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Aim: The aim of this study was to develop robust prognostic models for mortality prediction of COVID-19 patients, applicable to different sets of real scenarios, using radiomic and neural network features extracted from chest X-rays (CXRs) with a certified and commercially available software. Methods: 1816 patients from 5 different hospitals in the Province of Reggio Emilia were included in the study. Overall, 201 radiomic features and 16 neural network features were extracted from each COVID-19 patient’s radiography. The initial dataset was balanced to train the classifiers with the same number of dead and survived patients, randomly selected. The pipeline had three main parts: balancing procedure; three-step feature selection; and mortality prediction with radiomic features through three machine learning (ML) classification models: AdaBoost (ADA), Quadratic Discriminant Analysis (QDA) and Random Forest (RF). Five evaluation metrics were computed on the test samples. The performance for death prediction was validated on both a balanced dataset (Case 1) and an imbalanced dataset (Case 2). Results: accuracy (ACC), area under the ROC-curve (AUC) and sensitivity (SENS) for the best classifier were, respectively, 0.72 ± 0.01, 0.82 ± 0.02 and 0.84 ± 0.04 for Case 1 and 0.70 ± 0.04, 0.79 ± 0.03 and 0.76 ± 0.06 for Case 2. These results show that the prediction of COVID-19 mortality is robust in a different set of scenarios. Conclusions: Our large and varied dataset made it possible to train ML algorithms to predict COVID-19 mortality using radiomic and neural network features of CXRs.

Concurrent chemotherapy and radiotherapy (cCRT) is considered the standard treatment of locally advanced non-small cell lung cancer (LA-NSCLC). Unfortunately, management is still heterogeneous across different specialists. A multidisciplinary approach is needed in this setting due to recent, promising results obtained by consolidative immunotherapy. The aim of this survey is to assess current LA-NSCLC management in Italy. From January to April 2018, a 15-question survey focusing on diagnostic/therapeutic LA-NSCLC management was sent to 1,478 e-mail addresses that belonged to pneumologists, thoracic surgeons, and radiation and medical oncologists. 421 answers were analyzed: 176 radiation oncologists, 86 medical oncologists, 92 pneumologists, 64 thoracic surgeons and 3 other specialists. More than a half of the respondents had been practicing for >10 years after completing residency training. Some discrepancies were observed in clinical LA-NSCLC management: the lack of a regularly planned multidisciplinary tumor board, the use of upfront surgery in multistation stage IIIA, and territorial diffusion of cCRT in unresectable LA-NSCLC. Our analysis demonstrated good compliance with international guidelines in the diagnostic workup of LA-NSCLC. We observed a relationship between high clinical experience and good clinical practice. A multidisciplinary approach is mandatory for managing LA-NSCLC.

Despite the availability of national and international guidelines, an accurate and efficient, patient-specific, deformable image registration (DIR) validation methodology is not yet established, and several groups have found an incompatibility of the various digital phantoms with the commercial systems. To evaluate the quality of the computed tomography (CT) and on-board cone-beam CT (CBCT) DIRs, a novel methodology was developed and tested on 10 head and neck (HN) patients, using CT and CBCT anthropomorphic HN phantom images, digitally reprocessed to include the common organs at risk. Reference DVFs (refDVFs) were generated from the clinical patient CT-CBCT fused images using an independent registration software. The phantom CT images were artificially deformed, using the refDVFs, and registered with the phantom CBCT images, using the clinical registration software, generating a test DVF (testDVF) dataset. The clinical plans were recalculated on the daily patient ‘deformed’ CTs, and the dose maps transferred to the patient-planning CT, using both the refDVF and testDVF. The spatial and dosimetric errors were quantified and the DIR performance evaluated using an established operative tolerance level. The method showed the ability to quantify the DIR spatial errors and assess their dose impact at the voxel level and could be applied to patient-specific DIR evaluation during adaptive HN radiotherapy in routine practice.

