[en] Purpose: To compare dose distribution for two techniques of 3-dimensional conformal radiotherapy (RT): 6-field technique (6F) and 2-dynamic arc therapy (2DA). Methods and materials: Thirty nonmetastatic prostate cancer patients were included. In each patient, two treatment plans were prepared: with six coplanar fields (45 deg., 90 deg., 135 deg., 225 deg., 270 deg., 315 deg.) and with two dynamic lateral 100^o-wide arcs (40-140^o, 220-320^o). Dose-volume histograms (DVHs) were computed and mean area under curve (AUC) values were calculated for the DVHs of Planning Target Volume (PTV), rectum, urinary bladder and femoral heads. Doses given to 30% of rectum (DR₃), to 60% of rectum (DR₆), to 50% of bladder (DB₅), to 50% of femoral head (DF₅) and to 95% of PTV (DPTV₉₅) were reported as a percentage of the total dose. Results: Mean DR₃ and DR₆ for 6F and 2DA were 75.8%, 51.5% and 72.2%, 37.2%, respectively. Mean DB₅ for 6F and 2DA were 68% and 64.2%, respectively. Mean right DF₅ for 6F and 2DA were 35.4% and 45.5%, respectively. Mean DPTV₉₅ for 6F and 2DA were 99% and 99.2%, respectively. Mean AUCs of DVHs of rectum and urinary bladder were significantly higher for 6F (this was more evident for small PTV and in the intermediate dose range). Mean AUC of DVHs of PTV and femoral heads were significantly higher for 2DA. Conclusions: Both 6F and 2DA offer good dose distribution for PTV. 2DA allows for significantly better sparing of rectum and urinary bladder with slightly worse femoral head dose distribution. Further study is warranted in order to establish the clinical relevance of these differences

[en] The standard treatment options based on the risk category (stage, Gleason score, PSA) for localized prostate cancer include surgery, radiotherapy and watchful waiting. The literature does not provide clear-cut evidence for the superiority of surgery over radiotherapy, whereas both approaches differ in their side effects. The definitive external beam irradiation is frequently employed in stage T1b-T1c, T2 and T3 tumors. There is a pretty strong evidence that intermediate- and high-risk patients benefit from dose escalation. The latter requires reduction of the irradiated normal tissue (using 3-dimensional conformal approach, intensity modulated radiotherapy, image-guided radiotherapy, etc.). Recent data suggest that prostate cancer may benefit from hypofractionation due to relatively low α/β ratio; these findings warrant confirmation though. The role of whole pelvis irradiation is still controversial. Numerous randomized trials demonstrated a clinical benefit in terms of biochemical control, local and distant control, and overall survival from the addition of androgen suppression to external beam radiotherapy in intermediate- and high-risk patients. These studies typically included locally advanced (T3-T4) and poor-prognosis (Gleason score >7 and/or PSA >20 ng/mL) tumors and employed neoadjuvant/concomitant/adjuvant androgen suppression rather than only adjuvant setting. The ongoing trials will hopefully further define the role of endocrine treatment in more favorable risk patients and in the setting of the dose escalated radiotherapy. Brachytherapy (BRT) with permanent implants may be offered to low-risk patients (cT1-T2a, Gleason score <7, or 3 + 4, PSA ≤10 ng/mL), with prostate volume of ≤50 ml, no previous transurethral prostate resection and a good urinary function. Some recent data suggest a benefit from combining external beam irradiation and BRT for intermediate-risk patients. EBRT after radical prostatectomy improves disease-free survival and biochemical and local control rates in patients with positive surgical margins or pT3 tumors. Salvage radiotherapy may be considered at the time of biochemical failure in previously non-irradiated patients

[en] The purpose of this study was to evaluate whether the visualization of surgical clips (SCs) on the same set of planning computed tomography (CT) of breast cancer (BC) patients influences agreement on tumour bed (TB) delineation. Planning CT (CT_orig) of 47 BC patients with SCs to visualize the TB was processed in order to blur SCs and create a virtual CT (CT_mod). Four radiation oncologists (ROs, 2 juniors and 2 seniors) contoured TB on both the CT sets. Centre of mass distance (CMD), percentage overlap as Dice similarity coefficient (DSC), surface distance as average Hausdorff distance (AHD) and TB volume size were analysed. The intra-observer variability when contouring TB with and without SCs was statistically significant (p-values = 0.016, 0.0002 and ≪ 0.001 for CMD, AHD and DSC, respectively). Junior ROs showed worse reproducibility compared to seniors. The median DSC was < 0.7. The inter-observer variability with and without SCs was statistically significant (p < 0.001) for all metrics, with an increase of 48.7% in DSC and decrease of 50.7% and 57.1% in CMD and AHD, respectively, as relative median values, when SCs were visible. Regarding TB volumes, when SCs were visible, the intra-observer analysis revealed that 3/4 ROs delineated larger volumes, especially juniors. The inter-observer analysis showed that, in presence of visible SCs, the difference in TB volume among all the ROs fell from statistically significant to borderline significance (p = 0.052). TB contouring is confirmed to be an observer-dependent task. SCs decreased the intra and inter-observer variability but the overall agreement between ROs remained low.

