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Purpose A radiomics features classifier was implemented to evaluate segmentation quality of heart structures. A robust feature set sensitive to incorrect contouring would provide an ideal quantitative index to drive autocontouring optimization. Methods Twenty-five cardiac sub-structures were contoured as regions of interest in 36 CTs. Radiomic features were extracted from manually-contoured (MC) and Hierarchical-Clustering automatic-contouring (AC) structures. A robust feature-set was identified from correctly contoured CT datasets. Features variation was analyzed over a MC/AC dataset. A supervised-learning approach was used to train an Artificial-Intelligence (AI) classifier; incorrect contouring cases were generated from the gold-standard MC datasets with translations, expansions and contractions. ROC curves and confusion matrices were used to evaluate the AI-classifier performance. Results Twenty radiomics features, were found to be robust across structures, showing a good/excellent intra-class correlation coefficient (ICC) index comparing MC/AC. A significant correlation was obtained with quantitative indexes (Dice-Index, Hausdorff-distance). The trained AI-classifier detected correct contours (CC) and not correct contours (NCC) with an accuracy of 82.6% and AUC of 0.91. True positive rate (TPR) was 85.1% and 81.3% for CC and NCC. Detection of NCC at this point of the development still depended strongly on degree of contouring imperfection. Conclusions A set of radiomics features, robust on “gold-standard” contour and sensitive to incorrect contouring was identified and implemented in an AI-workflow to quantify segmentation accuracy. This workflow permits an automatic assessment of segmentation quality and may accelerate expansion of an existing autocontouring atlas database as well as improve dosimetric analyses of large treatment plan databases.

Mismatch-repair-deficiency resulting in microsatellite instability (MSI) may confer increased radiosensitivity in locally advanced/metastatic tumors and thus radiotherapy (RT) potentially might have a changing role in treating this subset of patients, alone or in combination with checkpoint inhibitors. We report a 76year-old patient presenting with locally advanced undifferentiated prostate cancer (LAPC), infiltrating bladder and rectum. Molecular analysis revealed high-MSI with an altered expression of MSH2 and MSH6 at immunohistochemistry. Two months after 6 chemotherapy cycles with Docetaxel associated to an LHRH analogue, a CT scan showed stable disease. After palliative RT (30Gy/10 fractions) directed to the tumor mass with a 3D-conformal setup, a follow-up CT scan at 8 weeks revealed an impressive response, that remained stable at CT after 9 months, with sustained biochemical response. To our knowledge, this is the first case of such a sustained response to low dose RT alone in H-MSI LAPC.Routine evaluation of MSI in patients with locally problematic advanced tumors might change treatment strategy and treatment aim in this setting, from a purely palliative approach to a quasi-curative paradigm.

Background Mismatch-repair-deficiency resulting in microsatellite instability (MSI) may confer increased radiosensitivity in locally advanced/metastatic tumors and thus radiotherapy (RT) potentially might have a changing role in treating this subset of patients, alone or in combination with checkpoint inhibitors. Case presentation We report a 76 year-old Italian male patient presenting with locally advanced undifferentiated prostate cancer (LAPC), infiltrating bladder and rectum. Molecular analysis revealed high-MSI with an altered expression of MSH2 and MSH6 at immunohistochemistry. Two months after 6 chemotherapy cycles with Docetaxel associated to an LHRH analogue, a computed tomography scan showed stable disease. After palliative RT (30 Gy/10 fractions) directed to the tumor mass with a 3D-conformal setup, a follow-up computed tomography scan at 8 weeks revealed an impressive response that remained stable at computed tomography after 9 months, with sustained biochemical response. To our knowledge, this is the first case of such a sustained response to low dose RT alone in high-MSI LAPC. Conclusions Routine evaluation of MSI in patients with locally problematic advanced tumors might change treatment strategy and treatment aim in this setting, from a purely palliative approach to a quasi-curative paradigm.