Background In patients with head-neck cancer treated with IMRT, immobility of the upper part of the body during radiation is maintained by means of customised immobilisation devices. The main purpose of this study was to determine how the procedures for preparation of customised immobilisation systems and the patients characteristics influence the extent of setup errors. Methods A longitudinal, prospective study involving 29 patients treated with IMRT. Data were collected before CT simulation and during all the treatment sessions (528 setup errors analysed overall); the correlation with possible risk factors for setup errors was explored using a linear mixed model. Results Setup errors were not influenced by the patient’s anxiety and pain. Temporary removal of the thermoplastic mask before carrying out the CT simulation shows statistically borderline, clinically relevant, increase of setup errors (+24.7%, 95% CI: −0.5% - 55.8%). Moreover, a unit increase of radiation therapists who model the customised thermoplastic mask is associated to a −18% (−29.2% - -4.9%) reduction of the errors. The setup error is influenced by the patient’s physical features; in particular, it increases both in patients in whom the treatment position is obtained with ‘Shoulder down’ (+27.9%, 2.2% - 59.7%) and in patients with ‘Scoliosis/kyphosis’ problems (+65.4%, 2.3% - 164.2%). Using a ‘Small size standard plus customized neck support device’ is associated to a −52.3% (−73.7% - -11.2%) reduction. The increase in number of radiation therapists encountered during the entire treatment cycle does not show associations. Increase in the body mass index is associated with a slight reduction in setup error by (−2.8%, −5% - -0.7%). Conclusion The position of the patient obtained by forcing the shoulders downwards, clinically significant scoliosis or kyphosis and the reduction of the number of radiation therapists who model the thermoplastic mask are found to be statistically significant risk factors that can cause an increase in setup errors, while the use of ‘Small size’ neck support device and patient BMI can diminish them.

Background18F-FDG PET/CT imaging allows to study oncological patients and their relative diagnosis through the standardised uptake value (SUV) evaluation. During radiopharmaceutical injection, an extravasation event may occur, making the SUV value less accurate and possibly leading to severe tissue damage. The study aimed to propose a new technique to monitor and manage these events, to provide an early evaluation and correction to the estimated SUV value through a SUV correction coefficient.MethodsA cohort of 70 patients undergoing 18F- FDG PET/CT examinations was enrolled. Two portable detectors were secured on the patients' arms. The dose-rate (DR) time curves on the injected DRin and contralateral DRcon arm were acquired during the first 10 min of injection. Such data were processed to calculate the parameters ΔpinNOR = (DRinmax- DRinmean)/DRinmax and ΔRt = (DRin(t) − DRcon(t)), where DRinmax is the maximum DR value, DRinmean is the average DR value in the injected arm. OLINDA software allowed dosimetric estimation of the dose in the extravasation region. The estimated residual activity in the extravasation site allowed the evaluation of the SUV's correction value and to define an SUV correction coefficient.ResultsFour cases of extravasations were identified for which ΔRt [(390 ± 26) µSv/h], while ΔRt [(150 ± 22) µSv/h] for abnormal and ΔRt [(24 ± 11) µSv/h] for normal cases. The ΔpinNOR showed an average value of (0.44 ± 0.05) for extravasation cases and an average value of (0.91 ± 0.06) and (0.77 ± 0.23) in normal and abnormal classes, respectively. The percentage of SUV reduction (SUV%CR) ranges between 0.3% and 6%. The calculated self-tissue dose values range from 0.027 to 0.573 Gy, according to the segmentation modality. A similar correlation between the inverse of ΔpinNOR and the normalised ΔRt with the SUV correction coefficient was found.ConclusionsThe proposed metrics allowed to characterised the extravasation events in the first few minutes after the injection, providing an early SUV correction when necessary. We also assume that the characterisation of the DR-time curve of the injection arm is sufficient for the detection of extravasation events. Further validation of these hypotheses and key metrics is recommended in larger cohorts.

The most prevalent and deadly primary malignant glioma in adults is glioblastoma (GBM), which has a median survival time of about 15 months. Despite the standard of care for glioblastoma, which includes gross total resection, high-dose radiation, and temozolomide chemotherapy, this tumor is still one of the most aggressive and difficult to treat. So, it is critical to find more potent therapies that can help glioblastoma patients have better clinical outcomes. Additionally, the prognosis for recurring malignant gliomas is poor, necessitating the need for innovative therapeutics. Immunotherapy is a rather new treatment for glioblastoma and its effects are not well studied when it is combined with standard chemoradiation therapy. We conducted this study to evaluate different glioblastoma immunotherapy approaches in terms of feasibility, efficacy, and safety. We conducted a computer-assisted literature search of electronic databases for essays that are unique, involve either prospective or retrospective research, and are entirely written and published in English. We examined both observational data and randomized clinical trials. Eighteen studies met the criteria for inclusion. In conclusion, combining immunotherapy with radiochemotherapy and tumor removal is generally possible and safe, and rather effective in the prolongation of survival measures.

