The objective of this study was to compare image quality and clinically significant lesion detection on deep learning reconstruction (DLR) and iterative reconstruction (IR) images of submillisievert ...chest and abdominopelvic CT.
Our prospective multiinstitutional study included 59 adult patients (33 women, 26 men; mean age ± SD, 65 ± 12 years old; mean body mass index weight in kilograms divided by the square of height in meters = 27 ± 5) who underwent routine chest (
= 22; 16 women, six men) and abdominopelvic (
= 37; 17 women, 20 men) CT on a 640-MDCT scanner (Aquilion ONE, Canon Medical Systems). All patients gave written informed consent for the acquisition of low-dose (LD) CT (LDCT) after a clinically indicated standard-dose (SD) CT (SDCT). The SDCT series (120 kVp, 164-644 mA) were reconstructed with interactive reconstruction (IR) (adaptive iterative dose reduction AIDR 3D, Canon Medical Systems), and the LDCT (100 kVp, 120 kVp; 30-50 mA) were reconstructed with filtered back-projection (FBP), IR (AIDR 3D and forward-projected model-based iterative reconstruction solution FIRST, Canon Medical Systems), and deep learning reconstruction (DLR) (Advanced Intelligent Clear-IQ Engine AiCE, Canon Medical Systems). Four subspecialty-trained radiologists first read all LD image sets and then compared them side-by-side with SD AIDR 3D images in an independent, randomized, and blinded fashion. Subspecialty radiologists assessed image quality of LDCT images on a 3-point scale (1 = unacceptable, 2 = suboptimal, 3 = optimal). Descriptive statistics were obtained, and the Wilcoxon sign rank test was performed.
Mean volume CT dose index and dose-length product for LDCT (2.1 ± 0.8 mGy, 49 ± 13mGy·cm) were lower than those for SDCT (13 ± 4.4 mGy, 567 ± 249 mGy·cm) (
< 0.0001). All 31 clinically significant abdominal lesions were seen on SD AIDR 3D and LD DLR images. Twenty-five, 18, and seven lesions were detected on LD AIDR 3D, LD FIRST, and LD FBP images, respectively. All 39 pulmonary nodules detected on SD AIDR 3D images were also noted on LD DLR images. LD DLR images were deemed acceptable for interpretation in 97% (35/37) of abdominal and 95-100% (21-22/22) of chest LDCT studies (
= 0.2-0.99). The LD FIRST, LD AIDR 3D, and LD FBP images had inferior image quality compared with SD AIDR 3D images (
< 0.0001).
At submillisievert chest and abdominopelvic CT doses, DLR enables image quality and lesion detection superior to commercial IR and FBP images.
Deep learning (DL) based solutions have been proposed for interpretation of several imaging modalities including radiography, CT, and MR. For chest radiographs, DL algorithms have found success in ...the evaluation of abnormalities such as lung nodules, pulmonary tuberculosis, cystic fibrosis, pneumoconiosis, and location of peripherally inserted central catheters. Chest radiography represents the most commonly performed radiological test for a multitude of non-emergent and emergent clinical indications. This study aims to assess accuracy of deep learning (DL) algorithm for detection of abnormalities on routine frontal chest radiographs (CXR), and assessment of stability or change in findings over serial radiographs.
We processed 874 de-identified frontal CXR from 724 adult patients (> 18 years) with DL (Qure AI). Scores and prediction statistics from DL were generated and recorded for the presence of pulmonary opacities, pleural effusions, hilar prominence, and enlarged cardiac silhouette. To establish a standard of reference (SOR), two thoracic radiologists assessed all CXR for these abnormalities. Four other radiologists (test radiologists), unaware of SOR and DL findings, independently assessed the presence of radiographic abnormalities. A total 724 radiographs were assessed for detection of findings. A subset of 150 radiographs with follow up examinations was used to asses change over time. Data were analyzed with receiver operating characteristics analyses and post-hoc power analysis.
About 42% (305/ 724) CXR had no findings according to SOR; single and multiple abnormalities were seen in 23% (168/724) and 35% (251/724) of CXR. There was no statistical difference between DL and SOR for all abnormalities (p = 0.2-0.8). The area under the curve (AUC) for DL and test radiologists ranged between 0.837-0.929 and 0.693-0.923, respectively. DL had lowest AUC (0.758) for assessing changes in pulmonary opacities over follow up CXR. Presence of chest wall implanted devices negatively affected the accuracy of DL algorithm for evaluation of pulmonary and hilar abnormalities.
DL algorithm can aid in interpretation of CXR findings and their stability over follow up CXR. However, in its present version, it is unlikely to replace radiologists due to its limited specificity for categorizing specific findings.
