Status and perspectives of detection by low-dose computed tomography or computed radiography in surgical patients with lung cancer, based on a five-year study

Background

A retrospective study involving 502 lung cancer patients who had received pulmonary resection from 2009–2013 was conducted in order to compare the clinical characteristics of patients whose diagnosis was detected by low‐dose computed tomography (LDCT) and computed radiography (CR).

Methods

Two groups were established, based on the method of detection: the LDCT group included 172 lung cancer patients; the CR group included 330 lung cancer patients. The evolution of proportions of patients in urban and rural regions was also analyzed, according to detection method.

Results

The percentage of patients with stage I was higher in the LDCT group than in the CR group (77.3%, 133/172 vs. 53.6%, 177/330). The incidence of postoperative complications within 30 days was significantly lower in the LDCT than in CR group (25.0% vs. 33.6%). The proportion of patients detected by LDCT or CR in urban regions was constantly higher than in rural regions (with an increase of 13.0% vs. 5.9%); the proportion of LDCT‐detected patients in urban regions was constantly higher than in rural regions (with an increase of 8.7% vs. 5.9%).

Conclusions

LDCT contributes to a higher proportion of early lung cancer diagnoses and a lower incidence of postoperative complications in surgical patients. The proportions of patients detected early and by LDCT have both increased steadily during the last five years. These two trends are more pronounced in urban compared with rural patients.

Screening for lung cancer using thin-slice low-dose computed tomography in southwestern China: a population-based real-world study

OBJECTIVES

Lung cancer is one of the most common malignant tumors threatening human life and health. At present, low-dose computed tomography (LDCT) screening for the high-risk population to achieve early diagnosis and treatment of lung cancer has become the first choice recommended by many authoritative international medical organizations. To further optimize the lung cancer screening method, we conducted a real-world study of LDCT lung cancer screening in a large sample of a healthy physical examination population, comparing differences in lung nodules and lung cancer detection between thin and thick-slice LDCT scanning.

METHODS

A total of 29 296 subjects who underwent low-dose thick-slice CT scanning (5 mm thickness) from January 2015 to December 2015 and 28 058 subjects who underwent low-dose thin-slice CT scanning (1 mm thickness) from January 2018 to December 2018 in West China Hospital were included. The positive detection rate, detection rate of lung cancer, pathological stage of lung cancer, and mortality rate of lung cancer were analyzed and compared between the two groups.

RESULTS

The positive rate of LDCT screening in the thin-slice scanning group was significantly higher than that in the thick-slice scanning group (20.1% vs. 14.4%, p < 0.001). In addition, the lung cancer detection rate in the thin-slice LDCT screening positive group was significantly higher than that in the thick-slice scanning group (78.0% vs. 52.9%, p < 0.001).

CONCLUSIONS

The screening positive rate of low-dose thin-slice CT scanning is higher and more early-stage lung cancer (IA1 stage) can be detected in the screen-positive group.

The American Cancer Society National Lung Cancer Roundtable strategic plan: Lung cancer in women

Lung cancer in women is a modern epidemic and represents a global health crisis. Cigarette smoking remains the most important risk factor for lung cancer in all patients and, among women globally, rates of smoking continue to increase. Although some data exist supporting sex-based differences across the continuum of lung cancer, there is currently a dearth of research exploring the differences in risk, biology, and treatment outcomes in women. Consequently, the American Cancer Society National Lung Cancer Roundtable recognizes the urgent need to promote awareness and future research that will close the knowledge gaps regarding lung cancer in women. To this end, the American Cancer Society National Lung Cancer Roundtable Task Group on Lung Cancer in Women convened a summit undertaking the following to: (1) summarize existing evidence and identify knowledge gaps surrounding the epidemiology, risk factors, biologic differences, and outcomes of lung cancer in women; (2) develop and prioritize research topics and questions that address research gaps and advance knowledge to improve quality of care of lung cancer in women; and (3) propose strategies for future research. PLAIN LANGUAGE SUMMARY: Lung cancer is the leading cause of cancer mortality in women, and, despite comparatively lower exposures to occupational and environmental carcinogens compared with men, disproportionately higher lung cancer rates in women who ever smoked and women who never smoked call for increased awareness and research that will close the knowledge gaps regarding lung cancer in women.

Leveraging prior knowledge in machine intelligence to improve lesion diagnosis for early cancer detection

BACKGROUND

Experts' interpretations of medical images for lesion diagnosis may not always align with the underlying in vivo tissue pathology and, therefore, cannot be considered the definitive truth regarding malignancy or benignity. While current machine learning (ML) models in medical imaging can replicate expert interpretations, their results may also diverge from the actual ground truth.

PURPOSE

This study investigates various factors contributing to these discrepancies and proposes solutions.

METHODS

The central idea of the proposed solution is to integrate prior knowledge into ML models to enhance the characterization of in vivo tissues. The incorporation of prior knowledge into decision-making is task-specific, tailored to the data acquired for that task. This central idea was tested on the diagnosis of lesions using low dose computed tomography (LdCT) for early cancer detection, particularly focusing on more challenging, ambiguous or indeterminate lesions (IDLs) as classified by experts. One key piece of prior knowledge involves CT x-ray energy spectrum, where different energies interact with in vivo tissues within a lesion, producing variable but reproducible image contrasts that encapsulate biological information. Typically, CT imaging devices use only the high-energy portion of this spectrum for data acquisition; however, this study considers the full spectrum for lesion diagnostics. Another critical aspect of prior knowledge includes the functional or dynamic properties of in vivo tissues, such as elasticity, which can indicate pathological conditions. Instead of relying solely on abstract image features as current ML models do, this study extracts these tissue pathological characteristics from the image contrast variations.

RESULTS

The method was tested on LdCT images of four sets of IDLs, including pulmonary nodules and colorectal polyps, with pathological reports serving as the ground truth for malignancy or benignity. The method achieved an area under the receiver operating characteristic curve (AUC) of 0.98 ± 0.03, demonstrating a significant improvement over existing state-of-the-art ML models, which typically have AUCs in the 0.70 range.

CONCLUSION

Leveraging prior knowledge in machine intelligence can enhance lesion diagnosis, resolve the ambiguity of IDLs interpreted by experts, and improve the effectiveness of LdCT screening for early-stage cancers.