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Purpose: To explore the correlation between the initial recurrence site and survival after recurrence (PRS) in non-small cell lung cancer (NSCLC). Methods: We collected 588 stages I–III NSCLC patients with recurrence after radical resection in Yunnan Cancer Hospital from January 2013 to December 2018. We used Kaplan–Meier survival curves to compare PRS in patients with different site recurrences. The univariate and multivariate Cox proportional hazard models were used to analyze the impact of the initial recurrence site on PRS. Results: The recurrence site included the lung (n = 109), brain (n = 113), bone (n = 79), abdomen (n = 28), pleura (n = 24), lymph node (n = 81), and multisite (n = 154). In the total population, patients with multisite recurrence had substantially worse PRS (24.8 months, 95% confidence interval [CI]: 17.46–32.20) than that of patients without multiple sites recurrence (42.2 months, 95% CI 32.24–52.10) (P = 0.026). However, patients with lung recurrence had better RFS (63.1 months, 95% CI 51.13–74.00) than those who did not (31.0 months, 95% CI 25.10–36.96) (P < 0.001). In adenocarcinoma, patients with pleural recurrence had substantially worse PRS (21.3 months, 95% CI 15.07–27.46) than that of patients without pleural recurrence (46.9 months, 95% CI 35.07–58.80) (P = 0.031). Multivariate Cox proportional hazards regression analysis revealed that lung recurrence (HR 0.58, 95% CI 0.40–0.82; P = 0.003) was independent protective prognostic factor for PRS in the total population, while pleural recurrence (HR 2.18, 95% CI 1.14–4.17; P = 0.018) was independent adverse prognostic factors for PRS in adenocarcinoma patients. Conclusion: The initial recurrence site was associated with PRS in NSCLC patients. Identification of recurrence sites could guide the subsequent treatment. [ABSTRACT FROM AUTHOR]

Background: It is common for elderly patients to be underrepresented in clinical trials for cancer, which can result in a lack of efficacy data and unclear criteria to guide treatment decisions for clinical doctors. Therefore, one of the common challenges in oncology treatment is determining the extent to which patients aged 75 and older have benefited from postoperative chemotherapy.Purpose: The study aimed to explore the effect of adjuvant chemotherapy (AC) on 3-year recurrence-free survival (RFS) after curative resection in patients aged 75 years and older with stage II–III colorectal cancer (CRC).Methods: The retrospective cohort analysis was performed on patients with stage II–III CRC who received curative resection at three cancer centers in China between 2008 and 2017. Kaplan-Meier curves and Multivariable Cox regression models were used to analyze the impact of AC on RFS in patients. Finally, propensity-score matching was used to reduce selection bias and confounding factors in patients aged 75 years and older with stage II–III CRC.Results: A total of 2885 patients were included (1729 (59.9%) male; 1312 (61.5%) received AC). The pre-matching cohort was comprised of 151 patients aged 75 years and older (median age (IQR)77.00 (76.00, 79.00); 97 (64.2%) male, 51 (72.9%) received AC). Age (P=0.001), postoperative carcinoembryonic antigen (CEA)(P=0.02) level were associated with prognosis. But AC was not associated with 3-year RFS (HR, 1.27; 95% CI, 0.80– 2.0; log-rank P=0.37). After a predisposition 1: 1 match (with or without AC, n = 42), AC remains uncorrelated with 3-year RFS (HR, 1.39; 95% CI, 0.52– 3.70; log-rank P=0.66).Conclusion: Patients over the age of 75 with stage II–III CRC who receive AC or do not face the same risk of postoperative recurrence. As a result, patients with stage II–III postoperative adjuvant chemotherapy can make an informed decision regarding whether they want to undergo chemotherapy based on their age and reduce the unnecessary side effects of chemotherapy. [ABSTRACT FROM AUTHOR]

Objectives: To explore the usefulness of spleen radiomics features based on contrast-enhanced computed tomography (CECT) in predicting early and late recurrences of hepatocellular carcinoma (HCC) patients after curative resection. Methods: This retrospective study included 237 HCC patients who underwent CECT and curative resection between January 2006 to January 2016. Radiomic features were extracted from CECT images, and then the spleen radiomics signatures and the tumor radiomics signatures were built. Cox regression analysis was performed to identify the independent risk factors of early and late recurrences. Then, multiple models were built to predict the recurrence-free survival of HCC after resection, and the incremental value of the radiomics signature to the clinicopathologic model was assessed and validated. Kaplan–Meier survival analysis was used to assess the association of the models with RFS. Results: The spleen radiomics signature was independent risk factor of early recurrence of HCC. The mixed model that integrated microvascular invasion, tumor radiomics signature and spleen radiomics signature for the prediction of early recurrence achieved the highest C-index of 0.780 (95% CI: 0.728,0.831) in the primary cohort and 0.776 (95% CI: 0.716,0.836) in the validation cohort, and presented better predictive performance than clinicopathological model and combined model. In the analysis of late recurrence, the spleen radiomics signature was the only prognostic factor associated with late recurrence of HCC. Conclusions: The identified spleen radiomics signatures are prognostic factors of both early and late recurrences of HCC patients after surgery and improve the predictive performance of model for early recurrence. [ABSTRACT FROM AUTHOR]

