

A Robust [18F]-PSMA-1007 Radiomics Ensemble Model for Prostate Cancer Risk Stratification.

The aim of this study is to investigate the role of [18F]-PSMA-1007 PET in differentiating high- and low-risk prostate cancer (PCa) through a robust radiomics ensemble model. This retrospective study included 143 PCa patients who underwent [18F]-PSMA-1007 PET/CT imaging. PCa areas were manually contoured on PET images and 1781 image biomarker standardization initiative (IBSI)-compliant radiomics features were extracted. A 30 times iterated preliminary analysis pipeline, comprising of the least absolute shrinkage and selection operator (LASSO) for feature selection and fivefold cross-validation for model optimization, was adopted to identify the most robust features to dataset variations, select candidate models for ensemble modelling, and optimize hyperparameters. Thirteen subsets of selected features, 11 generated from the preliminary analysis plus two additional subsets, the first based on the combination of robust and fine-tuning features, and the second only on fine-tuning features were used to train the model ensemble. Accuracy, area under curve (AUC), sensitivity, specificity, precision, and f-score values were calculated to provide models' performance. Friedman test, followed by post hoc tests corrected with Dunn-Sidak correction for multiple comparisons, was used to verify if statistically significant differences were found in the different ensemble models over the 30 iterations. The model ensemble trained with the combination of robust and fine-tuning features obtained the highest average accuracy (79.52%), AUC (85.75%), specificity (84.29%), precision (82.85%), and f-score (78.26%). Statistically significant differences (p < 0.05) were found for some performance metrics. These findings support the role of [18F]-PSMA-1007 PET radiomics in improving risk stratification for PCa, by reducing dependence on biopsies.

Microglia and glioblastoma heterocellular interplay sustains tumour growth and proliferation as an off-target effect of radiotherapy.

Glioblastoma (GBM), a WHO grade IV glioma, is a malignant primary brain tumour for which combination of surgery, chemotherapy and radiotherapy is the first-line approach despite adverse effects. Tumour microenvironment (TME) is characterized by an interplay of cells and soluble factors holding a critical role in neoplastic development. Significant pathophysiological changes have been found in GBM TME, such as glia activation and oxidative stress. Microglia play a crucial role in favouring GBM growth, representing target cells of immune escape mechanisms. Our study aims at analysing radiation-induced effects in modulating intercellular communication and identifying the basis of protective mechanisms in radiation-naïve GBM cells. Tumour cells were treated with conditioned media (CM) derived from 0, 2 or 15 Gy irradiated GBM cells or 0, 2 or 15 Gy irradiated human microglia. We demonstrated that irradiated microglia promote an increase of GBM cell lines proliferation through paracrine signalling. On the contrary, irradiated GBM-derived CM affect viability, triggering cell death mechanisms. In addition, we investigated whether these processes involve mitochondrial mass, fitness and oxidative phosphorylation and how GBM cells respond at these induced alterations. Our study suggests that off-target radiotherapy modulates microglia to support GBM proliferation and induce metabolic modifications.

A Critical Analysis of the Robustness of Radiomics to Variations in Segmentation Methods in 18F-PSMA-1007 PET Images of Patients Affected by Prostate Cancer.

BACKGROUND: Radiomics shows promising results in supporting the clinical decision process, and much effort has been put into its standardization, thus leading to the Imaging Biomarker Standardization Initiative (IBSI), that established how radiomics features should be computed. However, radiomics still lacks standardization and many factors, such as segmentation methods, limit study reproducibility and robustness. AIM: We investigated the impact that three different segmentation methods (manual, thresholding and region growing) have on radiomics features extracted from 18F-PSMA-1007 Positron Emission Tomography (PET) images of 78 patients (43 Low Risk, 35 High Risk). Segmentation was repeated for each patient, thus leading to three datasets of segmentations. Then, feature extraction was performed for each dataset, and 1781 features (107 original, 930 Laplacian of Gaussian (LoG) features, 744 wavelet features) were extracted. Feature robustness and reproducibility were assessed through the intra class correlation coefficient (ICC) to measure agreement between the three segmentation methods. To assess the impact that the three methods had on machine learning models, feature selection was performed through a hybrid descriptive-inferential method, and selected features were given as input to three classifiers, K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Linear Discriminant Analysis (LDA), Random Forest (RF), AdaBoost and Neural Networks (NN), whose performance in discriminating between low-risk and high-risk patients have been validated through 30 times repeated five-fold cross validation. CONCLUSIONS: Our study showed that segmentation methods influence radiomics features and that Shape features were the least reproducible (average ICC: 0.27), while GLCM features the most reproducible. Moreover, feature reproducibility changed depending on segmentation type, resulting in 51.18% of LoG features exhibiting excellent reproducibility (range average ICC: 0.68-0.87) and 47.85% of wavelet features exhibiting poor reproducibility that varied between wavelet sub-bands (range average ICC: 0.34-0.80) and resulted in the LLL band showing the highest average ICC (0.80). Finally, model performance showed that region growing led to the highest accuracy (74.49%), improved sensitivity (84.38%) and AUC (79.20%) in contrast with manual segmentation.