Several models have been developed to predict mortality in patients with COVID-19 pneumonia, but only a few have demonstrated enough discriminatory capacity. Machine learning algorithms represent a novel approach for the data-driven prediction of clinical outcomes with advantages over statistical modeling. We aimed to develop a machine learning-based score-the Piacenza score-for 30-day mortality prediction in patients with COVID-19 pneumonia. The study comprised 852 patients with COVID-19 pneumonia, admitted to the Guglielmo da Saliceto Hospital in Italy from February to November 2020. Patients' medical history, demographics, and clinical data were collected using an electronic health record. The overall patient data set was randomly split into derivation and test cohorts. The score was obtained through the naïve Bayes classifier and externally validated on 86 patients admitted to Centro Cardiologico Monzino (Italy) in February 2020. Using a forward-search algorithm, 6 features were identified: age, mean corpuscular hemoglobin concentration, PaO /FiO ratio, temperature, previous stroke, and gender. The Brier index was used to evaluate the ability of the machine learning model to stratify and predict the observed outcomes. A user-friendly website was designed and developed to enable fast and easy use of the tool by physicians. Regarding the customization properties of the Piacenza score, we added a tailored version of the algorithm to the website, which enables an optimized computation of the mortality risk score for a patient when some of the variables used by the Piacenza score are not available. In this case, the naïve Bayes classifier is retrained over the same derivation cohort but using a different set of patient characteristics. We also compared the Piacenza score with the 4C score and with a naïve Bayes algorithm with 14 features chosen a priori. The Piacenza score exhibited an area under the receiver operating characteristic curve (AUC) of 0.78 (95% CI 0.74-0.84, Brier score=0.19) in the internal validation cohort and 0.79 (95% CI 0.68-0.89, Brier score=0.16) in the external validation cohort, showing a comparable accuracy with respect to the 4C score and to the naïve Bayes model with a priori chosen features; this achieved an AUC of 0.78 (95% CI 0.73-0.83, Brier score=0.26) and 0.80 (95% CI 0.75-0.86, Brier score=0.17), respectively. Our findings demonstrated that a customizable machine learning-based score with a purely data-driven selection of features is feasible and effective for the prediction of mortality among patients with COVID-19 pneumonia.

PurposeIn the light of projected climate change impacts on buildings and their occupants, climate change adaptation for built environment to climate change is crucial. The risk of overheating is a key concern, particularly given its effect on heat-related health problems for elderly people. The purpose of this paper is to propose, test, and evaluate the strategies for climate change adaptation to minimise present and future risks of overheating for a new purpose-built care home and extra care accommodation near York.Design/methodology/approachThe overheating risk was assessed through dynamic simulations, using probabilistic projections for 2030s, 2050s and 2080s. Suitable adaptation measures were tested and compared using industry metrics. A stakeholders’ workshop compared the relative effectiveness of the identified measures and made a broader evaluation using defined criteria. Highest-ranked measures were combined into “adaptation packages” in order to populate adaptation timelines for the project.FindingsResults show that the original design presents a severe overheating risk. Increasing thermal mass and slightly improving ventilation are adequate for the 2030s; however solar shading and further improvements of ventilation are necessary for the 2050s. The stress test revealed that even the most effective passive measures combined would be insufficient to maintain comfortable conditions by the 2080s, and mechanical cooling would be needed.Originality/valueThe comparative analysis of adaptation measures using normalised CIBSE TM52 criteria improved risk communication and engagement with the client and the design team. The integration of quantitative and qualitative evaluation criteria led to an appropriate and timely strategy for adaptation.

