Updates on lung adenocarcinoma: invasive size, grading and STAS.

Advancements in the classification of lung adenocarcinoma have resulted in significant changes in pathological reporting. The eighth edition of the tumour-node-metastasis (TNM) staging guidelines calls for the use of invasive size in staging in place of total tumour size. This shift improves prognostic stratification and requires a more nuanced approach to tumour measurements in challenging situations. Similarly, the adoption of new grading criteria based on the predominant and highest-grade pattern proposed by the International Association for the Study of Lung Cancer (IASLC) shows improved prognostication, and therefore clinical utility, relative to previous grading systems. Spread through airspaces (STAS) is a form of tumour invasion involving tumour cells spreading through the airspaces, which has been highly researched in recent years. This review discusses updates in pathological T staging, adenocarcinoma grading and STAS and illustrates the utility and limitations of current concepts in lung adenocarcinoma.

Diagnostic efficacy of contrast-enhanced gastric ultrasonography in staging gastric cancer: a meta-analysis.

BACKGROUND: As comprehensive surgical management for gastric cancer becomes increasingly specialized and standardized, the precise differentiation between ≤T1 and ≥T2 gastric cancer before endoscopic intervention holds paramount clinical significance. OBJECTIVE: To evaluate the diagnostic efficacy of contrast-enhanced gastric ultrasonography in differentiating ≤T1 and ≥T2 gastric cancer. METHODS: PubMed, Web of Science, and Medline were searched to collect studies published from January 1, 2000 to March 16, 2023 on the efficacy of either double contrast-enhanced gastric ultrasonography (D-CEGUS) or oral contrast-enhanced gastric ultrasonography (O-CEGUS) in determining T-stage in gastric cancer. The articles were selected according to specified inclusion and exclusion criteria, and the quality of the included literature was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 scale. Meta-analysis was performed using Stata 12 software with data from the 2 × 2 crosslinked tables in the included literature. RESULTS: In total, 11 papers with 1124 patients were included in the O-CEGUS analysis, which revealed a combined sensitivity of 0.822 (95% confidence interval [CI] = 0.753-0.875), combined specificity of 0.964 (95% CI = 0.925-0.983), and area under the summary receiver operating characteristic (sROC) curve (AUC) of 0.92 (95% CI = 0.89-0.94). In addition, five studies involving 536 patients were included in the D-CEGUS analysis, which gave a combined sensitivity of 0.733 (95% CI = 0.550-0.860), combined specificity of 0.982 (95% CI = 0.936-0.995), and AUC of 0.93 (95% CI = 0.91-0.95). According to the I2 and P values ​​of the forest plot, there was obvious heterogeneity in the combined specificities of the included papers. Therefore, the two studies with the lowest specificities were excluded from the O-CEGUS and D-CEGUS analyses, which eliminated the heterogeneity among the remaining literature. Consequently, the combined sensitivity and specificity of the remaining studies were 0.794 (95% CI = 0.710-0.859) and 0.976 (95% CI = 0.962-0.985), respectively, for the O-CEDUS studies and 0.765 (95% CI = 0.543-0.899) and 0.986 (95% CI = 0.967-0.994), respectively, for the D-CEGUS studies. The AUCs were 0.98 and 0.99 for O-CEGUS and D-CEGUS studies, respectively. CONCLUSION: Both O-CEGUS and D-CEGUS can differentiate ≤T1 gastric cancer from ≥T2 gastric cancer, thus assisting the formulation of clinical treatment strategies for patients with very early gastric cancer. Given its simplicity and cost-effectiveness, O-CEGUS is often favored as a staging method for gastric cancer prior to endoscopic intervention.

Deep Learning-Based Multiparametric MRI Model for Preoperative T-Stage in Rectal Cancer.

