SUV&lt;sub&gt;max&lt;/sub&gt; and Tumour Location in PET-CT Predict Oncogene Status in Lung Cancer

Putora, Paul Martin; Szentesi, Kinga; Glatzer, Markus; Rodriguez, Regulo; Müller, Joachim; Baty, Florent; Früh, Martin

doi:10.1159/000450622

Cited by 6 publications

(6 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Besides, we can also find some other features, including higher frequencies of distant nodal metastasis and lymphangitic carcinomatosis, no air bronchogram [36,38] . On the other hand, EGFR mutations are prone to occur in the peripheral [39] . Both can be found in the peripheral-type squamous cell carcinoma (P-SqCC) and pneumonic-type lung adenocarcinoma (P-ADC) [8] .…”

Section: Bronchus/central and Clinicopathologic Featuresmentioning

confidence: 90%

“…[ 36 , 38 ] On the other hand, EGFR mutations are prone to occur in the peripheral. [ 39 ] Both can be found in the peripheral-type squamous cell carcinoma (P-SqCC) and pneumonic-type lung adenocarcinoma (P-ADC). [ 8 ] If patients in specific CT features (pleural tag and air bronchogram) with first tyrosine kinase inhibitors (TKIs) failure in peripheral location, they are more likely to have a recurrence in T790M status when rebiopsy.…”

Section: Bronchus/central and Clinicopathologic Featuresmentioning

confidence: 99%

“…[ 40 ] On the other hand, EGFR is prone to occur in the peripheral. [ 39 ] Both can be found in the P-SqCC and P-ADC. 2-Deoxy-2-[(18)F]fluoro-D-glucose positron emission tomography (FDG-PET) may be a good tool to explore the relationship between tumor location and programmed death-ligand 1 (PD-L1) expression.…”

Section: Bronchus/central and Clinicopathologic Featuresmentioning

confidence: 99%

See 2 more Smart Citations

Primary tumor location in lung cancer: the evaluation and administration

Xie

Tang

et al. 2021

Chinese Medical Journal

View full text Add to dashboard Cite

Lung cancer continues to be the leading cause of cancer-related death in the world, which is classically subgrouped into two major histological types: Non-small cell lung cancer (NSCLC) (85% of patients) and small-cell lung cancer (SCLC) (15%). Tumor location has been reported to be associated with the prognosis of various solid tumors. Several types of cancer often occur in a specific region and are more prone to spread to predilection locations, including colorectal cancer, prostate cancer, gastric cancer, ovarian cancer, cervical cancer, bladder cancer, lung tumor, and so on. Besides, tumor location is also considered as a risk factor for lung neoplasm with chronic obstructive pulmonary disease/emphysema. Additionally, the primary lung cancer location is associated with specific lymph node metastasis. And the recent analysis has shown that the primary location may affect metastasis pattern in metastatic NSCLC based on a large population. Numerous studies have enrolled the “location” factor in the risk model. Anatomy location and lobe-specific location are both important in prognosis. Therefore, it is important for us to clarify the characteristics about tumor location according to various definitions. However, the inconsistent definitions about tumor location among different articles are controversial. It is also a significant guidance in multimode therapy in the present time. In this review, we mainly aim to provide a new insight about tumor location, including anatomy, clinicopathology, and prognosis in patients with lung neoplasm.

show abstract

Section: Bronchus/central and Clinicopathologic Featuresmentioning

confidence: 90%

Section: Bronchus/central and Clinicopathologic Featuresmentioning

confidence: 99%

Section: Bronchus/central and Clinicopathologic Featuresmentioning

confidence: 99%

See 1 more Smart Citation

Primary tumor location in lung cancer: the evaluation and administration

Xie

Tang

et al. 2021

Chinese Medical Journal

View full text Add to dashboard Cite

show abstract

“…18 Fluorine-fluoro-D-glucose positron emission tomography/computed tomography (( 18 F)-FDG PET/ CT), as an emerging imaging technique, can not only depict anatomical structure, but also reflect the biological characteristics of tumors. However, the potential of PET/CT to predict mutation status of EGFR remains controversial (17)(18)(19)(20). Radiomics, which has emerged as a promising technique to identify genetic phenotypes in several types of tumors, is inspired by the concept of extracting underlying imaging features from medical images for quantitative imaging analysis (21).…”

mentioning

confidence: 99%

18F-fluorodeoxyglucose positron emission tomography/computed tomography-based radiomic features for prediction of epidermal growth factor receptor mutation status and prognosis in patients with lung adenocarcinoma

