The Role of Imaging in Health Screening: Screening for Specific Conditions

Ballard, David H.; Burton, Kirsteen R.; Lakomkin, Nikita; Kim, Shannon; Rajiah, Prabhakar; Patel, Midhir J.; Mazaheri, Parisa; Whitman, Gary J.

doi:10.1016/j.acra.2020.03.039

Cited by 8 publications

(6 citation statements)

References 118 publications

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“…For colon cancer, irregular obesity of the intestinal wall on all local or all imaging can be observed on the image. For different clinical drug therapies and treatment methods of these lesions, it is very important to select the appropriate examination method to identify these lesions [ 17 ]. Therefore, through the imaging examination of colon cancer, examination, and comprehensive analysis of clinical symptoms.…”

Section: Discussionmentioning

confidence: 99%

Dark-Lumen Magnetic Resonance Image Based on Artificial Intelligence Algorithm in Differential Diagnosis of Colon Cancer

Fang¹,

Kang

Yang

et al. 2022

Computational Intelligence and Neuroscience

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This research was aimed o investigate the application value and diagnostic effect of dark-lumen magnetic resonance imaging (dark-lumen MRI) based on artificial intelligence algorithm on colon cancer. A total of 98 patients with ulcerated colon cancer were selected as the study subjects. All patients underwent colonic endoscopy. The patients were divided into algorithm group (artificial intelligence algorithm processing image group) and control group (conventional method processing image group) according to different dark-lumen MRI processing methods. The detection efficiency of colon cancer was compared between the two groups. It showed that the diagnostic effect of dark-lumen MRI based on artificial intelligence algorithm was significant. The apparent diffusion coefficient (ADC) in the control group was 0.92 ± 0.14 mm2/s (minimum: 0.74, maximum: 1.30), ADC in the algorithm group was 1.55 ± 0.31 mm2/s (minimum: 1.22, maximum: 2.42). The ADC of patients in algorithm group was significantly higher than that of patients in control group, with statistical difference (t = 7.827, P < 0.001). The correct number of cases was 46 and the diagnostic error number was 3 in algorithm group, with accuracy of 93%. The correct number of cases was 41 and the diagnostic error number was 8 in control group, with accuracy of 83%. In comparison, the correct rate was 10% higher in algorithm group, indicating that the diagnostic effect was better in algorithm group. The mean value of invasion depth was 10.42 in the algorithm group and 5.27 in the control group, indicating that the algorithm group was more accurate in the judgment of invasion depth, had a good prospect of clinical application, and had guiding significance for the diagnosis of colon cancer.

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Section: Discussionmentioning

confidence: 99%

Dark-Lumen Magnetic Resonance Image Based on Artificial Intelligence Algorithm in Differential Diagnosis of Colon Cancer

Fang¹,

Kang

Yang

et al. 2022

Computational Intelligence and Neuroscience

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“…The nonspecific BPEinduced coloring may therefore reduce the possible role of CAD as a detection tool and help to highlight the challenge of breast MRI interpretation in lactating patients. Additional studies with pathological findings are warranted in order to further evaluate CAD's diagnostic and prognostic capabilities in this unique population (47), as well as the optimal timing upon lactation weaning in the setting of screening among high risk patients (48).…”

Section: Discussionmentioning

confidence: 99%

MRI of the Lactating Breast: Computer-Aided Diagnosis False Positive Rates and Background Parenchymal Enhancement Kinetic Features

et al. 2022

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Rationale and Objectives: To investigate the application of computer-added diagnosis (CAD) in dynamic contrast-enhanced (DCE) MRI of the healthy lactating breast, focusing on false-positive rates and background parenchymal enhancement (BPE) coloring patterns in comparison with breast cancer features in non-lactating patients. Materials and Methods:The study population was composed of 58 healthy lactating patients and control groups of 113 healthy premenopausal non-lactating patients and 55 premenopausal non-lactating patients with newly-diagnosed breast cancer. Patients were scanned on 1.5-T MRI using conventional DCE protocol. A retrospective analysis of DCE-derived CAD properties was conducted using a commercial software that is regularly utilized in our routine radiological work-up. Qualitative morphological characterization and automaticallyobtained quantitative parametric measurements of the BPE-induced CAD coloring were categorized and subgroups' trends and differences between the lactating and cancer cohorts were statistically assessed.Results: CAD false-positive coloring was found in the majority of lactating cases (87%). Lactation BPE coloring was characteristically non-mass enhancement (NME)-like shaped (87%), bilateral (79%) and symmetric (64%), whereas, unilateral coloring was associated with prior irradiation (p <0.0001). Inter-individual variability in CAD appearance of both scoring-grade and kinetic-curve dominance was found among the lactating cohort. When compared with healthy non-lactating controls, CAD false positive probability was significantly increased [Odds ratio 40.2, p <0001], while in comparison with the breast cancer cohort, CAD features were mostly inconclusive, even though increased size parameters were significantly associated with lactation-BPE (p <0.00001).Conclusion: BPE was identified as a common source for false-positive CAD coloring on breast DCE-MRI among lactating population. Despite several typical characteristics, overlapping features with breast malignancy warrant a careful evaluation and clinical correlation in all cases with suspected lactation induced CAD coloring.

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“…Imágenes como radiografía de tórax (RX) y tomografía computarizada (TC) son herramientas eficaces en pacientes sospechosos o diagnosticados con SARS-CoV-2, pues pueden utilizarse para el tamizaje, seguimiento y evolución de las alteraciones pulmonares provocadas por este virus 8,9 . Dentro de los principales hallazgos radiográficos en pacientes con COVID-19 confirmada se encuentran opacidades pulmonares con patrón en vidrio esmerilado, focos de consolidación y engrosamiento intersticial con patrón de tipo empedrado 10 .…”

Section: Introductionunclassified

Radiografía y tomografía de tórax como herramienta diagnóstica y pronóstica en pacientes con COVID-19

Sebelén¹,

Jiménez-López²,

Colón-Arias³

et al. 2022

ARM

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The Role of Imaging in Health Screening: Screening for Specific Conditions

Cited by 8 publications

References 118 publications

Dark-Lumen Magnetic Resonance Image Based on Artificial Intelligence Algorithm in Differential Diagnosis of Colon Cancer

Dark-Lumen Magnetic Resonance Image Based on Artificial Intelligence Algorithm in Differential Diagnosis of Colon Cancer

MRI of the Lactating Breast: Computer-Aided Diagnosis False Positive Rates and Background Parenchymal Enhancement Kinetic Features

Radiografía y tomografía de tórax como herramienta diagnóstica y pronóstica en pacientes con COVID-19

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