BACKGROUND: Conventional MRI staging can be challenging in the preoperative assessment of rectal cancer. Deep learning methods based on MRI have shown promise in cancer diagnosis and prognostication. However, the value of deep learning in rectal cancer T-staging is unclear. PURPOSE: To develop a deep learning model based on preoperative multiparametric MRI for evaluation of rectal cancer and to investigate its potential to improve T-staging accuracy. STUDY TYPE: Retrospective. POPULATION: After cross-validation, 260 patients (123 with T-stage T1-2 and 134 with T-stage T3-4) with histopathologically confirmed rectal cancer were randomly divided to the training (N = 208) and test sets (N = 52). FIELD STRENGTH/SEQUENCE: 3.0 T/Dynamic contrast enhanced (DCE), T2-weighted imaging (T2W), and diffusion-weighted imaging (DWI). ASSESSMENT: The deep learning (DL) model of multiparametric (DCE, T2W, and DWI) convolutional neural network were constructed for evaluating preoperative diagnosis. The pathological findings served as the reference standard for T-stage. For comparison, the single parameter DL-model, a logistic regression model composed of clinical features and subjective assessment of radiologists were used. STATISTICAL TESTS: The receiver operating characteristic curve (ROC) was used to evaluate the models, the Fleiss' kappa for the intercorrelation coefficients, and DeLong test for compare the diagnostic performance of ROCs. P-values less than 0.05 were considered statistically significant. RESULTS: The Area Under Curve (AUC) of the multiparametric DL-model was 0.854, which was significantly higher than the radiologist's assessment (AUC = 0.678), clinical model (AUC = 0.747), and the single parameter DL-models including T2W-model (AUC = 0.735), DWI-model (AUC = 0.759), and DCE-model (AUC = 0.789). DATA CONCLUSION: In the evaluation of rectal cancer patients, the proposed multiparametric DL-model outperformed the radiologist's assessment, the clinical model as well as the single parameter models. The multiparametric DL-model has the potential to assist clinicians by providing more reliable and precise preoperative T staging diagnosis. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

Prognostic and Clinical Implications of UNC13C expression in Hepatocellular Carcinoma Patients.

Aberrant expression of UNC13C (Unc-13 Homolog C) has been observed during the progression of oral squamous cell carcinoma. However, the expression pattern and clinical relevance of UNC13C in Hepatocellular carcinoma (HCC) remain to be elucidated. The purpose of this study is to examine UNC13C expression in HCC and explore its role in clinicopathological factor or prognosis in HCC. Two hundred and sixty-five patients diagnosed with HCC were included in the present study. The expression of UNC13C in HCC tissues was analyzed by immunohistochemistry analysis. The relationship between UNC13C protein and clinicopathological characteristics in HCC was investigated. Moreover, the high expression of UNC13C was significantly correlated with T stage, AJCC stage and overall survival rates. Cox regression analysis identified UNC13C as an independent prognostic indicator for HCC patients. UNC13C might be a prognostic biomarker and therapeutic target in HCC. Further studies with larger sample sets are needed to understand the clinical implications of UNC13C in hepatocellular carcinoma.

Distant metastasis in medullary thyroid carcinoma: Clinical outcomes and implications of T stage.

BACKGROUND: The eighth edition of the American Joint Committee on Cancer tumour, node, and metastasis staging system did not take T stage into consideration when evaluating Stage IV C medullary thyroid carcinoma (MTC) patients. The aim of this study is to investigate the clinical outcomes and implications of T stage in this population. METHODS: Eligible patients from the Surveillance, Epidemiology, and End Results database and the Department of Thyroid Surgery in West China Hospital of Sichuan University and who were diagnosed with Stage IV C MTC were included in this study. The overall survival (OS), the cancer-specific survival (CSS), and the precise cause of MTC-induced death were analysed. The potential risk factors, including the T stage, in the OS and CSS were evaluated by univariate and multivariate Cox regression models. RESULTS: This retrospective study enroled 204 Stage IV C MTC patients. The 5- and 10-year OS rates were 31.8% and 17.1%, respectively, and the 5- and 10-year CSS rates were 40.4% and 22.5%, respectively. More importantly, the rates of MTC-induced death between primary or distant metastatic lesions in Stage IV C MTC patients were comparable in our institution. Additionally, the univariate and multivariate analyses demonstrated that the presence of an advanced T stage was an independent prognostic factor for both the OS (T4 vs. T1-T3, hazard ratio [HR]: 1.714, 95% confidence interval [CI]: 1.175-2.500, p = .005) and the CSS (T4 vs. T1-T3, HR: 1.848, 95% CI: 1.229-2.780, p = .003). CONCLUSION: To achieve a better risk stratification, further classification of Stage IV C MTC patients by the T stage may be preferable.

