Advancements in Standardizing Radiological Reports: A Comprehensive Review.

Standardized radiological reports stimulate debate in the medical imaging field. This review paper explores the advantages and challenges of standardized reporting. Standardized reporting can offer improved clarity and efficiency of communication among radiologists and the multidisciplinary team. However, challenges include limited flexibility, initially increased time and effort, and potential user experience issues. The efforts toward standardization are examined, encompassing the establishment of reporting templates, use of common imaging lexicons, and integration of clinical decision support tools. Recent technological advancements, including multimedia-enhanced reporting and AI-driven solutions, are discussed for their potential to improve the standardization process. Organizations such as the ACR, ESUR, RSNA, and ESR have developed standardized reporting systems, templates, and platforms to promote uniformity and collaboration. However, challenges remain in terms of workflow adjustments, language and format variability, and the need for validation. The review concludes by presenting a set of ten essential rules for creating standardized radiology reports, emphasizing clarity, consistency, and adherence to structured formats.

Radiomic Features Applied to Contrast Enhancement Spectral Mammography: Possibility to Predict Breast Cancer Molecular Subtypes in a Non-Invasive Manner.

We aimed to investigate the association between the radiomic features of contrast-enhanced spectral mammography (CESM) images and a specific receptor pattern of breast neoplasms. In this single-center retrospective study, we selected patients with neoplastic breast lesions who underwent CESM before a biopsy and surgical assessment between January 2013 and February 2022. Radiomic analysis was performed on regions of interest selected from recombined CESM images. The association between the features and each evaluated endpoint (ER, PR, Ki-67, HER2+, triple negative, G2-G3 expressions) was investigated through univariate logistic regression. Among the significant and highly correlated radiomic features, we selected only the one most associated with the endpoint. From a group of 321 patients, we enrolled 205 malignant breast lesions. The median age at the exam was 50 years (interquartile range (IQR) 45-58). NGLDM_Contrast was the only feature that was positively associated with both ER and PR expression (p-values = 0.01). NGLDM_Coarseness was negatively associated with Ki-67 expression (p-value = 0.02). Five features SHAPE Volume(mL), SHAPE_Volume(vx), GLRLM_RLNU, NGLDM_Busyness and GLZLM_GLNU were all positively and significantly associated with HER2+; however, all of them were highly correlated. Radiomic features of CESM images could be helpful to predict particular molecular subtypes before a biopsy.

A multicenter study on radiomic features from T2 -weighted images of a customized MR pelvic phantom setting the basis for robust radiomic models in clinics.

PURPOSE: To investigate the repeatability and reproducibility of radiomic features extracted from MR images and provide a workflow to identify robust features. METHODS: T2 -weighted images of a pelvic phantom were acquired on three scanners of two manufacturers and two magnetic field strengths. The repeatability and reproducibility of features were assessed by the intraclass correlation coefficient and the concordance correlation coefficient, respectively, and by the within-subject coefficient of variation, considering repeated acquisitions with and without phantom repositioning, and with different scanner and acquisition parameters. The features showing intraclass correlation coefficient or concordance correlation coefficient >0.9 were selected, and their dependence on shape information (Spearman's ρ > 0.8) analyzed. They were classified for their ability to distinguish textures, after shuffling voxel intensities of images. RESULTS: From 944 two-dimensional features, 79.9% to 96.4% showed excellent repeatability in fixed position across all scanners. A much lower range (11.2% to 85.4%) was obtained after phantom repositioning. Three-dimensional extraction did not improve repeatability performance. Excellent reproducibility between scanners was observed in 4.6% to 15.6% of the features, at fixed imaging parameters. In addition, 82.4% to 94.9% of the features showed excellent agreement when extracted from images acquired with echo times 5 ms apart, but decreased with increasing echo-time intervals, and 90.7% of the features exhibited excellent reproducibility for changes in pulse repetition time. Of nonshape features, 2.0% was identified as providing only shape information. CONCLUSION: We showed that radiomic features are affected by MRI protocols and propose a general workflow to identify repeatable, reproducible, and informative radiomic features to ensure robustness of clinical studies.

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.

Radiomics of high-grade serous ovarian cancer: association between quantitative CT features, residual tumour and disease progression within 12 months.

