Search Results (7)

Purpose: This paper aimed to identify dosimetric, clinical, and CT-based densitometric predictors of radiation-induced pulmonary events in breast cancer patients treated with moderately hypofractionated radiotherapy. Materials and Methods: A single-institution cohort of 1172 consecutive patients treated with 3D conformal whole-breast radiotherapy (40 Gy/15 fractions) before 2017 was analyzed. Ipsilateral lung DVHs and CT densitometry metrics were extracted. Clinical variables and cardiac calcification (CAC) scores (Agatston_score, CAC_volume, Max_HU_Heart) were included. Univariable and multivariable logistic regressions were performed; collinearity was assessed via Spearman correlation and VIF. Optimal thresholds were derived using the Youden index. Internal validation used bootstrap resampling. Results: After a median follow-up of 6.5 years, 18 patients developed moderate/severe pulmonary events. The univariable analysis showed associations with lung densitometric features (median/mean HU, 10th percentile, the lung volume with HU < −850 (V850)), V37 Gy, lung volume, and CAC scores. Lower lung HU values and larger lung volumes were linked to higher risk. The best models combined V850 (or lung volume) with a CAC metric. The model including V850 > 175 cc and continuous Max_HU_Heart achieved an optimism-corrected AUC of 0.68, with good fit and calibration (Hosmer–Lemeshow p = 0.33, R² = 0.847). Conclusions: The baseline cardiopulmonary status, captured by lung and heart densitometry, predicts pulmonary toxicity better than dosimetry. V850 > 175 cc was associated with a 4-fold higher risk, consistent with air trapping, known as a marker of emphysema. Full article

Background/Objectives: Pancreatic cancer is a very aggressive disease with a poor prognosis, even when diagnosed at an early stage. This study aimed to validate and refine a radiomic-based [¹⁸F]FDG-PET model to predict distant relapse-free survival (DRFS) in patients with unresectable locally advanced pancreatic cancer (LAPC). Methods: A Cox regression model incorporating two radiomic features (RFs) and cancer stage (III vs. IV) was temporally validated using a larger cohort (215 patients treated between 2005–2022). Patients received concurrent chemoradiotherapy with capecitabine and hypo-fractionated Intensity Modulated Radiotherapy (IMRT). Data were split into training (145 patients, 2005–2017) and validation (70 patients, 2017–2022) groups. Seventy-eight RFs were extracted, harmonized, and analyzed using machine learning to develop refined models. Results: The model incorporating Statistical-Percentile10, Morphological-ComShift, and stage demonstrated moderate predictive accuracy (training: C-index = 0.632; validation: C-index = 0.590). When simplified to include only Statistical-Percentile10, performance improved slightly in the validation group (C-index = 0.601). Adding GLSZM3D-grayLevelVariance to Statistical-Percentile10, while excluding Morphological-ComShift, further enhanced accuracy (training: C-index = 0.654; validation: C-index = 0.623). Despite these refinements, all versions showed similar moderate ability to stratify patients into risk classes. Conclusions: [¹⁸F]FDG-PET radiomic features are robust predictors of DRFS after chemoradiotherapy in LAPC. Despite moderate performance, these models hold promise for patient risk stratification. Further validation with external cohorts is ongoing. Full article

In the field of conservation and protection of heritage buildings, knowledge plays a fundamental role, emphasized by national and international rules and regulations. This aspect becomes fundamental when conducting the structural assessment of a historical building. This study envisaged a cognitive phase via the application of advanced survey and diagnostic methodologies to define the materials, construction techniques, and state of conservation of the structural system of a specific building forming part of Florence’s heritage. The information complex produced formed the basis for the structural assessment and for the experimentation of the BIM methodology within the creation of databases for the management of cognitive processes of historical buildings. The case study is one of the gates of the last circle of walls of the 14th century and is the only one that has maintained its original height, despite modifications: the gate/tower of San Niccolò. The research conducted, in addition to achieving a structural assessment of the tower, has allowed the creation of a dynamic model for organizing and consulting the information, laying the groundwork for the creation of a conservation and maintenance plan. Full article

