In JAMA network open

IMPORTANCE : Annual low-dose computed tomographic (LDCT) screening reduces lung cancer mortality, but harms could be reduced and cost-effectiveness improved by reusing the LDCT image in conjunction with deep learning or statistical models to identify low-risk individuals for biennial screening.

OBJECTIVE : To identify low-risk individuals in the National Lung Screening Trial (NLST) and estimate, had they been assigned a biennial screening, how many lung cancers would have been delayed 1 year in diagnosis.

DESIGN, SETTING, AND PARTICIPANTS : This diagnostic study included participants with a presumed nonmalignant lung nodule in the NLST between January 1, 2002, and December 31, 2004, with follow-up completed on December 31, 2009. Data were analyzed for this study from September 11, 2019, to March 15, 2022.

EXPOSURES : An externally validated deep learning algorithm that predicts malignancy in current lung nodules using LDCT images (Lung Cancer Prediction Convolutional Neural Network [LCP-CNN]; Optellum Ltd) was recalibrated to predict 1-year lung cancer detection by LDCT for presumed nonmalignant nodules. Individuals with presumed nonmalignant lung nodules were hypothetically assigned annual vs biennial screening based on the recalibrated LCP-CNN model, Lung Cancer Risk Assessment Tool (LCRAT + CT [a statistical model combining individual risk factors and LDCT image features]), and the American College of Radiology recommendations for lung nodules, version 1.1 (Lung-RADS).

MAIN OUTCOMES AND MEASURES : Primary outcomes included model prediction performance, the absolute risk of a 1-year delay in cancer diagnosis, and the proportion of people without lung cancer assigned a biennial screening interval vs the proportion of cancer diagnoses delayed.

RESULTS : The study included 10 831 LDCT images from patients with presumed nonmalignant lung nodules (58.7% men; mean [SD] age, 61.9 [5.0] years), of whom 195 were diagnosed with lung cancer from the subsequent screen. The recalibrated LCP-CNN had substantially higher area under the curve (0.87) than LCRAT + CT (0.79) or Lung-RADS (0.69) to predict 1-year lung cancer risk (P < .001). If 66% of screens with nodules were assigned to biennial screening, the absolute risk of a 1-year delay in cancer diagnosis would have been lower for recalibrated LCP-CNN (0.28%) than LCRAT + CT (0.60%; P = .001) or Lung-RADS (0.97%; P < .001). To delay only 10% of cancer diagnoses at 1 year, more people would have been safely assigned biennial screening under LCP-CNN than LCRAT + CT (66.4% vs 40.3%; P < .001).

CONCLUSIONS AND RELEVANCE : In this diagnostic study evaluating models of lung cancer risk, a recalibrated deep learning algorithm was most predictive of 1-year lung cancer risk and had least risk of 1-year delay in cancer diagnosis among people assigned biennial screening. Deep learning algorithms could prioritize people for workup of suspicious nodules and decrease screening intensity for people with low-risk nodules, which may be vital for implementation in health care systems.

Landy Rebecca, Wang Vivian L, Baldwin David R, Pinsky Paul F, Cheung Li C, Castle Philip E, Skarzynski Martin, Robbins Hilary A, Katki Hormuzd A

2023-Mar-01

In JNCI cancer spectrum

Johnson Skyler B, King Andy J, Warner Echo L, Aneja Sanjay, Kann Benjamin H, Bylund Carma L

2023-Mar-01

In Advanced science (Weinheim, Baden-Wurttemberg, Germany)

Chen Xiuli, Gong Yusheng, Chen Wei

2023-Mar-16

clinical translation, neurological disorder, on-demand, temporal-spatial precision

In Journal of magnetic resonance imaging : JMRI

BACKGROUND : Biological heart age estimation can provide insights into cardiac aging. However, existing studies do not consider differential aging across cardiac regions.

PURPOSE : To estimate biological age of the left ventricle (LV), right ventricle (RV), myocardium, left atrium, and right atrium using magnetic resonance imaging radiomics phenotypes and to investigate determinants of aging by cardiac region.

STUDY TYPE : Cross-sectional.

POPULATION : A total of 18,117 healthy UK Biobank participants including 8338 men (mean age = 64.2 ± 7.5) and 9779 women (mean age = 63.0 ± 7.4).

FIELD STRENGTH/SEQUENCE : A 1.5 T/balanced steady-state free precession.

ASSESSMENT : An automated algorithm was used to segment the five cardiac regions, from which radiomic features were extracted. Bayesian ridge regression was used to estimate biological age of each cardiac region with radiomics features as predictors and chronological age as the output. The "age gap" was the difference between biological and chronological age. Linear regression was used to calculate associations of age gap from each cardiac region with socioeconomic, lifestyle, body composition, blood pressure and arterial stiffness, blood biomarkers, mental well-being, multiorgan health, and sex hormone exposures (n = 49).

STATISTICAL TEST : Multiple testing correction with false discovery method (threshold = 5%).

