Prospective phase II trial on ablative stereotactic body radiation therapy (SBRT) for medically inoperable thoracic nodes metastases.

BACKGROUND: Oligometastases in mediastinal nodes are increasingly prevalent, posing challenges for treatment with stereotactic body radiotherapy (SBRT) due to proximity to organs at risk (OARs). We report the results of a single prospective observational phase II trial on ablative SBRT for medically inoperable thoracic nodes metastases (NCT02970955). MATERIAL AND METHODS: Since 2017, patients with < 3 nodal metastases were evaluated by the tumor board and included if deemed inoperable. SBRT was delivered using risk adaptive approach based on number, site and size of metastatic nodes (50 Gy/5fractions, 60 Gy/8fractions, 70 Gy/10 fractions). Planning target volume (PTV) partial underdosage was allowed. The primary end point was local control (LC) at 12 months. Secondary end points were: acute and late toxicities, overall survival (OS), progression free survival (PFS), and time to next systemic therapy (TTNS). RESULTS: Between 03/2017-11/2021, 32 patients (41 nodal metastases) were included. NSCLC (13pts), breast (5pts) and colorectal cancer (4pts) were the most represented primary tumour. In 66 % cases, partial PTV undercoverage was necessary. LC at 1 and 2 years was 93.5 % and 82.3 %, respectively. Treatment was well-tolerated with no acute or late toxicity ≥ G3. Median OS was 59.7 months. OS at 1 and 2 years was 96.9 % and 83.8 % respectively. Median PFS was 12.2 months. PFS at 1 and 2 years was 53.1 % and 31.3 %, respectively. CONCLUSION: This trial supported the feasibility and safety of ablative SBRT for thoracic nodes metastases thanks to risk adaptive approach allowing to delay of new systemic therapies. Larger studies are needed to confirm these observations.

Oligo metastatic renal cell carcinoma: stereotactic body radiation therapy, if, when and how?

BACKGROUND AND PURPOSE: Renal cell carcinoma (RCC) has traditionally been considered radioresistant with a limited role for conventional fractionation as a local approach. Nevertheless, since the appearance of stereotactic body radiation therapy (SBRT), radiotherapy (RT) has been increasingly employed in the management of metastatic RCC (mRCC). The aim of this study was to evaluate the role of SBRT for synchronous and metachronous oligo metastatic RCC patients in terms of local control, delay of systemic treatment, overall survival and toxicity. PATIENTS AND METHODS: A Monocentric single institution retrospective data collection was performed. Inclusion criteria were: (1) oligo-recurrent or oligo-progressive disease (less than 5 metastases) in mRCC patients after radical/partial nephrectomy or during systemic therapy, (2) metastasectomy or other metastasis-directed, rather than SBRT not feasible, (3) any contraindication to receive systemic therapy (such as comorbidities), (4) all the histologies were included, (5) available signed informed consent form for treatment. Tumor response and toxicity were evaluated using the response evaluation criteria in solid tumors and the Common Terminology Criteria for Adverse Events version 4.03, respectively. Progression-free survival in-field and out-field (in-field and out-field PFS) and overall survival (OS) were calculated via the Kaplan-Meier method. The drug treatment-free interval was calculated from the start of SBRT to the beginning of any systemic therapy. RESULTS: From 2010 to December 2018, 61 patients with extracranial and intracranial metastatic RCC underwent SBRT on 83 lesions. Intracranial and extracranial lesions were included. Forty-five (74%) patients were treated for a solitary metastatic lesion. Median RT dose was 25 Gy (range 10-52) in 5-10 fractions. With a median follow-up of 2.3 years (range 0-7.15), 1-year in-field PFS was 70%, 2-year in-field PFS was 55%. One year out-field PFS was 39% and 1-year OS was 78%. Concomitant systemic therapy was employed for only 11 (18%) patients, for the others 50 (82%) the drug treatment-free rate was 70% and 50% at 1 and 2 years, respectively. No > G1 acute and late toxicities were reported. CONCLUSION: The pattern of failure was pre-dominantly out-of-field, even if the population was negatively selected and the used RT dose could be considered palliative. Therefore, SBRT appears to be a well-tolerated, feasible and safe approach in oligo metastatic RCC patients with an excellent in-field PFS. SBRT might play a role in the management of selected RCC patients allowing for a delay systemic therapy begin (one out of two patients were free from new systemic therapy at 2 years after SBRT). Further research on SBRT dose escalation is warranted.

Effect of maternal psychopathology on neurodevelopmental outcome and quality of the dyadic relationship in preterm infants: an explorative study.