A Modified T-Stage Classification for Gastric Neuroendocrine Tumors.

BACKGROUND: The eighth edition of the American Joint Committee on Cancer (AJCC) staging manual's TNM staging classification for gastric neuroendocrine tumors has been shown to have poor prognostic discriminability. The aim of present study was to propose a modified T-stage classification, and externally validate its performance in a separate population data registry. METHODS: A modified T-stage classification with tumor size and extent of tumor invasion was generated from the National Cancer Database between 2004 and 2014 (n = 1249). External validation was performed using the Surveillance, Epidemiology, and End Results registry between 1973 and 2013 (n = 539). RESULTS: In the National Cancer Database population, using the AJCC T-stage classification, the 5-y survival rates were 85.7%, 80.8%, 64.5%, and 46.1% in T1, T2, T3, and T4 patients respectively (P < 0.001). These rates were more contrasting with the modified T-stage (mT) classification at 87.0%, 78.2%, 59.0%, and 40.3% respectively (P < 0.001). When patients within each of the AJCC T stages were stratified by mT stages, significant survival heterogeneity was observed within each of the AJCC T2 to T4 stages (P < 0.01). Conversely, when mT stages were stratified by AJCC T stage, no survival difference was observed in any of the mT stages (P > 0.05). The same analyses were performed using Surveillance, Epidemiology, and End Results data, and all the observed results were validated. CONCLUSION: The current AJCC T stage classification categorizes patients into groups with heterogenous prognosis, thus failing to serve as an effective staging tool. A modified T-stage classification demonstrated significantly improved stratification for patients with gastric neuroendocrine tumors.

The prognostic role of tumor size in stage T1 gastric cancer.

BACKGROUND: The purpose was to assess the contribution of tumor size to the prognosis of patients with gastric cancer. METHODS: Patient data were sourced from the Surveillance, Epidemiology, and End Results program (SEER) database. Cox proportional risk regression was performed to determine the prognostic role of tumor size. Kaplan-Meier curves were conducted to calculate survival curves. Consistency index (c-index) and subject exercise curve (ROC) were utilized to assess the predictive ability of each factor on the prognosis of gastric cancer. RESULTS: Tumor size is preferable to other widely accepted prognostic clinical features in forecasting the survival of patients with gastric cancer. CONCLUSIONS: The discriminatory ability of tumor size at T1 stage is superior to many other clinical prognostic factors.

Tumor size rather than the thyroid invasion affects the prognosis of parathyroid carcinoma without lymph node or distant metastasis.

PURPOSE: This study aimed to explore the prognostic value of thyroid invasion of parathyroid carcinoma without lymph node or distant metastasis. METHODS: Two hundred and nine cases of parathyroid carcinoma from the SEER (1989-2014) were eligible for this study. A Chi-squared test, t test, X-tile, Kaplan-Meier curves, and multivariate Cox proportional hazard regression were used for analysis. RESULTS: Thyroid invasion, sex, race, age, radiation, and surgery were not significantly associated with cancer-specific survival by multivariate analysis. However, tumor size ≥ 4 cm was significantly associated with worse cancer-specific survival (P < 0.001). CONCLUSION: Thyroid invasion, which was the criterion for T1 and T2 staging criteria of parathyroid carcinoma according to the AJCC, did not affect the prognosis of patients with parathyroid carcinoma without local lymph node or distant metastasis. Our study indicates that a tumor size ≥ 4 cm may be an appropriate indicator of T1 and T2 cancer staging.