RESULTS : The largest model error was with RV and the smallest with LV age (mean absolute error in men: 5.26 vs. 4.96 years). There were 172 statistically significant age gap associations. Greater visceral adiposity was the strongest correlate of larger age gaps, for example, myocardial age gap in women (Beta = 0.85, P = 1.69 × 10^-26 ). Poor mental health associated with large age gaps, for example, "disinterested" episodes and myocardial age gap in men (Beta = 0.25, P = 0.001), as did a history of dental problems (eg LV in men Beta = 0.19, P = 0.02). Higher bone mineral density was the strongest associate of smaller age gaps, for example, myocardial age gap in men (Beta = -1.52, P = 7.44 × 10^-6 ).

DATA CONCLUSION : This work demonstrates image-based heart age estimation as a novel method for understanding cardiac aging.

EVIDENCE LEVEL : 1.

TECHNICAL EFFICACY : Stage 1.

Salih Ahmed M, Pujadas Esmeralda Ruiz, Campello Víctor M, McCracken Celeste, Harvey Nicholas C, Neubauer Stefan, Lekadir Karim, Nichols Thomas E, Petersen Steffen E, Raisi-Estabragh Zahra

2023-Mar-16

aging, cardiac health, cardiac imaging, radiomics

In Journal of magnetic resonance imaging : JMRI

BACKGROUND : As lymphovascular space invasion (LVSI) was closely related to lymph node metastasis and prognosis, the preoperative assessment of LVSI in early-stage cervical cancer is crucial for patients.

PURPOSE : To develop and validate nomogram based on multimodal MR radiomics to assess LVSI status in cervical cancer patients.

STUDY TYPE : Retrospective.

POPULATION : The study included 168 cervical cancer patients, of whom 129 cases (age 51.36 ± 9.99 years) from institution 1 were included as the training cohort and 39 cases (age 52.59 ± 10.23 years) from institution 2 were included as the external test cohort.

FIELD STRENGTH/SEQUENCE : There were 1.5 T and 3.0 T MRI scans (T1-weighted imaging [T1WI], fat-saturated T2-weighted imaging [FS-T2WI], and contrast-enhanced [CE]).

ASSESSMENT : Six machine learning models were built and selected to construct the radiomics signature. The nomogram model was constructed by combining the radiomics signature with the clinical signature, which was then validated for discrimination, calibration, and clinical usefulness.

STATISTICAL TESTS : The clinical characteristics were compared using t-tests, Mann-Whitney U tests, or chi-square tests. The Spearman and LASSO methods were used to select radiomics features. The receiver operating characteristic (ROC) analysis was performed, and the area under the curve (AUC), accuracy, sensitivity, and specificity were calculated.

RESULTS : The logistic regression (LR) model performed best in each sequence. The AUC of CE-T1-T2WI-combined was the highest in the LR model, with an AUC of 0.775 (95% CI: 0.570-0.979) in external test cohort. The nomogram showed high predictive performance in the training (AUC: 0.883 [95% CI: 0.823-0.943]) and test cohort (AUC: 0.830 [95% CI: 0.657-1.000]) for predicting LVSI. Decision curve analysis demonstrated that the nomogram was clinically useful.

DATA CONCLUSION : Our findings suggest that the proposed nomogram model based on multimodal MRI of CE T1WI-T2WI-combined could be used to assess LVSI status in early cervical cancer.

EVIDENCE LEVEL : 4.

TECHNICAL EFFICACY : Stage 2.

Wu Yu, Wang Shuxing, Chen Yiqing, Liao Yuting, Yin Xuntao, Li Ting, Wang Rui, Luo Xiaomei, Xu Wenchan, Zhou Jing, Wang Simin, Bu Jun, Zhang Xiaochun

2023-Mar-16

cervical cancer, lymphovascular space invasion, machine learning, magnetic resonance imaging, radiomics

In Insights into imaging

BACKGROUND : Structured reporting (SR) is recommended in radiology, due to its advantages over free-text reporting (FTR). However, SR use is hindered by insufficient integration of speech recognition, which is well accepted among radiologists and commonly used for unstructured FTR. SR templates must be laboriously completed using a mouse and keyboard, which may explain why SR use remains limited in clinical routine, despite its advantages. Artificial intelligence and related fields, like natural language processing (NLP), offer enormous possibilities to facilitate the imaging workflow. Here, we aimed to use the potential of NLP to combine the advantages of SR and speech recognition.

RESULTS : We developed a reporting tool that uses NLP to automatically convert dictated free text into a structured report. The tool comprises a task-oriented dialogue system, which assists the radiologist by sending visual feedback if relevant findings are missed. The system was developed on top of several NLP components and speech recognition. It extracts structured content from dictated free text and uses it to complete an SR template in RadLex terms, which is displayed in its user interface. The tool was evaluated for reporting of urolithiasis CTs, as a use case. It was tested using fictitious text samples about urolithiasis, and 50 original reports of CTs from patients with urolithiasis. The NLP recognition worked well for both, with an F1 score of 0.98 (precision: 0.99; recall: 0.96) for the test with fictitious samples and an F1 score of 0.90 (precision: 0.96; recall: 0.83) for the test with original reports.

CONCLUSION : Due to its unique ability to integrate speech into SR, this novel tool could represent a major contribution to the future of reporting.

Jorg Tobias, Kämpgen Benedikt, Feiler Dennis, Müller Lukas, Düber Christoph, Mildenberger Peter, Jungmann Florian

2023-Mar-16

Dialogue system, Natural language processing, Speech recognition, Structured reporting