PET/CT-based deep learning grading signature to optimize surgical decisions for clinical stage I invasive lung adenocarcinoma and biologic basis under its prediction: a multicenter study.

PURPOSE: No consensus on a grading system for invasive lung adenocarcinoma had been built over a long period of time. Until October 2020, a novel grading system was proposed to quantify the whole landscape of histologic subtypes and proportions of pulmonary adenocarcinomas. This study aims to develop a deep learning grading signature (DLGS) based on positron emission tomography/computed tomography (PET/CT) to personalize surgical treatments for clinical stage I invasive lung adenocarcinoma and explore the biologic basis under its prediction. METHODS: A total of 2638 patients with clinical stage I invasive lung adenocarcinoma from 4 medical centers were retrospectively included to construct and validate the DLGS. The predictive performance of the DLGS was evaluated by the area under the receiver operating characteristic curve (AUC), its potential to optimize surgical treatments was investigated via survival analyses in risk groups defined by the DLGS, and its biological basis was explored by comparing histologic patterns, genotypic alternations, genetic pathways, and infiltration of immune cells in microenvironments between risk groups. RESULTS: The DLGS to predict grade 3 achieved AUCs of 0.862, 0.844, and 0.851 in the validation set (n = 497), external cohort (n = 382), and prospective cohort (n = 600), respectively, which were significantly better than 0.814, 0.810, and 0.806 of the PET model, 0.813, 0.795, and 0.824 of the CT model, and 0.762, 0.734, and 0.751 of the clinical model. Additionally, for DLGS-defined high-risk population, lobectomy yielded an improved prognosis compared to sublobectomy p = 0.085 for overall survival [OS] and p = 0.038 for recurrence-free survival [RFS]) and systematic nodal dissection conferred a superior prognosis to limited nodal dissection (p = 0.001 for OS and p = 0.041 for RFS). CONCLUSION: The DLGS harbors the potential to predict the histologic grade and personalize the surgical treatments for clinical stage I invasive lung adenocarcinoma. Its applicability to other territories should be further validated by a larger international study.

[18F]FDG PET-CT radiomics signature to predict pathological complete response to neoadjuvant chemoimmunotherapy in non-small cell lung cancer: a multicenter study.

OBJECTIVES: This study aims to develop and validate a radiomics model based on 18F-fluorodeoxyglucose positron emission tomography-computed tomography ([18F]FDG PET-CT) images to predict pathological complete response (pCR) to neoadjuvant chemoimmunotherapy in non-small cell lung cancer (NSCLC). MATERIALS AND METHODS: One hundred eighty-five patients receiving neoadjuvant chemoimmunotherapy for NSCLC at 5 centers from January 2019 to December 2022 were included and divided into a training cohort and a validation cohort. Radiomics models were constructed via the least absolute shrinkage and selection operator (LASSO) method. The performances of models were evaluated by the area under the receiver operating characteristic curve (AUC). In addition, genetic analyses were conducted to reveal the underlying biological basis of the radiomics score. RESULTS: After the LASSO process, 9 PET-CT radiomics features were selected for pCR prediction. In the validation cohort, the ability of PET-CT radiomics model to predict pCR was shown to have an AUC of 0.818 (95% confidence interval [CI], 0.711, 0.925), which was better than the PET radiomics model (0.728 [95% CI, 0.610, 0.846]), CT radiomics model (0.732 [95% CI, 0.607, 0.857]), and maximum standard uptake value (0.603 [95% CI, 0.473, 0.733]) (p < 0.05). Moreover, a high radiomics score was related to the upregulation of pathways suppressing tumor proliferation and the infiltration of antitumor immune cell. CONCLUSION: The proposed PET-CT radiomics model was capable of predicting pCR to neoadjuvant chemoimmunotherapy in NSCLC patients. CLINICAL RELEVANCE STATEMENT: This study indicated that the generated 18F-fluorodeoxyglucose positron emission tomography-computed tomography radiomics model could predict pathological complete response to neoadjuvant chemoimmunotherapy, implying the potential of our radiomics model to personalize the neoadjuvant chemoimmunotherapy in lung cancer patients. KEY POINTS: • Recognizing patients potentially benefiting neoadjuvant chemoimmunotherapy is critical for individualized therapy of lung cancer. • [18F]FDG PET-CT radiomics could predict pathological complete response to neoadjuvant immunotherapy in non-small cell lung cancer. • [18F]FDG PET-CT radiomics model could personalize neoadjuvant chemoimmunotherapy in lung cancer patients.

