CT based radiomic approach on first line pembrolizumab in lung cancer

Clinical evaluation poorly predicts outcomes in lung cancer treated with immunotherapy. The aim of the study is to assess whether CT-derived texture parameters can predict overall survival (OS) and progression-free survival (PFS) in patients with advanced non-small-cell lung cancer (NSCLC) treated with first line Pembrolizumab. Twenty-one patients with NSLC were prospectively enrolled; they underwent contrast enhanced CT (CECT) at baseline and during Pembrolizumab treatment. Response to therapy was assessed both with clinical and iRECIST criteria. Two radiologists drew a volume of interest of the tumor at baseline CECT, extracting several texture parameters. ROC curves, a univariate Kaplan-Meyer analysis and Cox proportional analysis were performed to evaluate the prognostic value of texture analysis. Twelve (57%) patients showed partial response to therapy while nine (43%) had confirmed progressive disease. Among texture parameters, mean value of positive pixels (MPP) at fine and medium filters showed an AUC of 72% and 74% respectively (P < 0.001). Kaplan-Meyer analysis showed that MPP < 56.2 were significantly associated with lower OS and PFS (P < 0.0035). Cox proportional analysis showed a significant correlation between MPP4 and OS (P = 0.0038; HR = 0.89[CI 95%:0.83,0.96]). In conclusion, MPP could be used as predictive imaging biomarkers of OS and PFS in patients with NSLC with first line immune treatment

Chest CT texture-based radiomics analysis in differentiating COVID-19 from other interstitial pneumonia

Purpose To evaluate the potential role of texture-based radiomics analysis in differentiating Coronavirus Disease-19 (COVID-19) pneumonia from pneumonia of other etiology on Chest CT. Materials and methods One hundred and twenty consecutive patients admitted to Emergency Department, from March 8, 2020, to April 25, 2020, with suspicious of COVID-19 that underwent Chest CT, were retrospectively analyzed. All patients presented CT findings indicative for interstitial pneumonia. Sixty patients with positive COVID-19 real-time reverse transcription polymerase chain reaction (RT-PCR) and 60 patients with negative COVID-19 RT-PCR were enrolled. CT texture analysis (CTTA) was manually performed using dedicated software by two radiologists in consensus and textural features on filtered and unfiltered images were extracted as follows: mean intensity, standard deviation (SD), entropy, mean of positive pixels (MPP), skewness, and kurtosis. Nonparametric Mann–Whitney test assessed CTTA ability to differentiate positive from negative COVID-19 patients. Diagnostic criteria were obtained from receiver operating characteristic (ROC) curves. Results Unfiltered CTTA showed lower values of mean intensity, MPP, and kurtosis in COVID-19 positive patients compared to negative patients (p = 0.041, 0.004, and 0.002, respectively). On filtered images, fine and medium texture scales were significant differentiators; fine texture scale being most significant where COVID-19 positive patients had lower SD (p = 0.004) and MPP (p = 0.004) compared to COVID-19 negative patients. A combination of the significant texture features could identify the patients with positive COVID-19 from negative COVID-19 with a sensitivity of 60% and specificity of 80% (p = 0.001). Conclusions Preliminary evaluation suggests potential role of CTTA in distinguishing COVID-19 pneumonia from other interstitial pneumonia on Chest CT

Magnetic resonance of rectal cancer response to therapy: an Image quality comparison between 3.0 and 1.5 Tesla