[en] Highlights: • Italy has been slowly reopening after a 10 week lockdown following COVID-19 outbreak. • A survey was administered to Italian RT departments to depict the current scenario. • In P1, RT departments managed to contain contagion with modest workload reduction. • All safety measures, maintained in P2, allowed RT departments to restore activities. • Reorganization of activities will be crucial to prevent the second wave of pandemic.

[en] Radiomics is the high-throughput extraction of mineable and---possibly---reproducible quantitative imaging features from medical imaging. The aim of this work is to perform an unbiased bibliometric analysis on Radiomics 10 years after the first work became available, to highlight its status, pitfalls, and growing interest. Scopus database was used to investigate all the available English manuscripts about Radiomics. R Bibliometrix package was used for data analysis: a cumulative analysis of document categories, authors affiliations, country scientific collaborations, institution collaboration networks, keyword analysis, comprehensive of co-occurrence network, thematic map analysis, and 2021 sub-analysis of trend topics was performed. A total of 5623 articles and 16,833 authors from 908 different sources have been identified. The first available document was published in March 2012, while the most recent included was released on the 31st of December 2021. China and USA were the most productive countries. Co-occurrence network analysis identified five words clusters based on top 50 authors' keywords: Radiomics, computed tomography, radiogenomics, deep learning, tomography. Trend topics analysis for 2021 showed an increased interest in artificial intelligence (n = 286), nomogram (n = 166), hepatocellular carcinoma (n = 125), COVID-19 (n = 63), and X-ray computed (n = 60). Our work demonstrates the importance of bibliometrics in aggregating information that otherwise would not be available in a granular analysis, detecting unknown patterns in Radiomics publications, while highlighting potential developments to ensure knowledge dissemination in the field and its future real-life applications in the clinical practice. This work aims to shed light on the state of the art in radiomics, which offers numerous tangible and intangible benefits, and to encourage its integration in the contemporary clinical practice for more precise imaging analysis. ML-based bibliometric analysis is fundamental to detect unknown pattern of data in Radiomics publications. A raising interest in the field, the most relevant collaborations, keywords co-occurrence network, and trending topics have been investigated. Some pitfalls still exist, including the scarce standardization and the relative lack of homogeneity across studies.

[en] Multi atlas based segmentation (MABS) uses a database of atlas images, and an atlas selection process is used to choose an atlas subset for registration and voting. In the current state of the art, atlases are chosen according to a similarity criterion between the target subject and each atlas in the database. In this paper, we propose a new concept for atlas selection that relies on selecting the best performing group of atlases rather than the group of highest scoring individual atlases.

Experiments were performed using CT images of 50 patients, with contours of brainstem and parotid glands. The dataset was randomly split into two groups: 20 volumes were used as an atlas database and 30 served as target subjects for testing. Classic oracle selection, where atlases are chosen by the highest dice similarity coefficient (DSC) with the target, was performed. This was compared to oracle group selection, where all the combinations of atlas subgroups were considered and scored by computing DSC with the target subject. Subsequently, convolutional neural networks were designed to predict the best group of atlases. The results were also compared with the selection strategy based on normalized mutual information (NMI).

Oracle group was proven to be significantly better than classic oracle selection (p  <  10⁻⁵). Atlas group selection led to a median  ±  interquartile DSC of 0.740  ±  0.084, 0.718  ±  0.086 and 0.670  ±  0.097 for brainstem and left/right parotid glands respectively, outperforming NMI selection 0.676  ±  0.113, 0.632  ±  0.104 and 0.606  ±  0.118 (p  <  0.001) as well as classic oracle selection.

The implemented methodology is a proof of principle that selecting the atlases by considering the performance of the entire group of atlases instead of each single atlas leads to higher segmentation accuracy, being even better then current oracle strategy. This finding opens a new discussion about the most appropriate atlas selection criterion for MABS. (note)

[en] The diagnosis of breast cancer currently relies on radiological and clinical evaluation, confirmed by histopathological examination. However, such approach has some limitations as the suboptimal sensitivity, the long turnaround time for recall tests, the invasiveness of the procedure and the risk that some features of target lesions may remain undetected, making re-biopsy a necessity. Recent technological advances in the field of artificial intelligence hold promise in addressing such medical challenges not only in cancer diagnosis, but also in treatment assessment, and monitoring of disease progression. In the perspective of a truly personalised medicine, based on the early diagnosis and individually tailored treatments, two new technologies, namely radiomics and liquid biopsy, are rising as means to obtain information from diagnosis to molecular profiling and response assessment, without the need of a biopsied tissue sample. Radiomics works through the extraction of quantitative peculiar features of cancer from radiological data, while liquid biopsy gets the whole of the malignancy’s biology from something as easy as a blood sample. Both techniques hopefully will identify diagnostic and prognostic information of breast cancer potentially reducing the need for invasive (and often difficult to perform) biopsies and favouring an approach that is as personalised as possible for each patient. Nevertheless, such techniques will not substitute tissue biopsy in the near future, and even in further times they will require the aid of other parameters to be correctly interpreted and acted upon.