Volume-based predictive biomarkers of sequential FDG-PET/CT for sunitinib in cancer of unknown primary: identification of the best benefited patients.

PURPOSE: To test the performance of sequential 18F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) in predicting survival after sunitinib therapies in patients with cancer of unknown primary (CUP). METHODS: CUP patients were enrolled for sequential PET/CT scanning for sunitinib and a control group. Univariate and multivariate analysis were applied to test the efficacy of sunitinib therapy in CUP patients. Next, sequential analyses involving PET/CT parameters were performed to identify and validate sensitive PET/CT biomarkers for sunitinib therapy. Finally, time-dependent receiver operating characteristic (TDROC) analyses were performed to compare the predictive accuracy. RESULTS: Multivariate analysis proved that sunitinib group had significantly improved survival (p < 0.01) as compared to control group. After cycle 2 of therapy, multivariate analysis identified volume-based PET/CT parameters as sensitive biomarkers for sunitinib (p < 0.01). TDROC curves demonstrated whole-body total lesion glycolysis reduction (Δ WTLG) and follow-up WTLG to have good accuracy for efficacy prediction. This evidence was validated after cycle 4 of therapy with the same method. CONCLUSION: Sunitinib therapy proved effective in treatment of CUP. PET/CT volume-based parameters may help predict outcome of sunitinib therapy, in which Δ WTLG and follow-up WTLG seem to be sensitive biomarkers for sunitinib efficacy. Patients with greater reduction and lower WTLG at follow-up seem to have better survival outcome.

Preoperative Three-Dimensional Morphological Tumor Features Predict Microvascular Invasion in Hepatocellular Carcinoma.

RATIONALE AND OBJECTIVES: The study was designed to evaluate microvascular invasion (MVI) using three-dimensional (3D) morphological indicators prior to surgery. MATERIALS AND METHODS: This retrospective study included 156 patients with hepatocellular carcinoma (HCC) at our hospital from 2017 to 2018. Through thin-layer CT scanning and 3D reconstruction, the tumor surface inclination angles can be quantitatively analyzed to determine the surface irregularity rate (SIR), which serves as a comprehensive assessment method for tumor irregularity based on preoperative 3D morphological evaluation. Univariate and multivariate logistic regression analyses were employed to investigate the correlation with MVI. RESULTS: The SIR was related to MVI (OR: 10.667, P < 0.001). Multivariate logistic regression analysis showed that the SIR was an independent risk factor for MVI. The area under the receiver operating characteristic curve (ROC) of prediction model composed of the morphological indicator SIR was 0.831 (95% confidence interval: 0.759-0.895). CONCLUSION: The preoperative 3D morphological indicator SIR of a tumor is an accurate predictor of MVI, providing a valuable tool in clinical decision-making.

Glycolytic neutrophils accrued in the spleen compromise anti-tumour T cell immunity in breast cancer.