OBJECTIVES: To determine if radiomic features, alone or combined with clinical data, are associated with residual tumour (RT) at surgery, and predict the risk of disease progression within 12 months (PD12) in ovarian cancer (OC) patients. METHODS: This retrospective study enrolled 101 patients according to the following inclusion parameters: cytoreductive surgery performed at our institution (9 May 2007-23 February 2016), assessment of BRCA mutational status, preoperative CT available. Radiomic features of the ovarian masses were extracted from 3D structures drawn on CT images. A phantom experiment was performed to assess the reproducibility of radiomic features. The final radiomic features included in the analysis (n = 516) were grouped into clusters using a hierarchical clustering procedure. The association of each cluster's representative radiomic feature with RT and PD12 was assessed by chi-square test. Multivariate analysis was performed using logistic regression models. P values < 0.05 were considered significant. RESULTS: Patients with values of F2-Shape/Compactness1 below the median, of F1- GrayLevelCooccurenceMatrix25/0-1InformationMeasureCorr2 below the median and of F1-GrayLevelCooccurenceMatrix25/-333-1InverseVariance above the median showed higher risk of RT (36%, 36% and 35%, respectively, as opposed to 18%, 18% and 18%). Patients with values of F4-GrayLevelRunLengthMatrix25/-333RunPercentage above the median, of F2 shape/Max3DDiameter below the median and F1-GrayLevelCooccurenceMatrix25/45-1InverseVariance above the median showed higher risk of PD12 (22%, 24% and 23%, respectively, as opposed to 6%, 5% and 6%). At multivariate analysis F2-Shape/Max3DDiameter remained significant (odds ratio (95% CI) = 11.86 (1.41-99.88)). To predict PD12, a clinical radiomics model performed better than a base clinical model. CONCLUSION: This study demonstrated significant associations between radiomic features and prognostic factors such as RT and PD12. KEY POINTS: • No residual tumour (RT) at surgery is the most important prognostic factor in OC. • Radiomic features related to mass size, randomness and homogeneity were associated with RT. • Progression of disease within 12 months (PD12) indicates worse prognosis in OC. • A model including clinical and radiomic features performed better than only-clinical model to predict PD12.

New adaptive statistical iterative reconstruction ASiR-V: Assessment of noise performance in comparison to ASiR.

PURPOSE: To assess the noise characteristics of the new adaptive statistical iterative reconstruction (ASiR-V) in comparison to ASiR. METHODS: A water phantom was acquired with common clinical scanning parameters, at five different levels of CTDIvol . Images were reconstructed with different kernels (STD, SOFT, and BONE), different IR levels (40%, 60%, and 100%) and different slice thickness (ST) (0.625 and 2.5 mm), both for ASiR-V and ASiR. Noise properties were investigated and noise power spectrum (NPS) was evaluated. RESULTS: ASiR-V significantly reduced noise relative to FBP: noise reduction was in the range 23%-60% for a 0.625 mm ST and 12%-64% for the 2.5 mm ST. Above 2 mGy, noise reduction for ASiR-V had no dependence on dose. Noise reduction for ASIR-V has dependence on ST, being greater for STD and SOFT kernels at 2.5 mm. For the STD kernel ASiR-V has greater noise reduction for both ST, if compared to ASiR. For the SOFT kernel, results varies according to dose and ST, while for BONE kernel ASIR-V shows less noise reduction. NPS for CT Revolution has dose dependent behavior at lower doses. NPS for ASIR-V and ASiR is similar, showing a shift toward lower frequencies as the IR level increases for STD and SOFT kernels. The NPS is different between ASiR-V and ASIR with BONE kernel. NPS for ASiR-V appears to be ST dependent, having a shift toward lower frequencies for 2.5 mm ST. CONCLUSIONS: ASiR-V showed greater noise reduction than ASiR for STD and SOFT kernels, while keeping the same NPS. For the BONE kernel, ASiR-V presents a completely different behavior, with less noise reduction and modified NPS. Noise properties of the ASiR-V are dependent on reconstruction slice thickness. The noise properties of ASiR-V suggest the need for further measurements and efforts to establish new CT protocols to optimize clinical imaging.