Purpose. Different ML models were compared to predict toxicity in RT on a large cohort (n = 1314). Methods. The endpoint was RTOG G2/G3 acute toxicity, resulting in 204/1314 patients with the event. The dataset, including 25 clinical, anatomical, and dosimetric features, was split into 984 for training and 330 for internal tests. The dataset was standardized; features with a high p-value at univariate LR and with Spearman $\rho >0.8$ were excluded; synthesized data of the minority were generated to compensate for class imbalance. Twelve ML methods were considered. Model optimization and sequential backward selection were run to choose the best models with a parsimonious feature number. Finally, feature importance was derived for every model. Results. The model’s performance was compared on a training–test dataset over different metrics: the best performance model was LightGBM. Logistic regression with three variables (LR3) selected via bootstrapping showed performances similar to the best-performing models. The AUC of test data is slightly above 0.65 for the best models (highest value: 0.662 with LightGBM). Conclusions. No model performed the best for all metrics: more complex ML models had better performances; however, models with just three features showed performances comparable to the best models using many (n = 13–19) features. Full article

H-BIM paradigms are constituted by 3-D informative contents documenting the current and past state of existing structures. Although the transversal vision and the multidisciplinary interpretation have been attributed to BIM models, in the field of monumental structures, these databases can be organized in different ways depending on the adopted protocol and classifications for the required output of the work. In this manuscript, an H-BIM approach targeted at collecting and providing useful information to execute seismic vulnerability analyses of monumental structures is presented. The BIM modeling followed a protocol based on the following steps: geometrical acquisitions, scan-to-BIM modeling, and informative data collection. The methodology has been applied to the oldest part of Palazzo Vecchio in Florence (IT), an important monumental masonry structure representing the political headquarter of the city since the Middle Ages. The parametric modeling was realized classifying the information according to structural perspectives based on the cognitive steps for the investigation of the existing structures. Finally, a seismic assessment has been realized through a simplified procedure developed for cultural heritage buildings. The outcomes of the evaluation are still part of the collected information of the H-BIM model, as an example of continuous improvement of the available contents of the database. Full article

Assessment and evaluation of damage in cultural heritage structures are conducted primarily using nondestructive and noncontact methods. One common deployment is laser scanners or ground-based lidar scanners that produce a point cloud containing information at the centimeter to the millimeter level. This type of data allows for detecting surface damage, defects, cracks, and other anomalies based only on geometric surface descriptors using a single dataset, which does not rely on a change detection approach. Moreover, geometric features are not influenced by color, which is essential for heritage structures because they are nonuniform in color due to anthropologic and environmental effects (e.g., painting or moisture). In this work, a damage detection method developed based on local geometric features is evaluated and expanded for crack detection within the example fresco walls of Sala degli Elementi in the Palazzo Vecchio. The workflow’s performance is then compared in a qualitative manner to that of manual crack mapping results identified using images. Full article

For preservation efforts and stability assessment of historic structures it is imperative to understand the extent of existing damages and possible modes for how they could have occurred. The aim of this work is to illustrate the importance of integrating documentation, non-destructive testing, and numerical modeling for damage assessment of heritage structures. In particular, this work explores the synthesis of these techniques on a plastered masonry wall in Palazzo Vecchio. Laser scanning was used to capture the geometry of the wall while terrestrial photogrammetry and high-resolution images were used to document the magnitude of cracking in the plaster layer. High resolution thermal images were used to document the distribution of stones and additional cracks not visible through the plaster layer. The results of documentation and non-destructive testing were used to generate an as-built model for structural analysis. Finite distinct element modeling was used to simulate the response of the wall to a series of loading conditions. By comparing the results of simulation to existing crack patterns, theories for how the damage occurred were generated. Full article