Background: The literature shows that parents of preterm infants are at risk of psychological distress and that this may impact on the quality of the parent-child relationship and on the child's development.Aim: This longitudinal study was conducted to examine in preterm infants relationships between maternal psychological variables, parental protective factors, perinatal infant variables, and neurodevelopmental outcome. Furthermore, we explored the impact of these variables on the quality of the mother-infant relationship (dyadic synchrony).Subjects and methods: A total of 29 preterm infants (GA < 34 weeks) and their mothers were evaluated twice: at t0, during the infant's hospitalization in the neonatal intensive care unit (NICU), and at 12 months of infant corrected age (t2).Results: With the exception of decreases in anxiety and perceived social support and an increase in the rate of severe depression at follow-up, there were no significant changes between t0 and t1 assessments. The infant's perinatal risk status was the variable that impacted most on maternal psychopathology. Furthermore, our data revealed that baseline maternal stress related to the appearance of the child and to the mother's perception of her parenting role represent a risk factor in relation to developmental outcome at 12 months of corrected age. Finally, no correlations emerged between dyadic synchrony and infant perinatal data, maternal psychological variables (at t0 and at t1), or child developmental outcome at t1.Conclusions: Our results underline the need to identify negative maternal affective states early in the mother-child relationship and to provide mothers with adequate support in the NICU, to enhance their parental role.

Comparison of two portable solid state detectors with an improved collimation and alignment device for mammographic x-ray spectroscopy.

We describe a portable system for mammographic x-ray spectroscopy, based on a 2 X 2 X 1 mm3 cadmium telluride (CdTe) solid state detector, that is greatly improved over a similar system based on a 3 X 3 X 2 mm3 cadmium zinc telluride (CZT) solid state detector evaluated in an earlier work. The CdTe system utilized new pinhole collimators and an alignment device that facilitated measurement of mammographic x-ray spectra. Mammographic x-ray spectra acquired by each system were comparable. Half value layer measurements obtained using an ion chamber agreed closely with those derived from the x-ray spectra measured by either detector. The faster electronics and other features of the CdTe detector allowed its use with a larger pinhole collimator than could be used with the CZT detector. Additionally, the improved pinhole collimator and alignment features of the apparatus permitted much more rapid setup for acquisition of x-ray spectra than was possible on the system described in the earlier work. These improvements in detector technology, collimation and ease of alignment, as well as low cost, make this apparatus attractive as a tool for both laboratory research and advanced mammography quality control.

A completely automated CAD system for mass detection in a large mammographic database.

Mass localization plays a crucial role in computer-aided detection (CAD) systems for the classification of suspicious regions in mammograms. In this article we present a completely automated classification system for the detection of masses in digitized mammographic images. The tool system we discuss consists in three processing levels: (a) Image segmentation for the localization of regions of interest (ROIs). This step relies on an iterative dynamical threshold algorithm able to select iso-intensity closed contours around gray level maxima of the mammogram. (b) ROI characterization by means of textural features computed from the gray tone spatial dependence matrix (GTSDM), containing second-order spatial statistics information on the pixel gray level intensity. As the images under study were recorded in different centers and with different machine settings, eight GTSDM features were selected so as to be invariant under monotonic transformation. In this way, the images do not need to be normalized, as the adopted features depend on the texture only, rather than on the gray tone levels, too. (c) ROI classification by means of a neural network, with supervision provided by the radiologist's diagnosis. The CAD system was evaluated on a large database of 3369 mammographic images [2307 negative, 1062 pathological (or positive), containing at least one confirmed mass, as diagnosed by an expert radiologist]. To assess the performance of the system, receiver operating characteristic (ROC) and free-response ROC analysis were employed. The area under the ROC curve was found to be Az = 0.783 +/- 0.008 for the ROI-based classification. When evaluating the accuracy of the CAD against the radiologist-drawn boundaries, 4.23 false positives per image are found at 80% of mass sensitivity.

GPCALMA: a Grid-based tool for mammographic screening.

OBJECTIVES: The next generation of high energy physics (HEP) experiments requires a GRID approach to a distributed computing system: the key concept is the Virtual ORGANISATION (VO), a group of distributed users with a common goal and the will to share their resources. METHODS: A similar approach, applied to a group of hospitals that joined the GPCALMA project (Grid Platform for Computer Assisted Library for MAmmography), will allow common screening programs for early diagnosis of breast and, in the future, lung cancer. The application code makes use of neural networks for the image analysis and is useful in improving the radiologists' diagnostic performance. GRID services allow remote image analysis and interactive online diagnosis, with a potential for a relevant reduction of the delays presently associated with screening programs. RESULTS AND CONCLUSIONS: A prototype of the system, based on AliEn GRID Services [1], is already available, with a central server running common services [2] and several clients connecting to it. Mammograms can be acquired in any location; the related information required to select and access them at any time is stored in a common service called Data Catalogue, which can be queried by any client. Thanks to the PROOF facility [3], the result of a query can be used as input for analysis algorithms, which are executed on the nodes where the input images are stored,. The selected approach avoids data transfers for all the images with a negative diagnosis and allows an almost real time diagnosis for the set of images with high cancer probability.