Purpose Segmentation of cardiac sub-structures for dosimetric analyses is usually performed manually in time-consuming procedure. Automatic segmentation may facilitate large-scale retrospective analysis and adaptive radiotherapy. Various approaches, among them Hierarchical Clustering, were applied to improve performance of atlas-based segmentation (ABS). Methods Training dataset of ABS consisted of 36 manually contoured CT-scans. Twenty-five cardiac sub-structures were contoured as regions of interest (ROIs). Five auto-segmentation methods were compared: simultaneous automatic contouring of all 25 ROIs (Method-1); automatic contouring of all 25 ROIs using lungs as anatomical barriers (Method-2); automatic contouring of a single ROI for each contouring cycle (Method-3); hierarchical cluster-based automatic contouring (Method-4); simultaneous truth and performance level estimation (STAPLE). Results were evaluated on 10 patients. Dice similarity coefficient (DSC), average Hausdorff distance (AHD), volume comparison and physician score were used as validation metrics. Results Atlas performance improved increasing number of atlases. Among the five ABS methods, Hierarchical Clustering workflow showed a significant improvement maintaining a clinically acceptable time for contouring. Physician scoring was acceptable for 70% of the ROI automatically contoured. Inter-observer evaluation showed that contours obtained by Hierarchical Clustering method are statistically comparable with them obtained by a second, independent, expert contourer considering DSC. Considering AHD, distance from the gold standard is lower for ROIs segmented by ABS. Conclusions Hierarchical clustering resulted in best ABS results for the primarily investigated platforms and compared favorably to a second benchmark system. Auto-contouring of smaller structures, being in range of variation between manual contourers, may be ideal for large-scale retrospective dosimetric analysis.

Purpose Automatic segmentations to contour region of interest (ROIs) in radiotherapy is becoming an impending necessity to reduce physicians’ workload and inter-user variability. The aims were: (I) Comparison of automatic segmentations between atlas-based-segmentation (ABS) and model-based-segmentation (MBS); (II) Evaluation of ABS performances with different inputs; (III) Implementation of a classifier to assess segmentations quality. Methods and materials 42 patients (37 training, 5 validation), previously contoured within left partial breast irradiation protocol, were used for atlas construction. We focused on lungs, spinal cord, heart, contralateral breast and thyroid. Automatic segmentations were evaluated by DiceSimilarityCoefficient (DSC) and Hausdorff distance (AHD) computed in RayStation® by IronPython® scripts. ABS were launched, varying the number of structures and training patients. The first method contoured all the structures in one session searching the best matching with one training patient. The second one contoured each structure finding a one-by-one matching. To test the algorithms, 13 new patients were used. Moreover, TrueNegative cases were generated from original ROIs with translations/expansions of 0.2 cm, 0.3 cm, 0.5 cm and 0.8 cm. The classifier was implemented in Matlab®. Results ABS gained results for heart, spinal cord and breast similar with clinical gold standard. ABS performed auto-segmentation better than MBS for lungs (DSC: 0.97–0.97; AHD: 0.09–0.06 for left and right). The thyroid segmentation was not satisfactory (DSC Conclusion ABS using the customized atlas perform better than the MBS. ABS segmentations had performances comparable with clinical gold standard except for small ROI. ABS reduce physician contouring workload up to 75%, with a time gain of 20–30 min for patient.

IntroductionStressful or traumatic events occurred during childhood do have a role in the genesis of personality disorders. Less is known about this remarkable association in people with addictive behaviors.AimsThis study aims to disentangle the relationship between childhood adverse experiences, personality disorders (PDs) and substance use disorders (SUDs).MethodsA cross-sectional naturalistic study of 320 consecutive SUDs outpatients attending an Italian addiction service. Participants were clinically evaluated by a semi-structured interview, the SCID-II (Structured Clinical Interview for DSM-IV Axis II PD), and the CECA-Q (Childhood Experience of Care and Abuse-questionnaire). Multivariate logistic regression analyses were used to estimate odds ratio (OR) and 95% confidence intervals (95% CI)ResultsThe prevalence rate of PDs in our sample of patients is 62.2%. PDs were associated with a history of physical punishment in childhood (OR = 1.82; CI 95%: 1.05–3.16; P = 0.034). Being exposed to three adverse events increases the risk of Cluster B PD (OR = 5.92; CI 95%: 2.21–15.92; P < 0.001). Heroin addiction increases the risk of Cluster B PD, both as previous (OR = 2.93; CI 95%: 1.49–5.75; P = 0.003) or current (OR = 4.34; IC 95%: 1.97–11.8; P = 0.004) consumption. Childhood institutionalization increases the probability of cluster B PD (OR: 2.15; CI 95%: 1.02–4.54; P = 0.041). Whereas being employed reduces the probability of both PDs and SUDs (OR = 0.54; CI 95%: 0.31–0.93; P = 0.027).ConclusionsPDs occurred in 62% of patients and were related to adverse childhood experiences and SUDs having detrimental effects on both social and occupational functioning. Being employed could be a protective factor on the development of PDs.Disclosure of interestThe authors have not supplied their declaration of competing interest.