Prediction of visceral pleural invasion in lung cancer on CT: deep learning model achieves a radiologist-level performance with adaptive sensitivity and specificity to clinical needs

Choi, Hyewon; Kim, Hyungjin; Hong, Wonju; Park, Jongsoo; Hwang, Eui Jin; Park, Chang Min; Kim, Young Tae; Goo, Jin Mo

doi:10.1007/s00330-020-07431-2

Cited by 25 publications

(19 citation statements)

References 30 publications

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“…Among various characteristic malignant traits, researchers have extensively explored possibility of preoperative evaluation of VPI using various radiographic parameters (28). It has been documented in the literature that the CT features of VPI can be reliably used, with a fair degree of accuracy, ranging from 71-95%, to detect its presence (34). This detection, in turn, aids to properly stage tumors preoperatively, in which tumors otherwise meeting the criteria for T1N0M0 classification would be re-staged as T2N0M0 in light of VPI presence, as recommended by the American Joint Committee on Cancer (AJCC) guidelines.…”

Section: Discussionmentioning

confidence: 99%

The combination of computed tomography features and circulating tumor cells increases the surgical prediction of visceral pleural invasion in clinical T1N0M0 lung adenocarcinoma

Shi¹,

Li²,

Yang³

et al. 2021

Transl Lung Cancer Res

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Background: Visceral pleural invasion (VPI) is a clinical manifestation associated with a poor prognosis, and diagnosing it preoperatively is highly imperative for successful sublobar resection of these peripheral tumors. We evaluated the roles of computed tomography (CT) features and circulating tumor cells (CTCs) for improving VPI detection in patients with clinical T1N0M0 invasive lung adenocarcinoma.Methods: Three hundred and ninety-one patients were reviewed retrospectively in this study, of which 234 presented with a pleural tag or pleural contact on CT images. CTCs positive for the foliate receptors were enriched and analyzed prior to surgery. Logistic regression analyses were performed to assess the association of CT features and CTCs with VPI, and the receiver operating characteristic (ROC) curve was generated to compare the predictive power of these variables.Results: Patients mostly underwent either segmentectomies (18.9%) or lobectomies (79.0%). Only 49 of the 234 patients with pleural involvement on CT showed pathologically confirmed VPI. Multivariate logistic regression analysis revealed that CTC level ≥10.42 FU/3 mL was a significant VPI risk factor for invasive adenocarcinoma cases ≤30 mm [adjusted odds ratio (OR) =4.62, 95% confidence interval (CI): 2.05-10.44, P<0.001]. Based on CT features, subgroup analyses showed that the solid portion size was a statistically significant independent predictor of VPI for these peripheral nodules with pleural tag, while the solid portion length of the interface was an independent predictor of pleural contact. The receiver operating curve analyses showed that the combination of CTC and CT features were highly predictive of VPI [area under the curve (AUC) =0.921 for pleural contact and 0.862 for the pleural tag, respectively].Conclusions: CTC, combined with CT features of pleural tag or pleural contact, could significantly improve VPI detection in invasive lung adenocarcinomas at clinical T1N0M0 stage prior to the patient's surgery.

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Section: Discussionmentioning

confidence: 99%

The combination of computed tomography features and circulating tumor cells increases the surgical prediction of visceral pleural invasion in clinical T1N0M0 lung adenocarcinoma

Shi¹,

Li²,

Yang³

et al. 2021

Transl Lung Cancer Res

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“…In an external test consisting of 141 patients, the model had an AUC of 0.75 for VPI, comparable to the three thoracic radiologists' evaluations (AUC range, 0.73-0.79). At the cutoffs that showed 90% sensitivity and specificity in the internal test set, the algorithm had comparable to higher sensitivity and higher specificity than the radiologists (78). Finally, the model's output was an independent predictor for VPI in multivariate logistic regression in conjunction with the clinical stage and nodule type (77,78).…”

Section: Lung Cancer Stagingmentioning

confidence: 94%

“…Visceral pleural invasion (VPI) by lung cancer is an isolated T2 descriptor due to its adverse prognostic implication after adjustment for the pathologic T category (77). Choi et al developed an in-house DL algorithm to predict VPI, using 676 patients with clinical stage 1A lung adenocarcinoma (78). In an external test consisting of 141 patients, the model had an AUC of 0.75 for VPI, comparable to the three thoracic radiologists' evaluations (AUC range, 0.73-0.79).…”