The new coronavirus disease 2019 (COVID-19) pandemic has challenged many healthcare systems around the world. While most of the current understanding of the clinical features of COVID-19 is derived ...from Chinese studies, there is a relative paucity of reports from the remaining global health community. In this study, we analyze the clinical and radiologic factors that correlate with mortality odds in COVID-19 positive patients from a tertiary care center in Tehran, Iran. A retrospective cohort study of 90 patients with reverse transcriptase-polymerase chain reaction (RT-PCR) positive COVID-19 infection was conducted, analyzing demographics, co-morbidities, presenting symptoms, vital signs, laboratory values, chest radiograph findings, and chest CT features based on mortality. Chest radiograph was assessed using the Radiographic Assessment of Lung Edema (RALE) scoring system. Chest CTs were assessed according to the opacification pattern, distribution, and standardized severity score. Initial and follow-up Chest CTs were compared if available. Multiple logistic regression was used to generate a prediction model for mortality. The 90 patients included 59 men and 31 women (59.4 ± 16.6 years), including 21 deceased and 69 surviving patients. Among clinical features, advanced age (p = 0.02), low oxygenation saturation (p<0.001), leukocytosis (p = 0.02), low lymphocyte fraction (p = 0.03), and low platelet count (p = 0.048) were associated with increased mortality. High RALE score on initial chest radiograph (p = 0.002), presence of pleural effusions on initial CT chest (p = 0.005), development of pleural effusions on follow-up CT chest (p = 0.04), and worsening lung severity score on follow-up CT Chest (p = 0.03) were associated with mortality. A two-factor logistic model using patient age and oxygen saturation was created, which demonstrates 89% accuracy and area under the ROC curve of 0.86 (p<0.0001). Specific demographic, clinical, and imaging features are associated with increased mortality in COVID-19 infections. Attention to these features can help optimize patient management.
To compare lesion detection and image quality of chest computed tomographic (CT) images acquired at various tube current-time products (40-150 mAs) and reconstructed with adaptive statistical ...iterative reconstruction (ASIR) or filtered back projection (FBP).
In this Institutional Review Board-approved HIPAA-compliant study, CT data from 23 patients (mean age, 63 years ± 7.3 standard deviation; 10 men, 13 women) were acquired at varying tube current-time products (40, 75, 110, and 150 mAs) on a 64-row multidetector CT scanner with 10-cm scan length. All patients gave informed consent. Data sets were reconstructed at 30%, 50%, and 70% ASIR-FBP blending. Two thoracic radiologists assessed image noise, visibility of small structures, lesion conspicuity, and diagnostic confidence. Objective noise and CT number were measured in the thoracic aorta. CT dose index volume, dose-length product, weight, and transverse diameter were recorded. Data were analyzed by using analysis of variance and the Wilcoxon signed rank test.
FBP had unacceptable noise at 40 and 75 mAs in 17 and five patients, respectively, whereas ASIR had acceptable noise at 40-150 mAs. Objective noise with 30%, 50%, and 70% ASIR blending (11.8 ± 3.8, 9.6 ± 3.1, and 7.5 ± 2.6, respectively) was lower than that with FBP (15.8 ± 4.8) (P < .0001). No lesions were missed on FBP or ASIR images. Lesion conspicuity was graded as well seen on both FBP and ASIR images (P < .05). Mild pixilated blotchy texture was noticed with 70% blended ASIR images.
Acceptable image quality can be obtained for chest CT images acquired at 40 mAs by using ASIR without any substantial artifacts affecting diagnostic confidence.
http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.11101450/-/DC1.
The EML4-ALK fusion oncogene represents a novel molecular target in a small subset of non-small-cell lung cancers (NSCLC). To aid in identification and treatment of these patients, we examined the ...clinical characteristics and treatment outcomes of patients who had NSCLC with and without EML4-ALK.
Patients with NSCLC were selected for genetic screening on the basis of two or more of the following characteristics: female sex, Asian ethnicity, never/light smoking history, and adenocarcinoma histology. EML4-ALK was identified by using fluorescent in situ hybridization for ALK rearrangements and was confirmed by immunohistochemistry for ALK expression. EGFR and KRAS mutations were determined by DNA sequencing.
Of 141 tumors screened, 19 (13%) were EML4-ALK mutant, 31 (22%) were EGFR mutant, and 91 (65%) were wild type (WT/WT) for both ALK and EGFR. Compared with the EGFR mutant and WT/WT cohorts, patients with EML4-ALK mutant tumors were significantly younger (P < .001 and P = .005) and were more likely to be men (P = .036 and P = .039). Patients with EML4-ALK-positive tumors, like patients who harbored EGFR mutations, also were more likely to be never/light smokers compared with patients in the WT/WT cohort (P < .001). Eighteen of the 19 EML4-ALK tumors were adenocarcinomas, predominantly the signet ring cell subtype. Among patients with metastatic disease, EML4-ALK positivity was associated with resistance to EGFR tyrosine kinase inhibitors (TKIs). Patients in the EML4-ALK cohort and the WT/WT cohort showed similar response rates to platinum-based combination chemotherapy and no difference in overall survival.