The Role of [68Ga]Ga-DOTA-SSTR PET Radiotracers in Brain Tumors: A Systematic Review of the Literature and Ongoing Clinical Trials.

BACKGROUND: The development of [68Ga]Ga-DOTA-SSTR PET tracers has garnered interest in neuro-oncology, to increase accuracy in diagnostic, radiation planning, and neurotheranostics protocols. We systematically reviewed the literature on the current uses of [68Ga]Ga-DOTA-SSTR PET in brain tumors. METHODS: PubMed, Scopus, Web of Science, and Cochrane were searched in accordance with the PRISMA guidelines to include published studies and ongoing trials utilizing [68Ga]Ga-DOTA-SSTR PET in patients with brain tumors. RESULTS: We included 63 published studies comprising 1030 patients with 1277 lesions, and 4 ongoing trials. [68Ga]Ga-DOTA-SSTR PET was mostly used for diagnostic purposes (62.5%), followed by treatment planning (32.7%), and neurotheranostics (4.8%). Most lesions were meningiomas (93.6%), followed by pituitary adenomas (2.8%), and the DOTATOC tracer (53.2%) was used more frequently than DOTATATE (39.1%) and DOTANOC (5.7%), except for diagnostic purposes (DOTATATE 51.1%). [68Ga]Ga-DOTA-SSTR PET studies were mostly required to confirm the diagnosis of meningiomas (owing to their high SSTR2 expression and tracer uptake) or evaluate their extent of bone invasion, and improve volume contouring for better radiotherapy planning. Some studies reported the uncommon occurrence of SSTR2-positive brain pathology challenging the diagnostic accuracy of [68Ga]Ga-DOTA-SSTR PET for meningiomas. Pre-treatment assessment of tracer uptake rates has been used to confirm patient eligibility (high somatostatin receptor-2 expression) for peptide receptor radionuclide therapy (PRRT) (i.e., neurotheranostics) for recurrent meningiomas and pituitary carcinomas. CONCLUSION: [68Ga]Ga-DOTA-SSTR PET studies may revolutionize the routine neuro-oncology practice, especially in meningiomas, by improving diagnostic accuracy, delineation of radiotherapy targets, and patient eligibility for radionuclide therapies.

Hypofractionated Gamma Knife Radiosurgery: Institutional Experience on Benign and Malignant Intracranial Tumors.

BACKGROUND/AIM: We investigated the treatment outcomes and complications associated with hypofractionated GKRS for the treatment of benign and malignant intracranial tumors. PATIENTS AND METHODS: Patients with intracranial tumors not candidate or refusing surgery were evaluated to assess eligibility to undergo hypofractionated Gamma Knife radiosurgery (GKRS). Targeted volumes were calculated using the GammaPlan® workstation, and GKRS protocols were delivered with 3 or 5 daily fractions and a maximal total dose of 25 Gy. The thermoplastic mask was used to immobilize the patient's head without pin-based fixation frames. RESULTS: A total of 41 patients, affected with 6 different histologies, were treated and followed-up for a median of 12 months (range=4-24 months). Meningiomas were the most common tumors (33, 80.5%), followed by brain metastases (4, 9.7%). At last follow-up, 33 patients (80.5%) had stable disease, 8 tumor regression (19.5%), and 0 tumor progression. No acute radiation toxicity was observed. Death was reported in 3 patients (7.3%) due to malignant tumor progression. CONCLUSION: Our hypofractionated GKRS protocol proved to be effective and safe in the treatment of patients with benign and malignant intracranial tumors. Local tumor control was achieved in all patients, with 8 patients showing tumor regression and no cases of acute radiation toxicity.

