Cancer Biomarkers - Volume Pre-press, issue Pre-press

Authors: Masquelin, Axel H. | Cheney, Nick | José Estépar, Raúl San | Bates, Jason H.T. | Kinsey, C. Matthew

Article Type: Research Article

Abstract: BACKGROUND: Continued improvement in deep learning methodologies has increased the rate at which deep neural networks are being evaluated for medical applications, including diagnosis of lung cancer. However, there has been limited exploration of the underlying radiological characteristics that the network relies on to identify lung cancer in computed tomography (CT) images. OBJECTIVE: In this study, we used a combination of image masking and saliency activation maps to systematically explore the contributions of both parenchymal and tumor regions in a CT image to the classification of indeterminate lung nodules. METHODS: We selected individuals …from the National Lung Screening Trial (NLST) with solid pulmonary nodules 4–20 mm in diameter. Segmentation masks were used to generate three distinct datasets; 1) an Original Dataset containing the complete low-dose CT scans from the NLST, 2) a Parenchyma-Only Dataset in which the tumor regions were covered by a mask, and 3) a Tumor-Only Dataset in which only the tumor regions were included. RESULTS: The Original Dataset significantly outperformed the Parenchyma-Only Dataset and the Tumor-Only Dataset with an AUC of 80.80 ± 3.77% compared to 76.39 ± 3.16% and 78.11 ± 4.32%, respectively. Gradient-weighted class activation mapping (Grad-CAM) of the Original Dataset showed increased attention was being given to the nodule and the tumor-parenchyma boundary when nodules were classified as malignant. This pattern of attention remained unchanged in the case of the Parenchyma-Only Dataset. Nodule size and first-order statistical features of the nodules were significantly different with the average malignant and benign nodule maximum 3d diameter being 23 mm and 12 mm, respectively. CONCLUSION: We conclude that network performance is linked to textural features of nodules such as kurtosis, entropy and intensity, as well as morphological features such as sphericity and diameter. Furthermore, textural features are more positively associated with malignancy than morphological features. Show more

Keywords: Lung cancer, convolutional neural networks, low-dose computed tomography, feature attribution

DOI: 10.3233/CBM-230444

Citation: Cancer Biomarkers, vol. Pre-press, no. Pre-press, pp. 1-10, 2024

Authors: Steiner, Dylan | Park, Ju Ae | Singh, Sarah | Potter, Austin | Scalera, Jonathan | Beane, Jennifer | Suzuki, Kei | Lenburg, Marc E. | Burks, Eric J.

Article Type: Research Article

Abstract: BACKGROUND: Histologic grading of lung adenocarcinoma (LUAD) is predictive of outcome but is only possible after surgical resection. A radiomic biomarker predictive of grade has the potential to improve preoperative management of early-stage LUAD. OBJECTIVE: Validate a prognostic radiomic score indicative of lung cancer aggression (SILA) in surgically resected stage I LUAD (n = 161) histologically graded as indolent low malignant potential (LMP), intermediate, or aggressive vascular invasive (VI) subtypes. METHODS: The SILA scores were generated from preoperative CT-scans using the previously validated Computer-Aided Nodule Assessment and Risk …Yield (CANARY) software. RESULTS: Cox proportional regression showed significant association between the SILA and 7-year recurrence-free survival (RFS) in a univariate (p < 0.05) and multivariate (p < 0.05) model incorporating age, gender, smoking status, pack years, and extent of resection. The SILA was positively correlated with invasive size (spearman r = 0.54, p = 8.0 × 10 - 14 ) and negatively correlated with percentage of lepidic histology (spearman r = - 0.46, p = 7.1 × 10 - 10 ). The SILA predicted indolent LMP with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.74 and aggressive VI with an AUC of 0.71, the latter remaining significant when invasive size was included as a covariate in a logistic regression model (p < 0.01). CONCLUSIONS: The SILA scoring of preoperative CT scans was prognostic and predictive of resected pathologic grade. Show more

Keywords: Lung adenocarcinoma, vascular invasion, radiomic biomarkers, SILA, indolent lung cancer

DOI: 10.3233/CBM-230456

Citation: Cancer Biomarkers, vol. Pre-press, no. Pre-press, pp. 1-12, 2024

Authors: Burks, Eric J. | Sullivan, Travis B. | Rieger-Christ, Kimberly M.