Section: Lung Cancer Stagingmentioning

confidence: 99%

A narrative review of deep learning applications in lung cancer research: from screening to prognostication

Lee¹,

Hwang²,

Kim³

et al. 2022

Transl Lung Cancer Res

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Background and Objective: Deep learning (DL) algorithms have been developed for various tasks, including lung nodule detection on chest radiographs or lung cancer computed tomography screening, potential candidate selection in lung cancer screening, malignancy prediction for indeterminate pulmonary nodules, lung cancer staging, treatment response prediction, prognostication, and prediction of genetic mutations in lung cancer. Furthermore, these DL algorithms have been applied in various clinical settings in order for them to be generalized in real-world clinical practice. Multiple DL algorithms have been corroborated to be on par with experts or current clinical prediction models for several specific tasks. However, no article has yet comprehensively reviewed DL algorithms dedicated to lung cancer research. This narrative review presents an overview of the literature dealing with DL techniques applied in lung cancer research and briefly summarizes the results according to the DL algorithms' clinical use cases. Methods: we performed a narrative review by searching the Embase and OVID-MEDLINE databases for articles published in English from October, 2016 until September, 2021 and reviewing the bibliographies of key references to identify important literature related to DL in lung cancer research. The background, development, results, and clinical implications of each DL algorithm are briefly discussed. Lastly, we end this review article by highlighting future directions in lung cancer research using DL techniques.Key Content and Findings: DL algorithms have been introduced to show comparable or higher performance than human experts in various clinical settings. Specifically, they have been actively applied to detect lung nodules in chest radiographs or computed tomography (CT) examinations, optimize candidate selection for lung cancer screening (LCS), predict the malignancy of lung nodules, stage lung cancer, and predict treatment response, patients' prognoses, and genetic mutations in lung cancers.Conclusions: DL algorithms have corroborated their potential value for various tasks, ranging from lung cancer screening to prognostication of lung cancer patients. Future research is warranted for the clinical application of these algorithms in daily clinical practice and verification of their real-world clinical usefulness.

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“…The gold standard for the treatment of early-stage lung cancer is surgical resection. AI was applied to pre-surgical evaluation [61,62], and prognosis prediction after surgery, and could help identify patients who are suitable to receive adjuvant chemotherapy after surgery [54].…”

Section: Surgerymentioning

confidence: 99%

“…In pre-surgical evaluation, radiologist-level AI could help predict visceral pleural invasion [62], and identify early stage lung adenocarcinomas suitable for sub-lobar resection [61]. After surgery, AI could play a role in predicting prognosis.…”

Section: Surgerymentioning

confidence: 99%

Application of Artificial Intelligence in Lung Cancer

Chiu

Chao

Chen

2022

Cancers

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Lung cancer is the leading cause of malignancy-related mortality worldwide due to its heterogeneous features and diagnosis at a late stage. Artificial intelligence (AI) is good at handling a large volume of computational and repeated labor work and is suitable for assisting doctors in analyzing image-dominant diseases like lung cancer. Scientists have shown long-standing efforts to apply AI in lung cancer screening via CXR and chest CT since the 1960s. Several grand challenges were held to find the best AI model. Currently, the FDA have approved several AI programs in CXR and chest CT reading, which enables AI systems to take part in lung cancer detection. Following the success of AI application in the radiology field, AI was applied to digitalized whole slide imaging (WSI) annotation. Integrating with more information, like demographics and clinical data, the AI systems could play a role in decision-making by classifying EGFR mutations and PD-L1 expression. AI systems also help clinicians to estimate the patient’s prognosis by predicting drug response, the tumor recurrence rate after surgery, radiotherapy response, and side effects. Though there are still some obstacles, deploying AI systems in the clinical workflow is vital for the foreseeable future.

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Prediction of visceral pleural invasion in lung cancer on CT: deep learning model achieves a radiologist-level performance with adaptive sensitivity and specificity to clinical needs

Cited by 25 publications

References 30 publications

The combination of computed tomography features and circulating tumor cells increases the surgical prediction of visceral pleural invasion in clinical T1N0M0 lung adenocarcinoma

The combination of computed tomography features and circulating tumor cells increases the surgical prediction of visceral pleural invasion in clinical T1N0M0 lung adenocarcinoma

A narrative review of deep learning applications in lung cancer research: from screening to prognostication

Application of Artificial Intelligence in Lung Cancer

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