Purpose. To evaluate signal intensity (SI) differences between 3.0 T and 1.5 T on T2-weighted (T2w), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) in rectal cancer pre-, during, and postneoadjuvant chemoradiotherapy (CRT). Materials and Methods. 22 patients with locally advanced rectal cancer were prospectively enrolled. All patients underwent T2w, DWI, and ADC pre-, during, and post-CRT on both 3.0 T MRI and 1.5 T MRI. A radiologist drew regions of interest (ROIs) of the tumor and obturator internus muscle on the selected slice to evaluate SI and relative SI (rSI). Additionally, a subanalysis evaluating the SI before and after-CRT (ΔSI pre-post) in complete responder patients (CR) and nonresponder patients (NR) on T2w, DWI, and ADC was performed. Results. Significant differences were observed for T2w and DWI on 3.0 T MRI compared to 1.5 T MRI pre-, during, and post-CRT (all P<0.001), whereas no significant differences were reported for ADC among all controls (all P>0.05). rSI showed no significant differences in all the examinations for all sequences (all P>0.05). ΔSI showed significant differences between 3.0 T and 1.5 T MRI for DWI-ΔSI in CR and NR (188.39±166.90 vs. 30.45±21.73 and 169.70±121.87 vs. 22.00±31.29, respectively, all P 0.02) and ADC-ΔSI for CR (-0.58±0.27 vs. -0.21±0.24P value 0.02), while no significant differences were observed for ADC-ΔSI in NR and both CR and NR for T2w-ΔSI. Conclusion. T2w-SI and DWI-SI showed significant differences for 3.0 T compared to 1.5 T in all three controls, while ADCSI showed no significant differences in all three controls on both field strengths. rSI was comparable for 3.0 T and 1.5 T MRI in rectal cancer patients; therefore, rectal cancer patients can be assessed both at 3.0 T MRI and 1.5 T MRI. However, a significant DWI-ΔSI and ADC-ΔSI on 3.0 T in CR might be interpreted as a better visual assessment in discriminating response to therapy compared to 1.5 T. Further investigations should be performed to confirm future possible clinical application

Optimization of contrast medium volume for abdominal CT in oncologic patients: prospective comparison between fixed and lean body weight-adapted dosing protocols

Background: Patient body size represents the main determinant of parenchymal enhancement and by adjusting the contrast media (CM) dose to patient weight may be a more appropriate approach to avoid a patient over dosage of CM. To compare the performance of fixed-dose and lean body weight (LBW)-adapted contrast media dosing protocols, in terms of image quality and parenchymal enhancement. Results: One-hundred cancer patients undergoing multiphasic abdominal CT were prospectively enrolled in this multicentric study and randomly divided in two groups: patients in fixed-dose group (n = 50) received 120 mL of CM while in LBW group (n = 50) the amount of CM was computed according to the patient’s LBW. LBW protocol group received a significantly lower amount of CM (103.47 ± 17.65 mL vs. 120.00 ± 0.00 mL, p < 0.001). Arterial kidney signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) and pancreatic CNR were significantly higher in LBW group (all p ≤ 0.004). LBW group provided significantly higher arterial liver, kidney, and pancreatic contrast enhancement index (CEI) and portal venous phase kidney CEI (all p ≤ 0.002). Significantly lower portal vein SNR and CNR were observed in LBW-Group (all p ≤ 0.020). Conclusions: LBW-adapted CM administration for abdominal CT reduces the volume of injected CM and improves both image quality and parenchymal enhancement

Typical and atypical COVID-19 computed tomography findings

In December 2019 a novel coronavirus, named severe acute respiratory syndrome coronavirus 2 was identified and the disease associated was named coronavirus disease 2019 (COVID-19). Fever, cough, myalgia, fatigue associated to dyspnea represent most common clinical symptoms of the disease. The reference standard for diagnosis of severe acute respiratory syndrome coronavirus 2 infection is real time reverse-transcription polymerase chain reaction test applied on respiratory tract specimens. Despite of lower specificity, chest computed tomography (CT), as reported in manifold scientific studies, showed high sensitivity, therefore it may help in the early detection, management and follow-up of COVID-19 pneumonia. Patients affected by COVID-19 pneumonia usually showed on chest CT some typical features, such as: Bilateral ground glass opacities characterized by multilobe involvement with posterior and peripheral distribution; parenchymal consolidations with or without air bronchogram; interlobular septal thickening; crazy paving pattern, represented by interlobular and intralobular septal thickening surrounded by ground-glass opacities; subsegmental pulmonary vessels enlargement (> 3 mm). Halo sign, reversed halo sign, cavitation and pleural or pericardial effusion represent some of atypical findings of COVID-19 pneumonia. On the other hand lymphadenopathy's and bronchiectasis' frequency is unclear, indeed conflicting data emerged in literature. Radiologists play a key role in recognition of high suspicious findings of COVID-19 on chest CT, both typical and atypical ones. Thus, the aim of this review is to illustrate typical and atypical CT findings of COVID-19

Is CT radiomics superior to morphological evaluation for PN0 characterization? A pilot study in stage II colon adenocarcinoma