Article Type: Research Article

Abstract: BACKGROUND: The national lung screening trial (NLST) demonstrated a reduction in lung cancer mortality with lowdose CT (LDCT) compared to chest x-ray (CXR) screening. Overdiagnosis was high (79%) among bronchoalveolar carcinoma (BAC) currently replaced by adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA) and adenocarcinoma of low malignant potential (LMP) exhibiting 100% disease specific survival (DSS). OBJECTIVE: Compare the outcomes and proportions of BAC, AIS, MIA, and LMP among NLST screendetected stage IA NSCLC with overdiagnosis rate. METHODS: Whole slide images were reviewed by a thoracic pathologist from 174 of 409 NLST screen-detected …stage IA LUAD. Overdiagnosis rates were calculated from follow-up cancer incidence rates. RESULTS: Most BAC were reclassified as AIS/MIA/LMP (20/35 = 57%). The 7-year DSS was 100% for AIS/MIA/LMP and 94% for BAC. Excluding AIS/MIA/LMP, BAC behaved similarly to NSCLC (7-year DSS: 86% vs. 83%, p = 0.85) The overdiagnosis rate of LDCT stage IA NSCLC was 16.6% at 11.3-years, matching the proportion of AIS/MIA/LMP (16.2%) but not AIS/MIA (3.5%) or BAC (22.8%). CONCLUSIONS: AIS/MIA/LMP proportionally matches the overdiagnosis rate among stage IA NSCLC in the NLST, exhibiting 100% 7-year DSS. Biomarkers designed to recognize AIS/MIA/LMP preoperatively, would be useful to prevent overtreatment of indolent screen-detected cancers. Show more

Keywords: NLST, overdiagnosis, LMP, AIS, MIA

DOI: 10.3233/CBM-230452

Citation: Cancer Biomarkers, vol. Pre-press, no. Pre-press, pp. 1-11, 2024

Authors: Praygod, Tarimo Fredrick | Li, Jinlong | Li, Hongwei | Tan, Wanlong | Hu, Zhiming | Zhou, Li

Article Type: Research Article

Abstract: RNA-binding protein (RBP) plays pivotal roles in the malignant progression of cancer by regulating gene expression. In this paper, we aimed to develop RBP-based prognostic signature and identify critical hub RBPs in bladder cancer (BLCA). Firstly, a risk model based on differentially expressed RBP gens (DERBPs) between normal and tumor tissues was successfully established, which can predict the tumor stromal score and drug sensitivity. Then two another RBP risk models based on miRNA-correlated RBPs or lncRNA-correlated RBPs were also established, and RBMS3 was identified as the overlapping gene in the three models. Data from multiple bioinformatics databases revealed that …RBMS3 was an independent prognostic factor for overall survival (OS), and was associated with an immunosuppressive tumor microenvironment (TME) in BLCA. Further, Single-cell RNA-Seq (scRNA-Seq) data and the human protein altas (HPA) database showed that RBMS3 expression (both mRNA and protein) were up-regulated in BLCA tumor and tumor stromal cells. Finally, RBMS3 was shown to be associated with worse response to BLCA immunotherapy. Overall, RBMS3 is a key prognostic RBP with TME remodeling function and may serve as a target for BLCA immunotherapy. Show more

Keywords: RNA-binding protein, RBMS3, immunosuppressive tumor microenvironment, bladder cancer

DOI: 10.3233/CBM-230489

Citation: Cancer Biomarkers, vol. Pre-press, no. Pre-press, pp. 1-17, 2024