Yang

Zhou

et al. 2020

Transl Lung Cancer Res

View full text Add to dashboard Cite

Background: To investigate whether radiomic features from ( 18 F)-fluorodeoxyglucose positron emission tomography/computed tomography [( 18 F)-FDG PET/CT] can predict epidermal growth factor receptor (EGFR) mutation status and prognosis in patients with lung adenocarcinoma.Methods: One hundred and seventy-four consecutive patients with lung adenocarcinoma underwent ( 18 F)-FDG PET/CT and EGFR gene testing were retrospectively analyzed. Radiomic features combined with clinicopathological factors to construct a random forest (RF) model to identify EGFR mutation status. The mutant/wild-type model was trained on a training group (n=139) and validated in an independent validation group (n=35). The second RF classifier predicting the 19/21 mutation site was also built and evaluated in an EGFR mutation subset (training group, n=80; validation group, n=25). Radiomic score and 5 clinicopathological factors were integrated into a multivariate Cox proportional hazard (CPH) model for predicting overall survival (OS). AUC (the area under the receiver characteristic curve) and C-index were calculated to evaluate the model's performance. Results: Of 174 patients, 109 (62.6%) harbored EGFR mutations, 21L858R was the most common mutation type [55.9% (61/109)]. The mutant/wild-type model was identified in the training (AUC, 0.77) and validation (AUC, 0.71) groups. The 19/21 mutation site model had an AUC of 0.82 and 0.73 in the training and validation groups, respectively. The C-index of the CPH model was 0.757. The survival time between targeted therapy and chemotherapy for patients with EGFR mutations was significantly different (P=0.03). Conclusions: Radiomic features based on ( 18 F)-FDG PET/CT combined with clinicopathological factors could reflect genetic differences and predict EGFR mutation type and prognosis.

show abstract

“…For instance, exposure to tobacco carcinogens leads to centrally located NSCLC tumors with higher accumulation of alterations in suppressor genes [ 78 ]. On the other hand, oncogenic alterations such as EGFR-activating mutations are prone to occur in the peripheral location that is more common to LUAD [ 79 , 80 ]. Our results indicate that central locations of NSCLC (high neighborhood of bronchi) decrease the probability to detect RET alterations (Fig.…”

Section: Discussionmentioning

confidence: 99%

Deep learning-based tumor microenvironment segmentation is predictive of tumor mutations and patient survival in non-small-cell lung cancer

et al. 2022

View full text Add to dashboard Cite

Background Despite the fact that tumor microenvironment (TME) and gene mutations are the main determinants of progression of the deadliest cancer in the world – lung cancer, their interrelations are not well understood. Digital pathology data provides a unique insight into the spatial composition of the TME. Various spatial metrics and machine learning approaches were proposed for prediction of either patient survival or gene mutations from this data. Still, these approaches are limited in the scope of analyzed features and in their explainability, and as such fail to transfer to clinical practice. Methods Here, we generated 23,199 image patches from 26 hematoxylin-and-eosin (H&E)-stained lung cancer tissue sections and annotated them into 9 different tissue classes. Using this dataset, we trained a deep neural network ARA-CNN. Next, we applied the trained network to segment 467 lung cancer H&E images from The Cancer Genome Atlas (TCGA) database. We used the segmented images to compute human-interpretable features reflecting the heterogeneous composition of the TME, and successfully utilized them to predict patient survival and cancer gene mutations. Results We achieved per-class AUC ranging from 0.72 to 0.99 for classifying tissue types in lung cancer with ARA-CNN. Machine learning models trained on the proposed human-interpretable features achieved a c-index of 0.723 in the task of survival prediction and AUC up to 73.5% for PDGFRB in the task of mutation classification. Conclusions We presented a framework that accurately predicted survival and gene mutations in lung adenocarcinoma patients based on human-interpretable features extracted from H&E slides. Our approach can provide important insights for designing novel cancer treatments, by linking the spatial structure of the TME in lung adenocarcinoma to gene mutations and patient survival. It can also expand our understanding of the effects that the TME has on tumor evolutionary processes. Our approach can be generalized to different cancer types to inform precision medicine strategies.

show abstract

SUV<sub>max</sub> and Tumour Location in PET-CT Predict Oncogene Status in Lung Cancer

Cited by 6 publications

References 32 publications

Primary tumor location in lung cancer: the evaluation and administration

Primary tumor location in lung cancer: the evaluation and administration

18F-fluorodeoxyglucose positron emission tomography/computed tomography-based radiomic features for prediction of epidermal growth factor receptor mutation status and prognosis in patients with lung adenocarcinoma

Deep learning-based tumor microenvironment segmentation is predictive of tumor mutations and patient survival in non-small-cell lung cancer

Contact Info

Product

Resources

About