Background. The RITMIA™ app (Heart Sentinel™, Parma, Italy) is a novel application that combined with a wearable consumer-grade chest-strap Bluetooth heart rate monitor, provides automated detection of atrial fibrillation (AF), and may be promising for sustainable AF screening programs, since it is known that prolonged monitoring leads to increased AF diagnosis. Objective. The purpose of this study was to examine whether RITMIA™ could accurately differentiate sinus rhythm (SR) from AF compared with gold-standard physician-interpreted 12-lead electrocardiogram (ECG). Design. In this observational prospective study consecutive patients presenting for elective cardioversion (ECV) of AF, from November 2017 to November 2018, were enrolled. Patients underwent paired 12-lead ECG and RITMIA™ recording, both before and after ECV procedure. The RITMIA™ automated interpretation was compared with 12-lead ECG interpreted by the agreement of two cardiologists. The latter were blinded to the results of the App automated diagnosis. Feasibility, sensitivity, specificity, and K coefficient for RITMIA™ automated diagnosis were calculated. Results. A total of 100 consecutive patients were screened and enrolled. Five patients did not undergo ECV due to spontaneous restoration of SR. 95 patients who actually underwent ECV were included in the final analysis. Mean age was 66.2±10.7 years; female patients were 20 (21.1%). There were 190 paired ECGs and RITMIA™ recordings. The RITMIA™ app correctly detected AF with 97% sensitivity, 95.6% specificity, and a K coefficient of 0.93. Conclusions. The automated RITMIA™ algorithm very accurately differentiated AF from SR before and after elective ECV. The only hardware required by this method is a cheap consumer-grade Bluetooth heart rate monitor of the chest-strap type. This robust and affordable RITMIA™ technology could be used to conduct population-wide screening in patients at risk for silent AF, thanks to the long-term monitoring applicability.

BackgroundThe rates of local failure after curative radiotherapy for prostate cancer (PC) remain high despite more accurate locoregional treatments available, with one third of patients experiencing biochemical failure and clinical relapse occurring in 30–47% of cases. Today, androgen deprivation therapy (ADT) is the treatment of choice in this setting, but with not negligible toxicity and low effects on local disease. Therefore, the treatment of intraprostatic PC recurrence represents a challenge for radiation oncologists. Prostate reirradiation (Re-I) might be a therapeutic possibility. We present our series of patients treated with salvage stereotactic Re‑I for intraprostatic recurrence of PC after radical radiotherapy, with the aim of evaluating feasibility and safety of linac-based prostate Re‑I.Materials and methodsWe retrospectively evaluated toxicities and outcomes of patients who underwent salvage reirradiation using volumetric modulated arc therapy (VMAT) for intraprostatic PC recurrence. Inclusion criteria were age ≥ 18 years, histologically proven diagnosis of PC, salvage Re‑I for intraprostatic recurrence after primary radiotherapy for PC with curative intent, concurrent/adjuvant ADT with stereotactic body radiation therapy (SBRT) allowed, performance status ECOG 0–2, restaging choline/PSMA-PET/TC and prostate MRI after biochemical recurrence, and signed informed consent.ResultsFrom January 2019 to April 2022, 20 patients were recruited. Median follow-up was 26.7 months (range 7–50). After SBRT, no patients were lost at follow-up and all are still alive. One- and 2‑year progression free survival (PFS) was 100% and 81.5%, respectively, while 2‑year biochemical progression-free survival (bFFS) was 88.9%. Four patients (20%) experienced locoregional lymph node progression and were treated with a further course of SBRT. Prostate reirradiation allowed the ADT start to be postponed for 12–39 months. Re‑I was well tolerated by all patients and none discontinued the treatment. No cases of ≥ G3 genitourinary (GU) or gastrointestinal (GI) toxicity were reported. Seven (35%) and 2 (10%) patients experienced acute G1 and G2 GU toxicity, respectively. Late GU toxicity was recorded in 10 (50%) patients, including 8 (40%) G1 and 2 (10%) G2. ADT-related side effects were found in 7 patients (hot flashes and asthenia).ConclusionLinac-based SBRT is a safe technique for performing Re‑I for intraprostatic recurrence after primary curative radiotherapy for PC. Future prospective, randomized studies are desirable to better understand the effectiveness of reirradiation and the still open questions in this field.