Detection of "Incidentalomas" on Brain and Body 68Ga-DOTATOC-PET Scans: A Retrospective Study and Case Illustration.

BACKGROUND/AIM: One of the main limitations of standard imaging modalities is microscopic tumor extension, which is often difficult to detect on magnetic resonance imaging (MRI) and computer tomography (CT) in the early stages of the tumor. (68)Ga-DOTA(0)-Phe(1)-Tyr(3)-octreotide positron-emission tomography/computed tomography (68Ga-DOTATOC PET/CT) has shown efficacy in detecting lesions previously undiagnosed by neuroimaging modalities, such as MRI or CT, and has enabled the detection of multiple benign tumors (like multiple meningiomas in a patient presenting with a single lesion on MRI) or additional secondary metastatic locations. PATIENTS AND METHODS: We retrospectively reviewed data from the Cannizzaro Hospital on brain and body 68Ga-DOTATOC PET/CT "incidentalomas", defined as tumors missed on CT or MRI scans, but detected on 68Ga-DOTATOC PET/CT scans. "Incidentalomas" were classified into "brain" and "body" groups based on their location. The standardized uptake values (SUVs) were compared between the two groups. RESULTS: A total of 61 patients with "incidentalomas" documented on the 68Ga-DOTATOC PET/CT were identified: 18 patients with 25 brain lesions and 43 patients with 85 body lesions. The mean SUV at baseline was 9.01±7.66 in the brain group and 14.8±14.63 in the body group. CONCLUSION: We present the first series on brain and body "incidentalomas" detected on 68Ga-DOTATOC PET/CT. Whole-body 68Ga-DOTATOC PET/CT may be considered in selected patients with brain tumors with high expression of somatostatin receptors to assist radiosurgical or surgical planning and, simultaneously, provide accurate follow-up with early detection of potential metastases.

Spontaneous sacral fracture with associated acrometastasis of the hand.

BACKGROUND: Acrometastases, secondary tumors affecting oncological patients with systemic metastases, are associated with a poor prognosis. In rare cases, acrometastases may precede establishing the primary tumor diagnosis. CASE DESCRIPTION: A 72-year-old female heavy smoker presented with low back pain, and right lower extremity sciatica/radiculopathy. X-rays, CT, MR, and PET-CT scans documented primary lung cancer with multi-organ metastases and accompanying pathological fractures involving the sacrum (S1) and right 4th digit. She underwent a S1 laminectomy and amputation of the distal phalanx of the right fourth finger. The histological examination documented a poorly differentiated pulmonary adenocarcinoma infiltrating bone and soft tissues in the respective locations. The patient was treated with a course of systemic immunotherapy (i.e. pembrolizumab). At 6-month follow-up, the patient is doing well and can stand and walk without pain. CONCLUSION: Spontaneous sacral fractures may be readily misdiagnosed as osteoporotic and/or traumatic lesions. However, in this case, the additional simultaneous presence of a lytic finger lesion raised the suspicion that these were both metastatic tumors. Such acrometastases, as in this case attributed to a lung primary, may indeed involve the spine.

Feasibility on the Use of Radiomics Features of 11[C]-MET PET/CT in Central Nervous System Tumours: Preliminary Results on Potential Grading Discrimination Using a Machine Learning Model.