Purpose: Lymph node (LN) involvement is among the most important prognostic factors for patients with colon adenocarcinoma (CAC). However, CT morphological analysis is not a reliable method to assess LN status. Thus, we compared morphological and radiomic features extracted from contrastenhanced computed tomography (CECT) images, in assessing locoregional LNs in patients with CAC. Material and Methods: This retrospective study included 45 patients with stage II CAC who underwent preoperative portal-venous phase CT examination and diagnosed as pN0 after surgery. Patients with motion artifacts or lack of pathology report were excluded. Locoregional LNs were scored with a qualitative Likert-scale based on morphological imaging (node score with range 0–5) and divided into two groups: low likelihood (0–2 points) and high likelihood (3–5 points) of malignancy. Then 107 radiomic features were extracted from CECT for each LN. T-test and Mann–Whitney were performed to compare radiomic features between the two groups. P<0.05 was considered significant. Results: A total of 115 negative LNs were analyzed and divided into 48 with low likelihood and 67 with high likelihood of malignancy based on the node score. Radiomic analysis showed 70 features (5/13 shape, 16/19 first-order and 49/75 second-order features) with no significant difference between the two groups, according to pathology (all P>0.05). On the other hand, 37 features (8/13 shape, 3/19 first-order and 26/75 second-order features) were significantly different (all P<0.04). Conclusion: Our preliminary experience showed that CT radiomics characterizes locoregional LN status better than CECT morphological evaluation and could be used as a non-invasive preoperative tool in patients with CAC

CT-based radiomics for prediction of therapeutic response to Everolimus in metastatic neuroendocrine tumors

Purpose: To test radiomic approach in patients with metastatic neuroendocrine tumors (NETs) treated with Everolimus, with the aim to predict progression free survival (PFS) and death. Material and Methods: Twenty-five patients with metastatic neuroendocrine tumors, 15/25 pancreatic(60%), 9/25 ileal(36%), 1/25 lung(4%), were retrospectively enrolled between August 2013 and December 2020. All patients underwent contrast-enhanced CT before starting Everolimus, histological diagnosis, tumor grading, PFS, overall survival (OS), death, and clinical data collected. Population divided into two groups: responders (PFS 12>months) and non-responders (PFS<12months). 3D-segmentation performed on whole liver of naïve CT-scans on arterial and venous phases, using a dedicated software (3DSlicer v4.10.2). 107 radiomic features were extracted and compared between two groups (T-test or Mann-Withney); Radiomics performance assessed with receiver operating characteristic curve, Kaplan-Meyer curves used for survival analysis, univariate and multivariate logistic regression performed to predict death. P<0.05 considered significant. Results: 15/25 patients were classified as responders (median PFS 25 months and OS 29 months), and 10/25 as non-responders (median PFS 4.5 months and OS 23 months). Among radiomic parameters, Correlation and Imc1 showed significant differences between two groups (P<0.05) with the best performance (AUC 0.86-0.84, P<0.0001). Correlation <0.21 resulted correlated with death at Kaplan-Meyer analysis (P=0.02). Univariate analysis showed three radiomic features independently correlated with death, and in multivariate analysis radiomic model showed good performance with AUC 0.87, sensitivity 100%, and specificity 66.7%. Conclusion: In patients affected by metastatic NETs eligible for Everolimus treatment Radiomics could be used as imaging biomarker able to predict PFS and death

Management decisions of an Academic Radiology Department during COVID-19 pandemic: the important support of a business analytics software

Objectives: To analyze the response in the management of both radiological emergencies and continuity of care in oncologic/fragile patients of a radiology department of Sant’Andrea Academic Hospital in Rome supported by a dedicated business analytics software during the COVID-19 pandemic. Methods: Imaging volumes and workflows for 2019 and 2020 were analyzed. Information was collected from the hospital data warehouse and evaluated using a business analytics software, aggregated both per week and per quarter, stratified by patient service location (emergency department, inpatients, outpatients) and imaging modality. For emergency radiology subunit, radiologist workload, machine workload, and turnaround times (TATs) were also analyzed. Results: Total imaging volume in 2020 decreased by 21.5% compared to that in 2019 (p &lt;.001); CT in outpatients increased by 11.7% (p &lt;.005). Median global TAT and median code-blue global TAT were not statistically significantly different between 2019 and 2020 and between the first and the second pandemic waves in 2020 (all p &gt;.09). Radiologist workload decreased by 24.7% (p &lt;.001) during the first pandemic wave in 2020 compared with the same weeks of 2019 and showed no statistically significant difference during the second pandemic wave, compared with the same weeks of 2019 (p = 0.19). Conclusions: Despite the reduction of total imaging volume due to the COVID-19 pandemic in 2020 compared to 2019, management decisions supported by a dedicated business analytics software allowed to increase the number of CT in fragile/oncologic outpatients without significantly affecting emergency radiology TATs, and emergency radiologist workload. Key Points: • During the COVID-19 pandemic, management decisions supported by business analytics software guaranteed efficiency of emergency and preservation of fragile/oncologic patient continuity of care. • Real-time data monitoring using business analytics software is essential for appropriate management decisions in a department of radiology. • Business analytics should be gradually introduced in all healthcare institutions to identify strong and weak points in workflow taking correct decisions

