The pros and cons of lung cancer screening

Ledda, Roberta Eufrasia; Funk, Georg-Christian; Sverzellati, Nicola

doi:10.1007/s00330-024-10939-6

Eur Radiol

2024

DOI: 10.1007/s00330-024-10939-6

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The pros and cons of lung cancer screening

Roberta Eufrasia Ledda,

Georg-Christian Funk,

Nicola Sverzellati

Abstract: Several trials have shown that low-dose computed tomography-based lung cancer screening (LCS) allows a substantial reduction in lung cancer-related mortality, carrying the potential for other clinical benefits. There are, however, some uncertainties to be clarified and several aspects to be implemented to optimize advantages and minimize the potential harms of LCS. This review summarizes current evidence on LCS, discussing some of the well-established and potential benefits, including lung cancer… Show more

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Cited by 3 publications

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External validation of the performance of commercially available deep-learning-based lung nodule detection on low-dose CT images for lung cancer screening in Japan

Fukumoto,

Yamashita,

Kawashita

et al. 2024

Jpn J Radiol

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Purpose Artificial intelligence (AI) algorithms for lung nodule detection have been developed to assist radiologists. However, external validation of its performance on low-dose CT (LDCT) images is insufficient. We examined the performance of the commercially available deep-learning-based lung nodule detection (DL-LND) using LDCT images at Japanese lung cancer screening (LCS). Materials and methods Included were 43 patients with suspected lung cancer on LDCT images and pathologically confirmed lung cancer. The reference standard for nodules whose diameter exceeded 4 mm was set by a radiologist who referred to the reports of two other radiologists reading the LDCT images. After we applied commercially available DL-LND to the LDCT images, the radiologist reviewed all nodules detected by DL-LND. When he failed to identify an existing nodule, it was also included in the reference standard. To validate the performance of DL-LND, the sensitivity for lung nodules and lung cancer, the positive-predictive value (PPV) for lung nodules, and the mean number of false-positive (FP) nodules per CT scan were recorded. Results The radiologist detected 97 nodules including 43 lung cancers and missed 3 solid nodules detected by DL-LND. A total of 100 nodules was included in the reference standard. DL-LND detected 396 nodules including 40 lung cancers. The sensitivity for the 100 nodules was 96.0%; the PPV was 24.2% (96/396). The mean number of FP nodules per CT scan was 7.0; sensitivity for lung cancer was 93.0% (40/43). DL-LND missed three lung cancers; 2 of these were atypical pulmonary cysts. Conclusion We externally verified that the sensitivity for lung nodules and lung cancer by DL-LND was very high. However, its low PPV and the increased FP nodules remains a serious drawback of DL-LND.

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External validation of the performance of commercially available deep-learning-based lung nodule detection on low-dose CT images for lung cancer screening in Japan

Fukumoto,

Yamashita,

Kawashita

et al. 2024

Jpn J Radiol

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Nanoscale Extracellular Vesicle-Enabled Liquid Biopsy: Advances and Challenges for Lung Cancer Detection

Khan,

Raza,

2024

Micromachines

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Lung cancer is responsible for the death of over a million people worldwide every year. With its high mortality rate and exponentially growing number of new cases, lung cancer is a major threat to public health. The high mortality and poor survival rates of lung cancer patients can be attributed to its stealth progression and late diagnosis. For a long time, intrusive tissue biopsy has been considered the gold standard for lung cancer diagnosis and subtyping; however, the intrinsic limitations of tissue biopsy cannot be overlooked. In addition to being invasive and costly, it also suffers from limitations in sensitivity and specificity, is not suitable for repeated sampling, provides restricted information about the tumor and its molecular landscape, and is inaccessible in several cases. To cope with this, advancements in diagnostic technologies, such as liquid biopsy, have shown great prospects. Liquid biopsy is an innovative non-invasive approach in which cancer-related components called biomarkers are detected in body fluids, such as blood, urine, saliva and others. It offers a less invasive alternative with the potential for applications such as routine screening, predicting treatment outcomes, evaluating treatment effectiveness, detecting residual disease, or disease recurrence. A large number of research articles have indicated extracellular vesicles (EVs) as ideal biomarkers for liquid biopsy. EVs are a heterogeneous collection of membranous nanoparticles with diverse sizes, contents, and surface markers. EVs play a critical role in pathophysiological states and have gained prominence as diagnostic and prognostic biomarkers for multiple diseases, including lung cancer. In this review, we provide a detailed overview of the potential of EV-based liquid biopsy for lung cancer. Moreover, it highlights the strengths and weaknesses of various contemporary techniques for EV isolation and analysis in addition to the challenges that need to be addressed to ensure the widespread clinical application of EV-based liquid biopsies for lung cancer. In summary, EV-based liquid biopsies present interesting opportunities for the development of novel diagnostic and prognostic platforms for lung cancer, one of the most abundant cancers responsible for millions of cancer-related deaths worldwide.

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Indolent Adenocarcinoma In Situ Mistaken as Aggressive during Lung Cancer Screening Program: To Heal or to Harm?

Orlandi,

Mattioni,

Rolli

et al. 2024

Surgeries

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Background: Low-dose computed tomography-based lung cancer screening (LCS) has demonstrated efficacy in reducing lung cancer mortality. However, concerns about overdiagnosis and overtreatment hinder global LCS implementation. Methods: Here, we report the unique case of a slow-growing 1 cm pure ground-glass opacity (GGO) of the lung, known for 15 years, which unexpectedly developed into a 5 cm mixed GGO within 1 year, with an increased solid component and FDG-PET uptake. Results: The patient, asymptomatic, underwent right upper lobectomy and lymphadenectomy, even complicated with postoperative chylothorax, later revealing to be affected by only an unchanged adenocarcinoma in situ (AIS). Conclusions: This case serves as a reminder of the potential behavior of pre-invasive lesions, which can mimic invasive neoplasia and may lead to overtreatment, and underscores the challenge of distinguishing indolent lesions from potentially aggressive malignancies in LCS, highlighting the need for the ongoing refinement of LCS protocols to mitigate this risk.

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The pros and cons of lung cancer screening

Cited by 3 publications

References 91 publications

External validation of the performance of commercially available deep-learning-based lung nodule detection on low-dose CT images for lung cancer screening in Japan

External validation of the performance of commercially available deep-learning-based lung nodule detection on low-dose CT images for lung cancer screening in Japan

Nanoscale Extracellular Vesicle-Enabled Liquid Biopsy: Advances and Challenges for Lung Cancer Detection

Indolent Adenocarcinoma In Situ Mistaken as Aggressive during Lung Cancer Screening Program: To Heal or to Harm?

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