Evaluation of deep myometrial invasion in endometrial cancer patients: is dual-energy CT an option?

OBJECTIVES: Assessment of deep (>50%) myometrial invasion by dual-energy CT (DECT) and Trans-Vaginal US (TVUS) in patients with endometrial cancer. METHODS: We retrospectively enrolled patients with endometrial cancer who underwent DECT and TVUS for pre-surgical staging. Three sets of images were evaluated: 70 keV (routine CT images), 50 keV, and iodine-water reconstructions. The gold standard was pathology after surgery. The agreement between the different imaging modalities and the gold standard was estimated. Sensitivity, specificity and accuracy for each imaging modality were evaluated with 95% confidence intervals (CI). RESULTS: Thirty-nine patients were included. Median time from CT and TVUS to surgery was 23 and 18 days, respectively. The best agreement between evaluation of myometrial infiltration and the gold standard was 0.88 (0.72, 1.00) for the 50 keV images; the worst agreement was 0.43 (0.00, 0.88) for the 70 keV images. CT iodine reconstructions and US agreement were comparable. Specificity, sensitivity and accuracy were 0.91, 1.00, 0.94; 0.57, 0.86, 0.71; 0.82, 1.00, 0.87; 0.91, 0.77, 0.86 for 50 keV, 70 keV, iodine reconstructions and ultrasound, respectively. CONCLUSIONS: DECT is a promising tool for assessment of myometrial invasion in endometrial cancer patients, with a special focus on 50 keV images.

Evaluation of diffusion-weighted imaging (DWI) and MR spectroscopy (MRS) as early response biomarkers in cervical cancer patients.

PURPOSE: To prospectively assess whether choline levels and Apparent Diffusion Coefficient (ADC) values within cervical cancers before, during, and after non-surgical therapy are predictive of tumour response. PATIENTS AND METHODS: Patients undergoing MR examination for staging of cervical cancer, candidate for non-surgical therapy, were prospectively enrolled. According to the status at the end of therapies, patients were divided into responders and non-responders. The final outcome after a 5-year follow-up was classified as No Evidence of Disease (NED) or Progression of Disease (PD). Baseline values of mean ADC and Cho/H2O were compared between responders and non-responders, as well as between patients with NED and PD. The percent variation of ADC and Cho/H2O values over time was compared. P values <0.05 were considered significant. RESULTS: 16 patients were included. There was no significant difference at baseline between responders (n = 12) and non-responders (n = 4), nor between NED (n = 11) PD patients (n = 5), in ADC values and Cho/H2O ratio. There was no significant difference in percent variation of ADC values and of Cho/H2O, comparing responders and non-responders. There was a significant increase in absolute values of ADC from the initial to mid-therapy MRI (p = 0.0001), while Cho/H2O was stable (p value: 0.61). In the four non-responders, the ADC increase was not significant (p value: 0.25), while it was significant in the 11 responders (p value: 0.001). Values of spectroscopy were stable in both responders and non-responders. CONCLUSIONS: High increases of ADC values from baseline to mid-therapy MR reflect response to therapies. There were no significant variations in choline/water ratios over time.

Radiation dose from multidetector CT studies in children: results from the first Italian nationwide survey.

BACKGROUND: Multidetector CT (MDCT) scanners have contributed to the widespread use of CT in paediatric imaging. However, concerns are raised for the associated radiation exposure. Very few surveys on radiation exposure from MDCT studies in children are available. OBJECTIVE: The aim of this study was to outline the status of radiation exposure in children from MDCT practice in Italy. MATERIALS AND METHODS: In this retrospective multicentre study we asked Italian radiology units with an MDCT scanner with at least 16 slices to provide dosimetric and acquisition parameters of CT examinations in three age groups (1-5, 6-10, 11-15 years) for studies of head, chest and abdomen. The dosimetric results were reported in terms of third-quartile volumetric CT dose index (CTDIvol) (mGy), size-specific dose estimate (SSDE) (mGy), dose length product (DLP) (mGy cm), and total DLP for multiphase studies. These results were compared with paediatric European and adult Italian published data. A multivariate analysis assessed the association of CTDIvol with patient characteristics and scanning modalities. RESULTS: We collected data from 993 MDCT examinations performed at 25 centres. For age groups 1-5 years, 6-10 years and 11-15 years, the CTDIvol, DLP and total DLP values were statistically significantly below the values observed in our analogous national survey in adults, although the difference decreased with increasing age. CTDIvol variability among centres was statistically significant (variance = 0.07; 95% confidence interval = 0.03-0.16; P < 0.001). CONCLUSIONS: This study reviewed practice in Italian centres performing paediatric imaging with MDCT scanners. The variability of doses among centres suggests that the use of standardised CT protocols should be encouraged.