Quantitative MRI in the evaluation of patients with non-alcoholic steatohepatitis (NASH)

To evaluate the reliability of quantitative magnetic resonance imaging (MRI) in the diagnosis and follow-up of patients with NASH. Methods or Background From March to September 2021 twenty patients who met diagnostic criteria for NASH and twenty healthy volunteers were enrolled and underwent quantitative 1.5T MRI examination of the liver. Acquisition protocol comprised magnetic resonance elastography (MRE) and proton density fat fraction (PDFF). Image post-processing was performed with a dedicated workstation. Quantitative image analysis was performed by a radiologist with 15 years of experience in abdominal MRI; liver stiffness (kPa) and grade of steatosis (%) were collected. Statistical analysis was performed using a dedicated software and a p value &lt;0.05 was considered significant. Results or Findings Liver stiffness was higher in patients with NASH than in healthy volunteers (10.8±6.42 kPa vs 4.75±1.44 kPa; p=0.0002). PDFF was higher in patients with NASH than in healthy volunteers (2.52±0.56% vs 1.87±0.26%; p=0.0020). Steatosis was present in 100% of patients in the NASH group (15% in healthy volunteers); NASH group showed elevated liver stiffness in 80% of patients (0% in healthy volunteers). Quantitative MRI performance for liver stiffness and PDFF detection showed an area under the curve (AUC) of 0.915 (sensitivity 100%; specificity 75%) and 0.843 (sensitivity 60%; specificity 90%), respectively. The whole time examination was 72 seconds (55 seconds for MRE and 17 seconds for PDFF). Conclusion Quantitative MRI techniques are reliable in the study of NASH. Furthermore, being very fast and risk-free protocols, these technique could also be used as a screening method in populations at risk of developing diffuse liver disease. Limitations The small population sample and lack of multivariate clinical-radiological analysis were identified limitations

Prognostic models for classifying rectal tumour response to therapy using radiomics and CNN features

Purpose or Learning Objective To retrospectively evaluate the best radiomic features in predicting complete response to neoadjuvant therapy in patients affected by rectal cancer and to assess the possible correlation between them. Methods or Background A total of 109 handcrafted radiomic features and 4096 deep radiomic features were extracted from pre-treatment 3D-MRI of 43 patients, using transfer learning from a pre-trained convolutional neural network. The most widely explored 7 supervised machine learning-based classifiers and 6 different feature selection algorithms were validated and compared utilising all possible radiomic features, in order to examine their effectiveness in achieving an accurate predictive model. Cross-validation was performed in 100 rounds partitioning the data as 75% for training and 25% for testing. Results or Findings Using only handcrafted radiomic features, artificial neural network classifier and Fisher as feature selection algorithm delivered the best predictive performance on test data sets with the area under the curves (AUCs) [mean±SD] of 0.79±0.016 and 0.8±0.01, respectively. The best prognostic performance, using only deep radiomic features, was achieved by linear support vector machine (LSVM) classifier and Relief-based feature selection algorithm as 0.8±0.042 and 0.82±0.04, respectively. When using a combination of both handcrafted and deep radiomic features, almost all classifiers in combination with every feature selection algorithm generated better AUC than that obtained individually; the best AUCs were generated by the LSVM classifier and Relief-based feature selection as 0.84±0.025 and 0.87±0.013, respectively. Conclusion The best predicting models’ performance was achieved by integrating both handcrafted and deep radiomic features, i.e. LSVM classifier and Relief-based feature selection algorithms in combination with all classifiers