Application of nnU-Net for Automatic Segmentation of Lung Lesions on CT Images and Its Implication for Radiomic Models.

Radiomics investigates the predictive role of quantitative parameters calculated from radiological images. In oncology, tumour segmentation constitutes a crucial step of the radiomic workflow. Manual segmentation is time-consuming and prone to inter-observer variability. In this study, a state-of-the-art deep-learning network for automatic segmentation (nnU-Net) was applied to computed tomography images of lung tumour patients, and its impact on the performance of survival radiomic models was assessed. In total, 899 patients were included, from two proprietary and one public datasets. Different network architectures (2D, 3D) were trained and tested on different combinations of the datasets. Automatic segmentations were compared to reference manual segmentations performed by physicians using the DICE similarity coefficient. Subsequently, the accuracy of radiomic models for survival classification based on either manual or automatic segmentations were compared, considering both hand-crafted and deep-learning features. The best agreement between automatic and manual contours (DICE = 0.78 ± 0.12) was achieved averaging 2D and 3D predictions and applying customised post-processing. The accuracy of the survival classifier (ranging between 0.65 and 0.78) was not statistically different when using manual versus automatic contours, both with hand-crafted and deep features. These results support the promising role nnU-Net can play in automatic segmentation, accelerating the radiomic workflow without impairing the models' accuracy. Further investigations on different clinical endpoints and populations are encouraged to confirm and generalise these findings

Magnetic resonance mammography in the evaluation of recurrence at the prior lumpectomy site after conservative surgery and radiotherapy

INTRODUCTION: The aim was to assess the value of magnetic resonance mammography (MRM) in the detection of recurrent breast cancer on the prior lumpectomy site in patients with previous conservative surgery and radiotherapy. METHODS: Between April 1999 and July 2003, 93 consecutive patients with breast cancer treated with conservative surgery and radiotherapy underwent MRM, when a malignant lesion on the site of lumpectomy was suspected by ultrasound and/or mammography. MRM scans were evaluated by morphological and dynamic characteristics. MRM diagnosis was compared with histology or with a 36-month imaging follow-up. Enhancing areas independent of the prior lumpectomy site, incidentally detected during the MRM, were also evaluated. RESULTS: MRM findings were compared with histology in 29 patients and with a 36-month follow-up in 64 patients. MRM showed 90% sensitivity, 91.6% specificity, 56.3% positive predictive value and 98.7% negative predictive value for detection of recurrence on the surgical scar. MRM detected 13 lesions remote from the scar. The overall sensitivity, specificity, positive predictive value and negative predictive value of MRM for detection of breast malignancy were 93.8%, 90%, 62.5% and 98.8%, respectively. CONCLUSION: MRM is a sensitive method to differentiate recurrence from post-treatment changes at the prior lumpectomy site after conservative surgery and radiation therapy. The high negative predictive value of this technique can avoid unnecessary biopsies or surgical treatments

CT exposure in adult and paediatric patients: A review of the mechanisms of damage, relative dose and consequent possible risks

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Measurements of temperature increase induced on a tissue-mimicking material by a clinical US-guided HIFU system

A framework for the evaluation of temperature increase in a tissue-mimicking material (TMM) induced by a clinical Ultrasound-guided High Intensity Focused Ultrasound (US-guided HIFU) system was developed. HIFU procedures are minimally invasive treatments that achieve deep tumor ablation, with the sparing of normal tissues, through thermal or mechanical effects induced by a HIFU beam generated with a focused transducer. Temperature evaluation is therefore crucial for the assurance of patient safety and treatment effectiveness. Nevertheless, it is a very difficult task on the US-guided systems, where high-pressure fields are involved. As far as we know, this study is the first attempt of temperature evaluation on a clinical US-guided HIFU system. Temperature evaluation was performed at typical clinical settings (between 80 W and 400 W, for 3s sonications) by the use of needle thermocouples connected to a voltmeter and inserted in a polyacrylamide gel phantom, prepared in-house to reproduce soft tissue behavior. Data sampling was performed with the use of acquisition software developed with LabView, while US-imaging was used to verify the position of the thermocouple. Typical rising curves of temperature were recovered, and rapid decrease was found when the HIFU field turned off. The highest temperature increases were concentrated inside the geometrical focus and were higher than 55 Celsius degrees at all power outputs. Repetition of measurements was not possible after sonications at the highest power outputs (400W). The absolute temperature of 98 Celsius degrees was never exceeded

HeLLePhant: A phantom mimicking non-small cell lung cancer for texture analysis in CT images