Ultra-low-dose CT with model-based iterative reconstruction (MBIR): detection of ground-glass nodules in an anthropomorphic phantom study.

PURPOSE: The authors sought to evaluate the effect of model-based iterative reconstruction (MBIR) on the sensitivity of ground-glass nodule (GGN) detection at different dose levels. MATERIALS AND METHODS: Fifty-four artificial GGN were randomly divided into three sets, each positioned in an anthropomorphic phantom. The three sets were evaluated on standard-dose (SD, 350 mA), low-dose (LD, 35 mA) and ultra-low-dose (ULD, 10 mA) CT scans (100 kV, 64 × 0.625 mm, 0.5 s), and each scan was reconstructed twice with filtered back projection (FBP) and MBIR. Three radiologists independently evaluated the scans for GGN presence and size. SD + FBP was considered the reference standard. A region of interest (ROI) was used to calculate signal-to-noise ratio (SNR) and contrast-to-noise ratio normalised to dose (CNRD). McNemar's test, Bland-Altman analysis and t test were used for statistical assessment (p < 0.05). RESULTS: The mean diameter of the 54 GGNs was 9.2 mm (range 3.7-17.3 mm). For the three readers, no statistically significant differences were observed in the sensitivity of GGN detection between LD + MBIR, ULD + MBIR and SD + FBP (p > 0.05). Bland-Altman analysis showed a good reader agreement (±1.5 mm) for GGN size between SD + FBP and ULD + MBIR. For low dose and ultra-low dose, the SNR and CNRD were significantly higher with MBIR (p < 0.0001). The effective dose was 97.1 % lower with ultra-low dose (0.15 mSv) than standard dose (5.15 mSv). CONCLUSIONS: The detection of GGN with MBIR at low-dose and ultra-low-dose CT does not differ significantly from standard-dose CT with FBP in an anthropomorphic phantom.

Adult exposures from MDCT including multiphase studies: first Italian nationwide survey.

OBJECTIVES: To evaluate the radiation dose in routine multidetector computed tomography (MDCT) examinations in Italian population. METHODS: This was a retrospective multicentre study included 5,668 patients from 65 radiology departments who had undergone common CT protocols: head, chest, abdomen, chest­abdomen­pelvis (CAP), spine and cardiac. Data included patient characteristics, CT parameters, volumetric CT dose index (CTDIvol) and dose length product (DLP) for each CT acquisition phase. Descriptive statistics were calculated, and a multi-regression analysis was used to outline the main factors affecting exposure. RESULTS: The 75th percentiles of CTDIvol (mGy) and DLP (mGy cm) for whole head were 69 mGy and 1,312 mGy cm, respectively; for chest, 15 mGy and 569 mGy cm; spine, 42 mGy and 888 mGy cm; cardiac, 7 mGy and 131 mGy cm for calcium score, and 61 mGy and 1,208 mGy cm for angiographic CT studies. High variability was present in the DLP of abdomen and CAP protocols, where multiphase examinations dominated (71 % and 73 % respectively): for abdomen, 18 mGy, with 555 and 920 mGy cm in abdomen and abdomen­pelvis acquisitions respectively; for CAP, 17 mGy, with 508, 850 and 1,200 mGy cm in abdomen, abdomen­pelvis and CAP acquisitions respectively. CONCLUSION: The results of this survey could help in the definition of updated diagnostic reference levels (DRL). KEY POINTS: • Radiation dose associated with multidetector CT (MDCT) is an important health issue. • This national survey assessed dose exposures of 5,668 patients undergoing MDCT. • Dose indices correlate with BMI, voltage, rotation time, pitch and tube current. • These results may contribute to an update of national diagnostic reference levels.

