Imaging phenotype using radiomics to predict dry pleural dissemination in non-small cell lung cancer

Yang, Mo; Ren, Yijiu; She, Yunlang; Xie, Dong; Sun, Xuejun; Shi, Jingyun; Zhao, Guofang; Chen, Chang

doi:10.21037/atm.2019.05.20

Cited by 12 publications

(13 citation statements)

References 26 publications

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“…In general, studies sought to evaluate whether or not radiomic signatures could outperform existing methods for patient risk stratification. 20 studies related radiomics to overall survival [ [13] , [14] , [15] , [16] , [17] , [18] , [19] , [20] , [21] , [22] , [23] , [24] , [25] , [26] , [27] , [28] , [29] , [30] , [31] , [32] ], 18 to the likelihood of local or metastatic recurrence [ [18] , [19] , [20] , [32] , [33] , [34] , [35] , [36] , [37] , [38] , [39] , [40] , [41] , [42] , [43] , [44] , [45] , [46] ], 6 to response, disease-free or progression-free survival [ 32 , [47] , [48] , [49] , [50] , [51] ], and 2 to staging [ 54 , 55 ]. Two further studies focused on the association of radiomics signatures to lung toxicity [ 52 , 53 ], Four studies investigated multiple endpoints.…”

Section: The Potential Of Radiomics For Personalized Decision-making mentioning

confidence: 99%

Radiomics as a personalized medicine tool in lung cancer: Separating the hope from the hype

et al. 2020

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Radiomics has become a popular image analysis method in the last few years. Its key hypothesis is that medical images harbor biological, prognostic and predictive information that is not revealed upon visual inspection. In contrast to previous work with a priori defined imaging biomarkers, radiomics instead calculates image features at scale and uses statistical methods to identify those most strongly associated to outcome. This builds on years of research into computer aided diagnosis and pattern recognition. While the potential of radiomics to aid personalized medicine is widely recognized, several technical limitations exist which hinder biomarker translation. Aspects of the radiomic workflow lack repeatability or reproducibility under particular circumstances, which is a key requirement for the translation of imaging biomarkers into clinical practice. One of the most commonly studied uses of radiomics is for personalized medicine applications in Non-Small Cell Lung Cancer (NSCLC). In this review, we summarize reported methodological limitations in CT based radiomic analyses together with suggested solutions. We then evaluate the current NSCLC radiomics literature to assess the risk associated with accepting the published conclusions with respect to these limitations. We review different complementary scoring systems and initiatives that can be used to critically appraise data from radiomics studies. Wider awareness should improve the quality of ongoing and future radiomics studies and advance their potential as clinically relevant biomarkers for personalized medicine in patients with NSCLC.

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Section: The Potential Of Radiomics For Personalized Decision-making mentioning

confidence: 99%

Radiomics as a personalized medicine tool in lung cancer: Separating the hope from the hype

et al. 2020

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“…DPD and minimal PE were radiologically defined as “multiple small pleural nodules and/or uneven pleural thickenings without PE,” 5–9 and “PE within 10 mm thickness that is too little to be punctured, with or without pleural lesions,” 10 respectively.…”

Section: Methodsmentioning

confidence: 99%

“…Conversely, in cases of dry pleural dissemination (DPD), defined as solid pleural metastases without PE 5–9 or minimal PE (<10 mm thick) on CT scans, 10 which are considered early stages of pleural metastases, it is difficult to radiologically differentiate between benign and malignant lesions, and thoracentesis is not recommended 11 . Most of these cases are clinically diagnosed as M0, and unexpected metastases are discovered intraoperatively.…”

Section: Introductionmentioning

confidence: 99%

Pleural staging using local anesthetic thoracoscopy in dry pleural dissemination and minimal pleural effusion

et al. 2021

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Background Dry pleural dissemination (DPD) and minimal (<10 mm thick) pleural effusion (PE) may be discovered intraoperatively as unexpected metastases. A definitive diagnostic procedure such as pleural biopsy is rarely attempted in such patients preoperatively. We retrospectively investigated the use and safety of local anesthetic thoracoscopy (LAT) as a pleural staging tool in the diagnosis of DPD and minimal PE. Methods We reviewed 18 patients with non‐small cell lung cancer (radiological DPD and minimal PE in 13 and five patients, respectively) who underwent LAT using a flex‐rigid pleuroscope for pleural staging from April 2015 to September 2020. Results The median age of the patients was 72 years. Nine patients (50%) were men. The dominant histological type was adenocarcinoma (n = 16). Three patients each with radiological DPD and minimal PE had visible PE on the LAT. Pleural biopsy was performed in the 16 cases in which pleural abnormalities were identified. On pleural staging, five cases were diagnosed without pleural dissemination (M0), and 13 cases were diagnosed with pleural dissemination (M1a). Only one case in which the lesion could not be identified because of pleural adhesions was false‐negative. The success rates for pleural staging, sensitivity, specificity, positive predictive value, and negative predictive value were 94.4% (17/18), 92.8% (13/14), 100% (4/4), 100% (13/13), and 80.0% (4/5), respectively. There were no lung lacerations or other severe complications caused by the procedure or during blunt dissection. Conclusion LAT might be a useful tool for accurate pleural staging in cases with DPD and minimal PE suspected radiologically.

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“…Several studies have shown that handcrafted and deep radiomic features of the intra/peri-tumor can be used as biomarkers to predict lymph node metastases [75][76][77]. Furthermore, in the case of pleural metastases, radiomic features may have a diagnostic power with AUC of 0.93 [78].…”

Section: Applications Of Structural Radiomic Features In Lung Cancermentioning

confidence: 99%

Structural and functional radiomics for lung cancer

Jochems

Ibrahim

et al. 2021

Eur J Nucl Med Mol Imaging

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Introduction Lung cancer ranks second in new cancer cases and first in cancer-related deaths worldwide. Precision medicine is working on altering treatment approaches and improving outcomes in this patient population. Radiological images are a powerful non-invasive tool in the screening and diagnosis of early-stage lung cancer, treatment strategy support, prognosis assessment, and follow-up for advanced-stage lung cancer. Recently, radiological features have evolved from solely semantic to include (handcrafted and deep) radiomic features. Radiomics entails the extraction and analysis of quantitative features from medical images using mathematical and machine learning methods to explore possible ties with biology and clinical outcomes. Methods Here, we outline the latest applications of both structural and functional radiomics in detection, diagnosis, and prediction of pathology, gene mutation, treatment strategy, follow-up, treatment response evaluation, and prognosis in the field of lung cancer. Conclusion The major drawbacks of radiomics are the lack of large datasets with high-quality data, standardization of methodology, the black-box nature of deep learning, and reproducibility. The prerequisite for the clinical implementation of radiomics is that these limitations are addressed. Future directions include a safer and more efficient model-training mode, merge multi-modality images, and combined multi-discipline or multi-omics to form “Medomics.”

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Imaging phenotype using radiomics to predict dry pleural dissemination in non-small cell lung cancer

Cited by 12 publications

References 26 publications

Radiomics as a personalized medicine tool in lung cancer: Separating the hope from the hype

Radiomics as a personalized medicine tool in lung cancer: Separating the hope from the hype

Pleural staging using local anesthetic thoracoscopy in dry pleural dissemination and minimal pleural effusion

Structural and functional radiomics for lung cancer

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