EML4-ALK defines a molecular subset of NSCLC with distinct clinical characteristics. Patients who harbor this mutation do not benefit from EGFR TKIs and should be directed to trials of ALK-targeted agents.
Objective
Assess if deep learning–based artificial intelligence (AI) algorithm improves reader performance for lung cancer detection on chest X-rays (CXRs).
Methods
This reader study included 173 ...images from cancer-positive patients (n = 98) and 346 images from cancer-negative patients (n = 196) selected from National Lung Screening Trial (NLST). Eight readers, including three radiology residents, and five board-certified radiologists, participated in the observer performance test. AI algorithm provided image-level probability of pulmonary nodule or mass on CXRs and a heatmap of detected lesions. Reader performance was compared with AUC, sensitivity, specificity, false-positives per image (FPPI), and rates of chest CT recommendations.
Results
With AI, the average sensitivity of readers for the detection of visible lung cancer increased for residents, but was similar for radiologists compared to that without AI (0.61 95% CI, 0.55–0.67 vs. 0.72 95% CI, 0.66–0.77,
p
= 0.016 for residents, and 0.76 95% CI, 0.72–0.81 vs. 0.76 95% CI, 0.72–0.81,
p
= 1.00 for radiologists), while false-positive findings per image (FPPI) was similar for residents, but decreased for radiologists (0.15 95% CI, 0.11–0.18 vs. 0.12 95% CI, 0.09–0.16,
p
= 0.13 for residents, and 0.24 95% CI, 0.20–0.29 vs. 0.17 95% CI, 0.13–0.20,
p
< 0.001 for radiologists). With AI, the average rate of chest CT recommendation in patients positive for visible cancer increased for residents, but was similar for radiologists (54.7% 95% CI, 48.2–61.2% vs. 70.2% 95% CI, 64.2–76.2%,
p
< 0.001 for residents and 72.5% 95% CI, 68.0–77.1% vs. 73.9% 95% CI, 69.4–78.3%,
p
= 0.68 for radiologists), while that in cancer-negative patients was similar for residents, but decreased for radiologists (11.2% 95% CI, 9.6–13.1% vs. 9.8% 95% CI, 8.0–11.6%,
p
= 0.32 for residents and 16.4% 95% CI, 14.7–18.2% vs. 11.7% 95% CI, 10.2–13.3%,
p
< 0.001 for radiologists).
Conclusions
AI algorithm can enhance the performance of readers for the detection of lung cancers on chest radiographs when used as second reader.
Key Points
• Reader study in the NLST dataset shows that AI algorithm had sensitivity benefit for residents and specificity benefit for radiologists for the detection of visible lung cancer.
• With AI, radiology residents were able to recommend more chest CT examinations (54.7% vs 70.2%, p < 0.001) for patients with visible lung cancer.
• With AI, radiologists recommended significantly less proportion of unnecessary chest CT examinations (16.4% vs. 11.7%, p < 0.001) in cancer-negative patients.
Current standard initial therapy for advanced, ROS proto-oncogene 1, receptor tyrosine kinase fusion (
)-positive (ROS1
) non-small cell lung cancer (NSCLC) is crizotinib or entrectinib. Lorlatinib, ...a next-generation anaplastic lymphoma kinase/ROS1 inhibitor, recently demonstrated efficacy in ROS1
NSCLC, including in crizotinib-pretreated patients. However, mechanisms of lorlatinib resistance in ROS1
disease remain poorly understood. Here, we assessed mechanisms of resistance to crizotinib and lorlatinib.
Biopsies from patients with ROS1
NSCLC progressing on crizotinib or lorlatinib were profiled by genetic sequencing.
From 55 patients, 47 post-crizotinib and 32 post-lorlatinib biopsies were assessed. Among 42 post-crizotinib and 28 post-lorlatinib biopsies analyzed at distinct timepoints,
mutations were identified in 38% and 46%, respectively.
G2032R was the most commonly occurring mutation in approximately one third of cases. Additional
mutations included D2033N (2.4%) and S1986F (2.4%) post-crizotinib and L2086F (3.6%), G2032R/L2086F (3.6%), G2032R/S1986F/L2086F (3.6%), and S1986F/L2000V (3.6%) post-lorlatinib. Structural modeling predicted ROS1
causes steric interference to lorlatinib, crizotinib, and entrectinib, while it may accommodate cabozantinib. In Ba/F3 models, ROS1
, ROS1
, and ROS1
were refractory to lorlatinib but sensitive to cabozantinib. A patient with disease progression on crizotinib and lorlatinib and
L2086F received cabozantinib for nearly 11 months with disease control. Among lorlatinib-resistant biopsies, we also identified
amplification (4%),
G12C (4%),
amplification (4%),
mutation (4%), and
mutation (4%).
mutations mediate resistance to crizotinib and lorlatinib in more than one third of cases, underscoring the importance of developing next-generation ROS1 inhibitors with potency against these mutations, including G2032R and L2086F. Continued efforts are needed to elucidate ROS1-independent resistance mechanisms.
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