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

An increase has been observed not only in the absolute number of CT examinations but also in the length of coverage and number of scanning phases, with the result that exposure to ionising radiation from CT is becoming an increasingly serious problem. The extent of the problem is not entirely known and cannot be adequately addressed without proper knowledge of all the phases that leads to the effective dose calculation. In light of the growing awareness of the issue of ionising radiation dose and the possible risk for the individual and the population, there is a need for radiologists, medical physicists and radiographers to play an active role in dose management. In this review, the authors try to delineate the problem in a consequential and multifaceted way: radiation-patient interaction, possible mechanisms of damage, main CT dose units, risk and its quantification in the population, with the aim of optimising the acquisition dose without diagnostic drawbacks. For an "up-to-date" use of CT, radiologists must know the dose concerns for the single patient and population, and use the CT apparatus with the best dose care; substitute CT with other diagnostic techniques when possible, especially in children; reduce the number/extension of scans and phases, and the dose in single scans and single examinations.

Deep learning performance for detection and classification of microcalcifications on mammography.

BACKGROUND: Breast cancer screening through mammography is crucial for early detection, yet the demand for mammography services surpasses the capacity of radiologists. Artificial intelligence (AI) can assist in evaluating microcalcifications on mammography. We developed and tested an AI model for localizing and characterizing microcalcifications. METHODS: Three expert radiologists annotated a dataset of mammograms using histology-based ground truth. The dataset was partitioned for training, validation, and testing. Three neural networks (AlexNet, ResNet18, and ResNet34) were trained and evaluated using specific metrics including receiver operating characteristics area under the curve (AUC), sensitivity, and specificity. The reported metrics were computed on the test set (10% of the whole dataset). RESULTS: The dataset included 1,000 patients aged 21-73 years and 1,986 mammograms (180 density A, 220 density B, 380 density C, and 220 density D), with 389 malignant and 611 benign groups of microcalcifications. AlexNet achieved the best performance with 0.98 sensitivity, 0.89 specificity of, and 0.98 AUC for microcalcifications detection and 0.85 sensitivity, 0.89 specificity, and 0.94 AUC of for microcalcifications classification. For microcalcifications detection, ResNet18 and ResNet34 achieved 0.96 and 0.97 sensitivity, 0.91 and 0.90 specificity and 0.98 and 0.98 AUC, retrospectively. For microcalcifications classification, ResNet18 and ResNet34 exhibited 0.75 and 0.84 sensitivity, 0.85 and 0.84 specificity, and 0.88 and 0.92 AUC, respectively. CONCLUSIONS: The developed AI models accurately detect and characterize microcalcifications on mammography. RELEVANCE STATEMENT: AI-based systems have the potential to assist radiologists in interpreting microcalcifications on mammograms. The study highlights the importance of developing reliable deep learning models possibly applied to breast cancer screening. KEY POINTS: • A novel AI tool was developed and tested to aid radiologists in the interpretation of mammography by accurately detecting and characterizing microcalcifications. • Three neural networks (AlexNet, ResNet18, and ResNet34) were trained, validated, and tested using an annotated dataset of 1,000 patients and 1,986 mammograms. • The AI tool demonstrated high accuracy in detecting/localizing and characterizing microcalcifications on mammography, highlighting the potential of AI-based systems to assist radiologists in the interpretation of mammograms.

Breast Digital Tomosynthesis versus Contrast-Enhanced Mammography: Comparison of Diagnostic Application and Radiation Dose in a Screening Setting.