The coordination of immunity across organs is fundamental to cancer development and progression. It is well known that the hostile metabolic microenvironment in the tumour is a major obstacle to effective anti-tumour immunity. However, whether metabolic alterations in secondary lymphoid tissues beyond the tumour can affect anti-tumour immunity remains elusive. Using positron-emission tomography-computed tomography, we show that the spleens of humans and mice with breast cancer are metabolically reprogrammed to a glycolytic state. Such an increase in glucose consumption in the spleen primarily occurs in neutrophils generated by extramedullary haematopoiesis and recruitment from the bone marrow. These neutrophils in the white pulp create a glucose-deprived microenvironment, which, in turn, induces T cell anergy by impairing pyruvate kinase M2 and its action on STAT5, thus compromising their anti-tumour activities. Furthermore, CCL9 chemokine produced by splenic stromal cells is central to splenic neutrophil accumulation, and blockade of the CCR1 receptor favours tumour eradication. Thus, neutrophils metabolically influence the spleen microenvironment and control anti-tumour T cell responses.

Modification of cerebrovascular morphologies during different stages of life.

To expand previous understanding of age-related vascular changes, we examined the association between aging and characteristics of cerebral arteries among 1133 participants aged 35 to 75 years recruited from Shanghai, China. Characteristics of the cerebral vessels including arterial branch density, mean radius, and mean tortuosity were quantified using MR angiography. The radius, tortuosity, and length of the basilar artery (BA) and the M1 segment of middle cerebral artery (MCA) were also accessed. Linear regression model was used to examine the association between age and vasculature features. The sample was divided into four subgroups by age and the association was analyzed in each subgroup. Age was found to be a significant predictor for cerebrovascular modifications after adjusting for vascular risk factors. Further analysis in subgroup revealed that the associations were due to the predominate effect of the vascular modifications happened during the younger years (35-54 years). The radius of either BA or MCA was associated with aging only in subjects aged 45-54 years. In conclusion, rapid alterations in all three morphological features assessed have been noticed to be associated with aging in the 45-54 subgroup, suggesting the potential importance of the 5th decade for early preservation method for vascular aging.

Role of preoperative prediction of microvascular invasion in hepatocellular carcinoma based on the texture of FDG PET image: A comparison of quantitative metabolic parameters and MRI.

Objective: To investigate the role of prediction microvascular invasion (mVI) in hepatocellular carcinoma (HCC) by 18F-FDG PET image texture analysis and hybrid criteria combining PET/CT and multi-parameter MRI. Materials and methods: Ninety-seven patients with HCC who received the examinations of MRI and 18F-FDG PET/CT were retrospectively included in this study and were randomized into training and testing cohorts. The lesion image texture features of 18F-FDG PET were extracted using MaZda software. The optimal predictive texture features of mVI were selected, and the classification procedure was conducted. The predictive performance of mVI by radiomics classier in training and testing cohorts was respectively recorded. Next, the hybrid model was developed by integrating the 18F-FDG PET image texture, metabolic parameters, and MRI parameters to predict mVI through logistic regression. Furthermore, the diagnostic performance of each time was recorded. Results: The 18F-FDG PET image radiomics classier showed good predicted performance in both training and testing cohorts to discriminate HCC with/without mVI, with an AUC of 0.917 (95% CI: 0.824-0.970) and 0.771 (95% CI: 0.578, 0.905). The hybrid model, which combines radiomics classier, SUVmax, ADC, hypovascular arterial phase enhancement pattern on contrast-enhanced MRI, and non-smooth tumor margin, also yielded better predictive performance with an AUC of 0.996 (95% CI: 0.939, 1.000) and 0.953 (95% CI: 0.883, 1.000). The differences in AUCs between radiomics classier and hybrid classier were significant in both training and testing cohorts (DeLong test, both p < 0.05). Conclusion: The radiomics classier based on 18F-FDG PET image texture and the hybrid classier incorporating 18F-FDG PET/CT and MRI yielded good predictive performance, which might provide a precise prediction of HCC mVI preoperatively.

Immune checkpoint blocking impact and nomogram prediction of COVID-19 inactivated vaccine seroconversion in patients with cancer: a propensity-score matched analysis.