Purpose: Based on a multi-centered and a large sample size, this study aims to analyze the relationship between preoperative and postoperative serum CEA and recurrence of rectal cancer without preoperative therapy. Methods: This retrospective cohort study enrolled stage I to III rectal cancer patients without preoperative therapy (N = 1,022) who received radical resection of rectal cancer from 2 hospitals in China. Based on the preoperative and postoperative serum carcinoembryonic antigen, the patients were subdivided into 3 groups ie, normal preoperative CEA (≤ 5.0 ng/mL, N = 627), elevated preoperative (> 5.0 ng/mL) but normalized postoperative CEA (normalized postoperative CEA, N = 255), as well as elevated preoperative and postoperative CEA (elevated postoperative CEA, N = 67). The generalized additive model was used to assess the relationship between carcinoembryonic antigen and the risk of recurrence. Further, the Cox regression model was used to evaluate the relationship between carcinoembryonic antigen and 3-year recurrence-free survival (RFS) after adjusting for potential confounders. Results: The 3-year RFS of patients with elevated postoperative CEA was 45.8% (95% CI, 35.2% − 59.5%), which was significantly lower compared to the other two groups of patients (normalized postoperative CEA: 75.9%, 95% CI, 70.8%-81.4%; and normal preoperative CEA: 84.9%, 95% CI, 82.2%-87.8%) (P < 0.001). Based on multivariable Cox model analysis, the elevated postoperative CEA was a prognostic factor for 3 years RFS (hazard ratio [HR], 3.08; 95% CI, 2.05– 4.66; P< 0.001). At the same time, normalized postoperative CEA was insignificantly correlated with 3-year RFS (HR, 1.38; 95% CI, 1.00– 1.92; P = 0.05) and was not an independent risk factor. Conclusion: We found that preoperative and postoperative serum CEA of rectal cancer patients were related to the 3-year recurrence-free survival rate. Moreover, the risk of recurrence in the normalized postoperative CEA group of patients was insignificantly different from that of the normalized preoperative CEA patients. Therefore, it is necessary to combine preoperative and postoperative CEA to predict the prognosis of patients with rectal cancer, rather than using it alone. [ABSTRACT FROM AUTHOR]

Purpose: To develop and further validate a deep learning signature-based nomogram from computed tomography (CT) images for prediction of the overall survival (OS) in resected non-small cell lung cancer (NSCLC) patients. Patients and Methods: A total of 1792 deep learning features were extracted from non-enhanced and venous-phase CT images for each NSCLC patient in training cohort (n=231). Then, a deep learning signature was built with the least absolute shrinkage and selection operator (LASSO) Cox regression model for OS estimation. At last, a nomogram was constructed with the signature and other independent clinical risk factors. The performance of nomogram was assessed by discrimination, calibration and clinical usefulness. In addition, in order to quantify the improvement in performance added by deep learning signature, the net reclassification improvement (NRI) was calculated. The results were validated in external validation cohort (n=77). Results: A deep learning signature with 9 selected features was significantly associated with OS in both training cohort (hazard ratio [HR]=5.455, 95% CI: 3.393– 8.769, P< 0.001) and external validation cohort (HR=3.029, 95% CI: 1.673– 5.485, P=0.004). The nomogram combining deep learning signature with clinical risk factors of TNM stage, lymphatic vessel invasion and differentiation grade showed favorable discriminative ability with C-index of 0.800 as well as a good calibration, which was validated in external validation cohort (C-index=0.723). Additional value of deep learning signature to the nomogram was statistically significant (NRI=0.093, P=0.027 for training cohort; NRI=0.106, P=0.040 for validation cohort). Decision curve analysis confirmed the clinical usefulness of this nomogram in predicting OS. Conclusion: The deep learning signature-based nomogram is a robust tool for prognostic prediction in resected NSCLC patients. [ABSTRACT FROM AUTHOR]