BACKGROUND/AIM: Nowadays, Machine Learning (ML) algorithms have demonstrated remarkable progress in image-recognition tasks and could be useful for the new concept of precision medicine in order to help physicians in the choice of therapeutic strategies for brain tumours. Previous data suggest that, in the central nervous system (CNS) tumours, amino acid PET may more accurately demarcate the active disease than paramagnetic enhanced MRI, which is currently the standard method of evaluation in brain tumours and helps in the assessment of disease grading, as a fundamental basis for proper clinical patient management. The aim of this study is to evaluate the feasibility of ML on 11[C]-MET PET/CT scan images and to propose a radiomics workflow using a machine-learning method to create a predictive model capable of discriminating between low-grade and high-grade CNS tumours. MATERIALS AND METHODS: In this retrospective study, fifty-six patients affected by a primary brain tumour who underwent 11[C]-MET PET/CT were selected from January 2016 to December 2019. Pathological examination was available in all patients to confirm the diagnosis and grading of disease. PET/CT acquisition was performed after 10 min from the administration of 11C-Methionine (401-610 MBq) for a time acquisition of 15 min. 11[C]-MET PET/CT images were acquired using two scanners (24 patients on a Siemens scan and 32 patients on a GE scan). Then, LIFEx software was used to delineate brain tumours using two different semi-automatic and user-independent segmentation approaches and to extract 44 radiomics features for each segmentation. A novel mixed descriptive-inferential sequential approach was used to identify a subset of relevant features that correlate with the grading of disease confirmed by pathological examination and clinical outcome. Finally, a machine learning model based on discriminant analysis was used in the evaluation of grading prediction (low grade CNS vs. high-grade CNS) of 11[C]-MET PET/CT. RESULTS: The proposed machine learning model based on (i) two semi-automatic and user-independent segmentation processes, (ii) an innovative feature selection and reduction process, and (iii) the discriminant analysis, showed good performance in the prediction of tumour grade when the volumetric segmentation was used for feature extraction. In this case, the proposed model obtained an accuracy of ~85% (AUC ~79%) in the subgroup of patients who underwent Siemens tomography scans, of 80.51% (AUC 65.73%) in patients who underwent GE tomography scans, and of 70.31% (AUC 64.13%) in the whole patients' dataset (Siemens and GE scans). CONCLUSIONS: This preliminary study on the use of an ML model demonstrated to be feasible and able to select radiomics features of 11[C]-MET PET with potential value in prediction of grading of disease. Further studies are needed to improve radiomics algorithms to personalize predictive and prognostic models and potentially support the medical decision process.

68Ga-DOTATOC PET/CT Follow Up after Single or Hypofractionated Gamma Knife ICON Radiosurgery for Meningioma Patients.

68Ga-DOTATOC represents a useful tool in tumor contouring for radiosurgery planning. We present a case series of patients affected by meningiomas on who we performed 68Ga-DOTATOC positron emission tomography (PET)/CT pre-operatively, a subgroup of which also underwent a post-operative 68Ga-DOTATOC PET/CT to evaluate the standardized uptake value (SUV) modification after Gamma Knife ICON treatment in single or hypofractionated fractions. Twenty patients were enrolled/included in this study: ten females and ten males. The median age was 52 years (range 33-80). The median tumor diameter was 3.68 cm (range 0.12-22.26 cm), and the median pre-radiotherapy maximum SUV value was 11 (range 2.3-92). The average of the relative percentage changes between SUVs at baseline and follow up was -6%, ranging from -41% to 56%. The SUV was reduced in seven out of 12 patients (58%), stable in two out of 12 (17%), and increased in three out of 12 (25%), suggesting a biological response of the tumor to the Gamma Knife treatment in most of the cases. 68Ga-DOTATOC-PET represents a valuable tool in assessing the meningioma diagnosis for primary radiosurgery; it is also promising for follow-up assessment.

Development of a new fully three-dimensional methodology for tumours delineation in functional images.

Delineation of tumours in Positron Emission Tomography (PET) plays a crucial role in accurate diagnosis and radiotherapy treatment planning. In this context, it is of outmost importance to devise efficient and operator-independent segmentation algorithms capable of reconstructing the tumour three-dimensional (3D) shape. In previous work, we proposed a system for 3D tumour delineation on PET data (expressed in terms of Standardized Uptake Value - SUV), based on a two-step approach. Step 1 identified the slice enclosing the maximum SUV and generated a rough contour surrounding it. Such contour was then used to initialize step 2, where the 3D shape of the tumour was obtained by separately segmenting 2D PET slices, leveraging the slice-by-slice marching approach. Additionally, we combined active contours and machine learning components to improve performance. Despite its success, the slice marching approach poses unnecessary limitations that are naturally removed by performing the segmentation directly in 3D. In this paper, we migrate our system into 3D. In particular, the segmentation in step 2 is now performed by evolving an active surface directly in the 3D space. The key points of such an advancement are that it performs the shape reconstruction on the whole stack of slices simultaneously, naturally leveraging cross-slice information that could not be exploited before. Additionally, it does not require any specific stopping condition, as the active surface naturally reaches a stable topology once convergence is achieved. Performance of this fully 3D approach is evaluated on the same dataset discussed in our previous work, which comprises fifty PET scans of lung, head and neck, and brain tumours. The results have confirmed that a benefit is indeed achieved in practice for all investigated anatomical districts, both quantitatively, through a set of commonly used quality indicators (dice similarity coefficient >87.66%, Hausdorff distance < 1.48 voxel and Mahalanobis distance < 0.82 voxel), and qualitatively in terms of Likert score (>3 in 54% of the tumours).