Automatic detection of lung nodules in CT datasets based on stable 3D mass-spring models.

We propose a computer-aided detection (CAD) system which can detect small-sized (from 3mm) pulmonary nodules in spiral CT scans. A pulmonary nodule is a small lesion in the lungs, round-shaped (parenchymal nodule) or worm-shaped (juxtapleural nodule). Both kinds of lesions have a radio-density greater than lung parenchyma, thus appearing white on the images. Lung nodules might indicate a lung cancer and their early stage detection arguably improves the patient survival rate. CT is considered to be the most accurate imaging modality for nodule detection. However, the large amount of data per examination makes the full analysis difficult, leading to omission of nodules by the radiologist. We developed an advanced computerized method for the automatic detection of internal and juxtapleural nodules on low-dose and thin-slice lung CT scan. This method consists of an initial selection of nodule candidates list, the segmentation of each candidate nodule and the classification of the features computed for each segmented nodule candidate.The presented CAD system is aimed to reduce the number of omissions and to decrease the radiologist scan examination time. Our system locates with the same scheme both internal and juxtapleural nodules. For a correct volume segmentation of the lung parenchyma, the system uses a Region Growing (RG) algorithm and an opening process for including the juxtapleural nodules. The segmentation and the extraction of the suspected nodular lesions from CT images by a lung CAD system constitutes a hard task. In order to solve this key problem, we use a new Stable 3D Mass-Spring Model (MSM) combined with a spline curves reconstruction process. Our model represents concurrently the characteristic gray value range, the directed contour information as well as shape knowledge, which leads to a much more robust and efficient segmentation process. For distinguishing the real nodules among nodule candidates, an additional classification step is applied; furthermore, a neural network is applied to reduce the false positives (FPs) after a double-threshold cut. The system performance was tested on a set of 84 scans made available by the Lung Image Database Consortium (LIDC) annotated by four expert radiologists. The detection rate of the system is 97% with 6.1 FPs/CT. A reduction to 2.5 FPs/CT is achieved at 88% sensitivity. We presented a new 3D segmentation technique for lung nodules in CT datasets, using deformable MSMs. The result is a efficient segmentation process able to converge, identifying the shape of the generic ROI, after a few iterations. Our suitable results show that the use of the 3D AC model and the feature analysis based FPs reduction process constitutes an accurate approach to the segmentation and the classification of lung nodules.

Computer-assisted diagnosis (CAD) in mammography: comparison of diagnostic accuracy of a new algorithm (Cyclopus, Medicad) with two commercial systems.

PURPOSE: The study compares the diagnostic accuracy (correct identification of cancer) of a new computer-assisted diagnosis (CAD) system (Cyclopus) with two other commercial systems (R2 and CADx). MATERIALS AND METHODS: Cyclopus was tested on a set of 120 mammograms on which the two compared commercial systems had been previously tested. The set consisted of mammograms reported as negative, preceding 31 interval cancers reviewed as screening error or minimal sign, and of 89 verified negative controls randomly selected from the same screening database. RESULTS: Cyclopus sensitivity was 74.1% (R2=54.8%; CADx=41.9%) and was higher for interval cancers reviewed as screening error (90.9%; R2=54.5%; CADx=81.8%) compared with those reviewed as minimal sign (65.0%; R2=55.0%; CADx=20.0%). Specificity was 15.7% (R2=29.2%; CADx=17.9%). Overall accuracy was 30.8% (R2=35.8%; CADx=24.1%). The positive predictive value of a case with CAD marks [regions of interest (ROI)] was 23.4% (23/98; R2=16.0%; CADx=15.1%). Average ROI number per view among negative controls was 1.13 (R2=0.93; CADx=0.99). Cyclopus was more sensitive for masses compared with isolated microcalcifications (208 vs 62 ROI; R2=90 vs 213; CADx=192 vs 130). CONCLUSIONS: Compared with two other commercial systems, Cyclopus was more sensitive (R2 p=0.14; CADx p=0.02) and less specific (R2 p=0.02; CADx p=0.64).

MAGIC-5: an Italian mammographic database of digitised images for research.

The implementation of a database of digitised mammograms is discussed. The digitised images were collected beginning in 1999 by a community of physicists in collaboration with radiologists in several Italian hospitals as a first step in developing and implementing a computer-aided detection (CAD) system. All 3,369 mammograms were collected from 967 patients and classified according to lesion type and morphology, breast tissue and pathology type. A dedicated graphical user interface was developed to visualise and process mammograms to support the medical diagnosis directly on a high-resolution screen. The database has been the starting point for developing other medical imaging applications, such as a breast CAD, currently being upgraded and optimised for use in a distributed environment with grid services, in the framework of the Instituto Nazionale di Fisicia Nucleare (INFN)-funded Medical Applications on a Grid Infrastructure Connection (MAGIC)-5 project.