BACKGROUND: Patients with cancer on active immune checkpoint inhibitors therapy were recommended to seek prophylaxis from COVID-19 by vaccination. There have been few reports to date to discuss the impact of progression cell death-1 blockers (PD-1B) on immune or vaccine-related outcomes, and what risk factors that contribute to the serological status remains to be elucidated. The study aims to find the impact of PD-1B on vaccination outcome and investigate other potential risk factors associated with the risk of seroconversion failure. METHODS: Patients with active cancer treatment were retrospectively enrolled to investigate the interaction effects between PD-1B and vaccination. Through propensity score matching of demographic and clinical features, the seroconversion rates and immune/vaccination-related adverse events (irAE and vrAE) were compared in a head-to-head manner. Then, a nomogram predicting the failure risk was developed with variables significant in multivariate regression analysis and validated in an independent cohort. RESULTS: Patients (n=454) receiving either PD-1B or COVID-19 vaccination, or both, were matched into three cohorts (vac+/PD-1B+, vac+/PD-1B-, and vac-/PD-1B+, respectively), with a non-concer control group of 206 participants. 68.1% (94/138), 71.3% (117/164), and 80.5% (166/206) were seropositive in vac+/PD-1B+cohort, vac+/PD-1B- cohort, and non-cancer control group, respectively. None of irAE or vrAE was observed to be escalated in PD-1B treatment except for low-grade rash.The vaccinated patients with cancer had a significantly lower rate of seroconversion rates than healthy control. A nomogram was thus built that encompassed age, pathology, and chemotherapy status to predict the seroconversion failure risk, which was validated in an independent cancer cohort of 196 patients. CONCLUSION: Although patients with cancer had a generally decreased rate of seroconversion as compared with the healthy population, the COVID-19 vaccine was generally well tolerated, and seroconversion was not affected in patients receiving PD-1B. A nomogram predicting failure risk was developed, including age, chemotherapy status, pathology types, and rheumatic comorbidity.

Diagnostic Value of Volume-Based Fluorine-18-Fluorodeoxyglucose PET/CT Parameters for Characterizing Thyroid Incidentaloma.

Objective: To assess clinical value of fluorine-18-fluorodeoxyglucose positron emission tomography/computed tomography (PET/CT) for differentiation of malignant from benign focal thyroid incidentaloma. Materials and Methods: This retrospective study included 99 patients with focal thyroid incidentaloma of 5216 non-thyroid cancer patients that had undergone PET/CT. PET/CT semi-quantitative parameters, volume-based functional parameters, metabolic tumor volume (MTV), and total lesion glycolysis (TLG) of thyroid incidentaloma were assessed. Receiver-operating characteristic (ROC) analysis was conducted and areas under the curve (AUC) were compared by Hanley and McNeil test to evaluate usefulness of maximum standardized uptake value (SUVmax), MTV and TLG, as markers for differentiating malignant from benign thyroid incidentalomas. Results: Of 99 thyroid incidentalomas, 64 (64.6%) were malignant and 35 (35.4%) were benign. Malignant thyroid incidentalomas were larger (1.8 cm vs. 1.3 cm, p = 0.006), and had higher SUVmax (11.3 vs. 4.8, p < 0.001), MTV (all p < 0.001) and TLG (all p < 0.001) than benign. TLG 4.0 had the highest performance for differentiation of malignant from benign thyroid incidentaloma in all semi-quantitative parameters with AUC 0.895 by ROC curve analysis. AUC (TLG 4.0) was significantly larger than AUC (SUVmean), AUC (MTV 2.5), AUC (MTV 3.0), AUC (MTV 3.5), AUC (TLG 2.5), and AUC (TLG 3.0), respectively (all, p < 0.05). There was no statistical difference between AUC (TLG 4.0) and AUC (SUVmax) (p > 0.05). A threshold TLG 4.0 of 2.475 had 81.3% sensitivity and 94.3% specificity for identifying malignant thyroid incidentalomas. Conclusion: Volume-based PET/CT parameters could potentially have clinical value in differential diagnosis of thyroid incidentaloma along with SUVmax.