[en] Non-Small Cell Lung Cancer (NSCLC) is characterized by aggressiveness and includes the majority of thorax malignancies. The possibility of early stratification of patients as responsive and non-responsive to radiotherapy with a non-invasive method is extremely appealing. The distribution of the Fluorodeoxyglucose ("1"8F-FDG) in tumours, provided by Positron-Emission-Tomography (PET) images, has been proved to be useful to assess the initial staging of the disease, recurrence, and response to chemotherapy and chemo-radiotherapy (CRT). In the last years, particular efforts have been focused on the possibility of using ad interim "1"8F-FDG PET (FDG_i_n_t) to evaluate response already in the course of radiotherapy. However, controversial findings have been reported for various malignancies, although several results would support the use of FDG_i_n_t for individual therapeutic decisions, at least in some pathologies. The objective of the present review is to assemble comprehensively the literature concerning NSCLC, to evaluate where and whether FDG_i_n_t may offer predictive potential. Several searches were completed on Medline and the Embase database, combining different keywords. Original papers published in the English language from 2005 to 2016 with studies involving FDG_i_n_t in patients affected by NSCLC and treated with radiation therapy or chemo-radiotherapy only were chosen. Twenty-one studies out of 970 in Pubmed and 1256 in Embase were selected, reporting on 627 patients. Certainly, the lack of univocal PET parameters was identified as a major drawback, while standardization would be required for best practice. In any case, all these papers denoted FDG_i_n_t as promising and a challenging examination for early assessment of outcomes during CRT, sustaining its predictivity in lung cancer. (orig.)

[en] Purpose: Fatigue is an underevaluated cancer-related and treatment-related symptom. We analyzed fatigue in head and neck cancer patients undergoing radiotherapy (RT). Methods and Materials: A total of 117 patients were enrolled (mean age, 58 years). Radiation therapy (median dose, 66 Gy) was given with either exclusive or postoperative intent in 52 and 65 patients, respectively. Chemotherapy (CT) was added before and/or during RT in 61 patients. The patients completed a 20-item questionnaire (Multidimensional Fatigue Inventory [MFI-20]) before, during (weekly), and after RT. The impact of patient-, tumor-, and treatment-related factors on fatigue was evaluated with unifactorial and multifactorial tests. Results: Fatigue level increased during RT reaching a maximum at Week 6 and then slowly decreased. In multivariate stepwise regression analysis age (inversely related, p < 0.05), psychologic disorders (p < 0.005), and previous head-and-neck surgery (inversely related, p < 0.005) were correlated with higher pre-RT fatigue level. Pre-RT fatigue score (p < 0.0001), induction and/or concomitant CT (p = 0.035), need of cortisone during RT (p = 0.005), and thyroid disorders (p = 0.032) were correlated with higher during-RT fatigue level. Pre-RT fatigue score (p < 0.0001), induction and/or concomitant CT (p < 0.001), and need of cortisone during RT (p < 0.005) were correlated with higher post-RT fatigue level. No impact of gender, performance status, comorbidities other than psychologic and thyroid, tumor stage/site, RT intent, dose, volume, duration, or toxicity was observed. Conclusion: Fatigue affects all patients undergoing RT for head-and-neck cancer, reaches maximum score at the 6th week of RT, and slowly decreases thereafter. Age, thyroid dysfunction, psychologic disorders, pre-RT fatigue score, CT, and cortisone use are correlated with RT-related fatigue levels

[en] Brainstem radionecrosis is an important issue during the irradiation of tumors of the posterior fossa. The aim of the present study is to analyze postsurgical geometrical variations of tumor bed (TB) and brainstem (BS) and their impact on dosimetry. Retrospective collection of data from pediatric patients treated at a single institution. Availability of presurgical magnetic resonance imaging (MRI) was verified; availability of at least two postsurgical MRIs was considered a further inclusion criterion. The following metrics were analyzed: total volume, Dice similarity coefficient (DSC), and Haudsdorff distances (HD). Fourteen patients were available for the quantification of major postsurgical geometrical variations of TB. DSC, HD max, and HD average values were 0.47 (range: 0.08;0.76), 11.3 mm (7.7;24.5), and 2.6 mm (0.7;6.7) between the first and the second postoperative MRI, respectively. Postsurgical geometrical variations of the BS were also observed. Coverage to the TB was reduced in one patient (D95: -2.9 Gy), while D2 to the BS was increased for the majority of patients. Overall, predictive factors for significant geometrical changes were presurgical gross tumor volume (GTV) > 33 mL, hydrocephaly at diagnosis, Luschka foramen involvement, and younger age ($\le <!--\; ???\; -->$8 years). Major volume changes were observed in this cohort, with some dosimetric impact. The use of a recent co-registration MRI is advised. The 2-3 mm HD average observed should be considered in the planning target volume/planning organ at risk volume (PTV/PRV) margin and/or robust optimization planning. Results from wider efforts are needed to verify our findings.