Purpose: Phantoms mimicking human tissue heterogeneity and intensity are required to establish radiomic features robustness in Computed Tomography (CT) images. We developed inserts with two different techniques for the radiomic study of Non-Small Cell Lung Cancer (NSCLC) lesions. Methods: We developed two insert prototypes: two 3D-printed made of glycol-modified polyethylene terephthalate (PET-G), and nine with sodium polyacrylate plus iodinated contrast medium. The inserts were put in a handcraft phantom (HeLLePhant). We also analysed four materials of a commercial homogeneous phantom (Catphan® 424) and collected 29 NSCLC patients for comparison. All the CT acquisitions were performed with the same clinical protocol and scanner at 120kVp. The HeLLePhant phantom was scanned ten times in fixed condition at 120kVp and 100kVp for repeatability investigation. We extracted 153 radiomic features using Pyradiomics. To compare the features between phantoms and patients, we computed how many phantom features fell in the range between 10th and 90th percentile of the corresponding patient values. We deemed repeatable the features with a coefficient of variation (CV) less than or equal to 0.10. Results: The best similarity with the patients was obtained with the polyacrylate inserts (55.6–90.2%), the worst with Catphan (15.7–19.0%). For the PET-G inserts 35.3% and 36.6% of the features match the patient range. We found high repeatability for all the inserts of the HeLLePhant phantom (74.3–100% at 120kVp, 75.7–97.9% at 100kVp), and observed a texture dependency in repeatability. Conclusions: Our study shows a promising way to construct heterogeneous inserts mimicking a target tissue for radiomic studies

Discrimination of Tumor Texture Based on MRI Radiomic Features: Is There a Volume Threshold? A Phantom Study

Radiomics is emerging as a promising tool to extract quantitative biomarkers—called radiomic features—from medical images, potentially contributing to the improvement in diagnosis and treatment of oncological patients. However, technical limitations might impair the reliability of radiomic features and their ability to quantify clinically relevant tissue properties. Among these, sampling the image signal in a too-small region can reduce the ability to discriminate tissues with different properties. However, a volume threshold guaranteeing a reliable analysis, which might vary according to the imaging modality and clinical scenario, has not been assessed yet. In this study, an MRI phantom specifically developed for radiomic investigation of gynecological malignancies was used to explore how the ability of radiomic features to discriminate different image textures varies with the volume of the analyzed region. The phantom, embedding inserts with different textures, was scanned on two 1.5T and one 3T scanners, each using the T2-weighted sequence of the clinical protocol implemented for gynecological studies. Within each of the three inserts, six cylindrical regions were drawn with volumes ranging from 0.8 cm3 to 29.8 cm3, and 944 radiomic features were extracted from both original images and from images processed with different filters. For each scanner, the ability of each feature to discriminate the different textures was quantified. Despite differences observed among the scanner models, the overall percentage of discriminative features across scanners was >70%, with the smallest volume having the lowest percentage of discriminative features for all scanners. Stratification by feature class, still aggregating data for original and filtered images, showed statistical significance for the association between the percentage of discriminative features with VOI sizes for features classes GLCM, GLDM, and GLSZM on the first 1.5T scanner and for first-order and GLSZM classes on the second 1.5T scanner. Poorer results in terms of features’ discriminative ability were found for the 3T scanner. Focusing on original images only, the analysis of discriminative features stratified by feature class showed that the first-order and GLCM were robust to VOI size variations (>85% discriminative features for all sizes), while for the 1.5T scanners, the GLSZM and NGTDM feature classes showed a percentage of discriminative features >80% only for volumes no smaller than 3.3 cm3, and equal or larger than 7.4 cm3 for the GLRLM. As for the 3T scanner, only the GLSZM showed a percentage of discriminative features >80% for all volume sizes above 3.3 cm3. Analogous considerations were obtained for each filter, providing useful indications for feature selection in this clinical case. Similar studies should be replicated with suitably adapted phantoms to derive useful data for other clinical scenarios and imaging modalities

Metastatic and non-metastatic lymph nodes: quantification and different distribution of iodine uptake assessed by dual-energy CT

OBJECTIVES: To evaluate quantification of iodine uptake in metastatic and non-metastatic lymph nodes (LNs) by dual-energy CT (DECT) and to assess if the distribution of iodine within LNs at DECT correlates with the pathological structure. METHODS: Ninety LNs from 37 patients (23 with lung and 14 with gynaecological malignancies) were retrospectively selected. Information of LNs sent for statistical analysis included Hounsfield units (HU) at different energy levels; decomposition material densities fat-iodine, iodine-fat, iodine-water, water-iodine. Statistical analysis included evaluation of interobserver variability, material decomposition densities and spatial HU distribution within LNs. RESULTS: Interobserver agreement was excellent. There was a significant difference in iodine-fat and iodine-water decompositions comparing metastatic and non-metastatic LNs (p < 0.001); fat-iodine and water-iodine did not show significant differences. HU distribution showed a significant gradient from centre to periphery within non-metastatic LNs that was significant up to 20-30% from the centre, whereas metastatic LNs showed a more homogeneous distribution of HU, with no significant gradient. CONCLUSIONS: DECT demonstrated a lower iodine uptake in metastatic compared to non-metastatic LNs. Moreover, the internal iodine distribution showed an evident gradient of iodine distribution from centre to periphery in non-metastatic LNs, and a more homogeneous distribution within metastatic LNs, which corresponded to the pathological structure. KEY POINTS: • This study demonstrated a lower iodine uptake in metastatic than non-metastatic LNs. • Internal distribution of HU was different between metastatic and non-metastatic lymph nodes. • The intranodal iodine distribution disclosed a remarkable correlation with the histological LN structure