Active contour algorithm with discriminant analysis for delineating tumors in positron emission tomography.

In the context of cancer delineation using positron emission tomography datasets, we present an innovative approach which purpose is to tackle the real-time, three-dimensional segmentation task in a full, or at least nearly full automatized way. The approach comprises a preliminary initialization phase where the user highlights a region of interest around the cancer on just one slice of the tomographic dataset. The algorithm takes care of identifying an optimal and user-independent region of interest around the anomalous tissue and located on the slice containing the highest standardized uptake value so to start the successive segmentation task. The three-dimensional volume is then reconstructed using a slice-by-slice marching approach until a suitable automatic stop condition is met. On each slice, the segmentation is performed using an enhanced local active contour based on the minimization of a novel energy functional which combines the information provided by a machine learning component, the discriminant analysis in the present study. As a result, the whole algorithm is almost completely automatic and the output segmentation is independent from the input provided by the user. Phantom experiments comprising spheres and zeolites, and clinical cases comprising various body districts (lung, brain, and head and neck), and two different radio-tracers (18 F-fluoro-2-deoxy-d-glucose, and 11C-labeled Methionine) were used to assess the algorithm performances. Phantom experiments with spheres and with zeolites showed a dice similarity coefficient above 90% and 80%, respectively. Clinical cases showed high agreement with the gold standard (R2 = 0.98). These results indicate that the proposed method can be efficiently applied in the clinical routine with potential benefit for the treatment response assessment, and targeting in radiotherapy.

A smart and operator independent system to delineate tumours in Positron Emission Tomography scans.

Positron Emission Tomography (PET) imaging has an enormous potential to improve radiation therapy treatment planning offering complementary functional information with respect to other anatomical imaging approaches. The aim of this study is to develop an operator independent, reliable, and clinically feasible system for biological tumour volume delineation from PET images. Under this design hypothesis, we combine several known approaches in an original way to deploy a system with a high level of automation. The proposed system automatically identifies the optimal region of interest around the tumour and performs a slice-by-slice marching local active contour segmentation. It automatically stops when a "cancer-free" slice is identified. User intervention is limited at drawing an initial rough contour around the cancer region. By design, the algorithm performs the segmentation minimizing any dependence from the initial input, so that the final result is extremely repeatable. To assess the performances under different conditions, our system is evaluated on a dataset comprising five synthetic experiments and fifty oncological lesions located in different anatomical regions (i.e. lung, head and neck, and brain) using PET studies with 18F-fluoro-2-deoxy-d-glucose and 11C-labeled Methionine radio-tracers. Results on synthetic lesions demonstrate enhanced performances when compared against the most common PET segmentation methods. In clinical cases, the proposed system produces accurate segmentations (average dice similarity coefficient: 85.36 ±â€¯2.94%, 85.98 ±â€¯3.40%, 88.02 ±â€¯2.75% in the lung, head and neck, and brain district, respectively) with high agreement with the gold standard (determination coefficient R2 = 0.98). We believe that the proposed system could be efficiently used in the everyday clinical routine as a medical decision tool, and to provide the clinicians with additional information, derived from PET, which can be of use in radiation therapy, treatment, and planning.

Multiparametric MRI Apparent Diffusion Coefficient (ADC) Accuracy in Diagnosing Clinically Significant Prostate Cancer.