Computerized image analysis for whole-slide images has been shown to improve efficiency, accuracy, and consistency in histopathology evaluations. We aimed to assess whether immunohistochemistry (IHC) image quantitative features can reflect the immune status and provide prognostic information for colorectal cancer patients. A fully automated pipeline was designed to extract histogram features from IHC digital images in a training set (N = 243). A Hist-Immune signature was generated with selected features using the LASSO Cox model. The results were validated using internal (N = 147) and external (N = 76) validation sets. The five-feature-based Hist-Immune signature was significantly associated with overall survival in training (HR 2.72, 95% CI 1.68–4.41, P <.001), internal (2.86, 1.28–6.39, 0.010), and external (2.30, 1.02–6.16, 0.044) validation sets. The full model constructed by integrating the Hist-Immune signature and clinicopathological factors had good discrimination ability (C-index 0.727, 95% CI 0.678–0.776), confirmed using internal (0.703, 0.621–0.784) and external (0.756, 0.653–0.859) validation sets. Our findings indicate that the Hist-Immune signature constructed based on the quantitative features could reflect the immune status of patients with colorectal cancer, which might advocate change in risk stratification and consequent precision medicine. [ABSTRACT FROM AUTHOR]

Background: We proposed an artificial intelligence-based immune index, Deep-immune score, quantifying the infiltration of immune cells interacting with the tumor stroma in hematoxylin and eosin-stained whole-slide images of colorectal cancer.Methods: A total of 1010 colorectal cancer patients from three centers were enrolled in this retrospective study, divided into a primary (N = 544) and a validation cohort (N = 466). We proposed the Deep-immune score, which reflected both tumor stroma proportion and the infiltration of immune cells in the stroma region. We further analyzed the correlation between the score and CD3+ T cells density in the stroma region using immunohistochemistry-stained whole-slide images. Survival analysis was performed using the Cox proportional hazard model, and the endpoint of the event was the overall survival.Result: Patients were classified into 4-level score groups (score 1-4). A high Deep-immune score was associated with a high level of CD3+ T cells infiltration in the stroma region. In the primary cohort, survival analysis showed a significant difference in 5-year survival rates between score 4 and score 1 groups: 87.4% vs. 58.2% (Hazard ratio for score 4 vs. score 1 0.27, 95% confidence interval 0.15-0.48, P < 0.001). Similar trends were observed in the validation cohort (89.8% vs. 67.0%; 0.31, 0.15-0.62, < 0.001). Stratified analysis showed that the Deep-immune score could distinguish high-risk and low-risk patients in stage II colorectal cancer (P = 0.018).Conclusion: The proposed Deep-immune score quantified by artificial intelligence can reflect the immune status of patients with colorectal cancer and is associate with favorable survival. This digital pathology-based finding might advocate change in risk stratification and consequent precision medicine. [ABSTRACT FROM AUTHOR]

• Proposed model improved prognostic prediction in locally advanced rectal cancer. • Non-invasive imaging signatures were independent prognostic factors. • An individualized tool linking chemotherapy decisions and benefits was provided. • More patients benefiting from adjuvant chemotherapy would be identified. • The balance between benefit and toxicity should be considered in specific subgroups. Adjuvant therapy is recommended to minimize the risk of distant metastasis (DM) and local recurrence (LR) in patients with locally advanced rectal cancer (LARC). However, its role is controversial. We aimed to develop a pretreatment MRI-based deep learning model to predict LR, DM, and overall survival (OS) over 5 years after surgery and to identify patients benefitting from adjuvant chemotherapy (AC). The multi-survival tasks network (MuST) model was developed in a primary cohort (n = 308) and validated using two external cohorts (n = 247, 245). An AC decision tree integrating the MuST-DM score, perineural invasion (PNI), and preoperative carbohydrate antigen 19–9 (CA19-9) was constructed to assess chemotherapy benefits and aid personalized treatment of patients. We also quantified the prognostic improvement of the decision tree. The MuST network demonstrated high prognostic accuracy in the primary and two external cohorts for the prediction of three different survival tasks. Within the stratified analysis and decision tree, patients with CA19-9 levels > 37 U/mL and high MuST-DM scores exhibited favorable chemotherapy efficacy. Similar results were observed in PNI-positive patients with low MuST-DM scores. PNI-negative patients with low MuST-DM scores exhibited poor chemotherapy efficacy. Based on the decision tree, 14 additional patients benefiting from AC and 391 patients who received over-treatment were identified in this retrospective study. The MuST model accurately and non-invasively predicted OS, DM, and LR. A specific and direct tool linking chemotherapy decisions and benefit quantification has also been provided. [ABSTRACT FROM AUTHOR]