This study aims to evaluate the Average Glandular Dose (AGD) and diagnostic performance of CEM versus Digital Mammography (DM) as well as versus DM plus one-view Digital Breast Tomosynthesis (DBT), which were performed in the same patients at short intervals of time. A preventive screening examination in high-risk asymptomatic patients between 2020 and 2022 was performed with two-view Digital Mammography (DM) projections (Cranio Caudal and Medio Lateral) plus one Digital Breast Tomosynthesis (DBT) projection (mediolateral oblique, MLO) in a single session examination. For all patients in whom we found a suspicious lesion by using DM + DBT, we performed (within two weeks) a CEM examination. AGD and compression force were compared between the diagnostic methods. All lesions identified by DM + DBT were biopsied; then, we assessed whether lesions found by DBT were also highlighted by DM alone and/or by CEM. We enrolled 49 patients with 49 lesions in the study. The median AGD was lower for DM alone than for CEM (3.41 mGy vs. 4.24 mGy, p = 0.015). The AGD for CEM was significantly lower than for the DM plus one single projection DBT protocol (4.24 mGy vs. 5.55 mGy, p < 0.001). We did not find a statistically significant difference in the median compression force between the CEM and DM + DBT. DM + DBT allows the identification of one more invasive neoplasm one in situ lesion and two high-risk lesions, compared to DM alone. The CEM, compared to DM + DBT, failed to identify only one of the high-risk lesions. According to these results, CEM could be used in the screening of asymptomatic high-risk patients.

Prediction of the Malignancy of a Breast Lesion Detected on Breast Ultrasound: Radiomics Applied to Clinical Practice.

The study aimed to evaluate the performance of radiomics features and one ultrasound CAD (computer-aided diagnosis) in the prediction of the malignancy of a breast lesion detected with ultrasound and to develop a nomogram incorporating radiomic score and available information on CAD performance, conventional Breast Imaging Reporting and Data System evaluation (BI-RADS), and clinical information. Data on 365 breast lesions referred for breast US with subsequent histologic analysis between January 2020 and March 2022 were retrospectively collected. Patients were randomly divided into a training group (n = 255) and a validation test group (n = 110). A radiomics score was generated from the US image. The CAD was performed in a subgroup of 209 cases. The radiomics score included seven radiomics features selected with the LASSO logistic regression model. The multivariable logistic model incorporating CAD performance, BI-RADS evaluation, clinical information, and radiomic score as covariates showed promising results in the prediction of the malignancy of breast lesions: Area under the receiver operating characteristic curve, [AUC]: 0.914; 95% Confidence Interval, [CI]: 0.876-0.951. A nomogram was developed based on these results for possible future applications in clinical practice.

How Radiomics Can Improve Breast Cancer Diagnosis and Treatment.

Recent technological advances in the field of artificial intelligence hold promise in addressing medical challenges in breast cancer care, such as early diagnosis, cancer subtype determination and molecular profiling, prediction of lymph node metastases, and prognostication of treatment response and probability of recurrence. Radiomics is a quantitative approach to medical imaging, which aims to enhance the existing data available to clinicians by means of advanced mathematical analysis using artificial intelligence. Various published studies from different fields in imaging have highlighted the potential of radiomics to enhance clinical decision making. In this review, we describe the evolution of AI in breast imaging and its frontiers, focusing on handcrafted and deep learning radiomics. We present a typical workflow of a radiomics analysis and a practical "how-to" guide. Finally, we summarize the methodology and implementation of radiomics in breast cancer, based on the most recent scientific literature to help researchers and clinicians gain fundamental knowledge of this emerging technology. Alongside this, we discuss the current limitations of radiomics and challenges of integration into clinical practice with conceptual consistency, data curation, technical reproducibility, adequate accuracy, and clinical translation. The incorporation of radiomics with clinical, histopathological, and genomic information will enable physicians to move forward to a higher level of personalized management of patients with breast cancer.

Statement of the Italian Association of Medical Physics (AIFM) task group on radiation dose monitoring systems.

The evaluation of radiation burden in vivo is crucial in modern radiology as stated also in the European Directive 2013/59/Euratom-Basic Safety Standard. Although radiation dose monitoring can impact the justification and optimization of radiological procedure, as well as effective patient communication, standardization of radiation monitoring software is far to be achieved. Toward this goal, the Italian Association of Medical Physics (AIFM) published a report describing the state of the art and standard guidelines in radiation dose monitoring system quality assurance. This article reports the AIFM statement about radiation dose monitoring systems (RDMSs) summarizing the different critical points of the systems related to Medical Physicist Expert (MPE) activities before, during, and after their clinical implementation. In particular, the article describes the general aspects of radiation dose data management, radiation dose monitoring systems, data integrity, and data responsibilities. Furthermore, the acceptance tests that need to be implemented and the most relevant dosimetric data for each radiological modalities are reported under the MPE responsibility.

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.