AIM: To evaluate the accuracy of multiparametric magnetic resonance imaging apparent diffusion coefficient (mpMRI ADC) in the diagnosis of clinically significant prostate cancer (PCa). PATIENTS AND METHODS: From January 2016 to December 2016, 44 patients who underwent radical prostatectomy for PCa and mpMRI lesions suggestive of cancer were retrospectively evaluated at definitive specimen. The accuracy of suspicious mpMRI prostate imaging reporting and data system (PI-RADS ≥3) vs. ADC values in the diagnosis of Gleason score ≥7 was evaluated. RESULTS: Receiver operating characteristics (ROC) curve analysis gave back an ADC threshold of 0.747×10-3 mm2/s to separate between Gleason Score 6 and ≥7. The diagnostic accuracy of ADC value (cut-off 0.747×10-3 mm2/s) vs. PI-RADS score ≥3 in diagnosing PCa with Gleason score ≥7 was equal to 84% vs. 63.6% with an area under the curve (AUC) ROC of 0.81 vs. 0.71, respectively. CONCLUSION: ADC evaluation could support clinicians in decision making of patients with PI-RADS score <3 at risk for PCa.

A fully automatic approach for multimodal PET and MR image segmentation in gamma knife treatment planning.

BACKGROUND AND OBJECTIVES: Nowadays, clinical practice in Gamma Knife treatments is generally based on MRI anatomical information alone. However, the joint use of MRI and PET images can be useful for considering both anatomical and metabolic information about the lesion to be treated. In this paper we present a co-segmentation method to integrate the segmented Biological Target Volume (BTV), using [11C]-Methionine-PET (MET-PET) images, and the segmented Gross Target Volume (GTV), on the respective co-registered MR images. The resulting volume gives enhanced brain tumor information to be used in stereotactic neuro-radiosurgery treatment planning. GTV often does not match entirely with BTV, which provides metabolic information about brain lesions. For this reason, PET imaging is valuable and it could be used to provide complementary information useful for treatment planning. In this way, BTV can be used to modify GTV, enhancing Clinical Target Volume (CTV) delineation. METHODS: A novel fully automatic multimodal PET/MRI segmentation method for Leksell Gamma Knife® treatments is proposed. This approach improves and combines two computer-assisted and operator-independent single modality methods, previously developed and validated, to segment BTV and GTV from PET and MR images, respectively. In addition, the GTV is utilized to combine the superior contrast of PET images with the higher spatial resolution of MRI, obtaining a new BTV, called BTVMRI. A total of 19 brain metastatic tumors, undergone stereotactic neuro-radiosurgery, were retrospectively analyzed. A framework for the evaluation of multimodal PET/MRI segmentation is also presented. Overlap-based and spatial distance-based metrics were considered to quantify similarity concerning PET and MRI segmentation approaches. Statistics was also included to measure correlation among the different segmentation processes. Since it is not possible to define a gold-standard CTV according to both MRI and PET images without treatment response assessment, the feasibility and the clinical value of BTV integration in Gamma Knife treatment planning were considered. Therefore, a qualitative evaluation was carried out by three experienced clinicians. RESULTS: The achieved experimental results showed that GTV and BTV segmentations are statistically correlated (Spearman's rank correlation coefficient: 0.898) but they have low similarity degree (average Dice Similarity Coefficient: 61.87 ± 14.64). Therefore, volume measurements as well as evaluation metrics values demonstrated that MRI and PET convey different but complementary imaging information. GTV and BTV could be combined to enhance treatment planning. In more than 50% of cases the CTV was strongly or moderately conditioned by metabolic imaging. Especially, BTVMRI enhanced the CTV more accurately than BTV in 25% of cases. CONCLUSIONS: The proposed fully automatic multimodal PET/MRI segmentation method is a valid operator-independent methodology helping the clinicians to define a CTV that includes both metabolic and morphologic information. BTVMRI and GTV should be considered for a comprehensive treatment planning.

An enhanced random walk algorithm for delineation of head and neck cancers in PET studies.

An algorithm for delineating complex head and neck cancers in positron emission tomography (PET) images is presented in this article. An enhanced random walk (RW) algorithm with automatic seed detection is proposed and used to make the segmentation process feasible in the event of inhomogeneous lesions with bifurcations. In addition, an adaptive probability threshold and a k-means based clustering technique have been integrated in the proposed enhanced RW algorithm. The new threshold is capable of following the intensity changes between adjacent slices along the whole cancer volume, leading to an operator-independent algorithm. Validation experiments were first conducted on phantom studies: High Dice similarity coefficients, high true positive volume fractions, and low Hausdorff distance confirm the accuracy of the proposed method. Subsequently, forty head and neck lesions were segmented in order to evaluate the clinical feasibility of the proposed approach against the most common segmentation algorithms. Experimental results show that the proposed algorithm is more accurate and robust than the most common algorithms in the literature. Finally, the proposed method also shows real-time performance, addressing the physician's requirements in a radiotherapy environment.

Correlation between histological grade and positron emission tomography parameters in cervical carcinoma.

The aim of the present study was to evaluate the changes in cervical cancer glucose metabolism for different levels of cellular differentiation. The metabolic activity was measured by standardized uptake value (SUV), SUV normalized to lean body mass, metabolic tumor volume and total lesion glycolysis using fluorine-18 fluorodeoxyglucose positron emission tomography (PET). A correlation study of these values could be used to facilitate therapeutic choice and to improve clinical practice and outcome. This study considered 32 patients with diagnosed cervical cancers, at different International Federation of Gynecology and Obstetrics stages. Glucose metabolism was assessed by PET examination, and histological specimens were examined to determine their initial grade of differentiation. A correlation study of these values was evaluated. Histological examination showed that all cases were of squamous cell carcinoma. Regarding the differentiation of the tumor, 19 well- to moderately-differentiated tumors and 13 poorly-differentiated tumors were determined. Negative findings for correlations between metabolic parameters and initial grade of histological differentiation were found, and considering that histological grade has been shown to have no consistent prognostic value in cervical cancer treatment, PET imaging could play a significant role in cervical cancer prognosis.

Clinical application of proton beams in the treatment of uveal melanoma: the first therapies carried out in Italy and preliminary results (CATANA Project).

BACKGROUND: The first Italian proton therapy facility was realized in Catania, at the INFN-LNS. With its energy (62 MeV proton beam), it is ideal for the treatment of shallow tumors like those of the ocular region: uveal melanoma, first of all (the most common primary intraocular malignancy of adults) and other less frequent lesions like choroidal hemangioma, conjunctiva melanoma, and eyelid tumors. MATERIAL AND METHODS: The first patient was enrolled in February 2002, and to date 30 patients have been treated. All patients had a localized uveal melanoma, with no systemic metastases, and had specific indications for proton beam radiation therapy: lesions between 5-25 mm basal diameter, not exceeding 15 mm thickness, absence of total retinal detachment or glaucoma. According to the tumor dimensions, 2 patients had a small lesion or T1 (6%), 3 had a medium-sized lesion or T2 (10%), 14 had a large lesion or T3 (47%), and 11 had an extra-large lesion or T3 (37%); no patient had extrascleral invasion or T4 of the TNM-AJCC Staging System. In most cases, the tumor infiltrated only the choroid (14 patients, 47%) or the choroid plus the ciliary body (14 patients, 47%). We also treated a primitive iris melanoma, without diffusion to the ciliary body. The target volume was defined as the tumor plus a safety margin of 2.5 mm, laterally and antero-posteriorly; this margin was increased to 3 mm if ciliary body involvement was present. The treatment was carried out in 4 fractions on 4 consecutive days to a total dose of 54.5 Gy (single fraction 13.6 Gy), which corresponds to 60 CGE (Cobalt Gray Equivalent; single fraction 15 CGE), because the relative biological effectiveness is 1.1. RESULTS: The first follow-up is planned at 6-8 months after the end of the treatment, and our clinical end points are local control (defined as cessation of growth or tumor shrinkage), eye retention, and maintenance of a good visual function. At the time of this writing, we had preliminary results from 13 patients. Nine patients showed tumor shrinkage (69%), 3 a substantially stable dimension (23%), but almost all patients presented an increased ultrasound reflectivity (a surrogate for tumor control). DISCUSSION AND CONCLUSIONS: The literature data show that charged particle therapy has allowed an optimal local control in the treatment of uveal melanomas (about 96% in the different series, superior to that obtained with plaquetherapy [between 83% and 92%]), a metastatic rate slightly better than enucleation reports, and a survival rate of almost 90% at 5 years. Our preliminary results show a tumor response in almost all cases, with no major acute or subacute side effects. We thus plan to continue with our treatment procedures and our dose prescription.