Validation of the risk factors for primary control of early T-stage laryngeal, oropharyngeal, and hypopharyngeal squamous cell carcinoma by transoral surgery: a prospective observational study.

BACKGROUND: We had previously identified the following risk factors for insufficient control of early T-stage head and neck cancer by transoral surgery (TOS): (1) tumor thickness > 7 mm on enhanced computed tomography (CT), and (2) poor differentiation in pathological examination. We subsequently used a different patient cohort to validate the usefulness of these factors in determining the need for adaptation of TOS. STUDY SETTING: A prospective observational study METHODS: Patients who received TOS as a definitive treatment between April 1, 2016 and September 30, 2020 were included. Primary control rates (by single TOS and TOS alone) in relation to the above-mentioned risk factors were calculated. Overall (O), recurrence-free (RF), and disease-free (DF) survival (S) outcomes were evaluated. A combination analysis based on the number of risk factors was also performed. RESULTS: Patients with tumor thickness > 7 mm had a 2.88-fold [95% confidence interval (CI) 1.01-8.51] higher risk of incomplete primary resection by single TOS, while patients who showed poor differentiation on pathological assessments had a 13.14-fold (95% CI 3.66-47.14) higher risk of insufficient primary control by TOS alone. The 3 year OS, RFS, and DFS rates were 99%, 83%, and 63%, respectively. Patients with both risk factors had a 93.00-fold (95% CI 4.99-1732.00) higher risk of incomplete primary control by TOS alone. CONCLUSIONS: Among patients with early-stage laryngeal, oropharyngeal, and hypopharyngeal squamous cell carcinoma, primary control by TOS alone may not be achieved in patients with both risk factors, that is, tumor thickness > 7 mm as measured by enhanced CT and poor differentiation on pathological examination.

[Application of Automated Machine Learning Based on Radiomics Features of T2WI and RS-EPI DWI to Predict Preoperative T Staging of Rectal Cancer].

OBJECTIVE: To explore the radiomics features of T2 weighted image (T2WI) and readout-segmented echo-planar imaging (RS-EPI) plus difusion-weighted imaging (DWI), to develop an automated mahchine-learning model based on the said radiomics features, and to test the value of this model in predicting preoperative T staging of rectal cancer. METHODS: The study retrospectively reviewed 131 patients who were diagnosed with rectal cancer confirmed by the pathology results of their surgical specimens at West China Hospital of Sichuan University between October, 2017 and December, 2018. In addition, these patients had preoperative rectal MRI. Tumor regions from preoperative MRI were manually segmented by radiologists with the ITK-SNAP software from T2WI and RS-EPI DWI images. PyRadiomics was used to extract 200 features-100 from T2WI and 100 from the apparent diffusion coefficient (ADC) calculated from the RS-EPI DWI. MWMOTE and NEATER were used to resample and balance the dataset, and 13 cases of T 1-2 stage simulation cases were added. The overall dataset was divided into a training set (111 cases) and a test set (37 cases) by a ratio of 3∶1. Tree-based Pipeline Optimization Tool (TPOT) was applied on the training set to optimize model parameters and to select the most important radiomics features for modeling. Five independent T stage models were developed accordingly. Accuracy and the area under the curve ( AUC) of receiver operating characteristic (ROC) were used to pick out the optimal model, which was then applied on the training set and the original dataset to predict the T stage of rectal cancer. RESULTS: The performance of the the five T staging models recommended by automated machine learning were as follows: The accuracy for the training set ranged from 0.802 to 0.838, sensitivity, from 0.762 to 0.825, specificity, from 0.833 to 0.896, AUC, from 0.841 to 0.893, and average precision (AP) from 0.870 to 0.901. After comparison, an optimal model was picked out, with sensitivity, specificity and AUC for the training set reaching 0.810, 0.875, and 0.893, respectively. The sensitivity, specificity and AUC for the test set were 0.810, 0.813, and 0.810, respectively. The sensitivity, specificity and AUC for the original dataset were 0.810, 0.830, and 0.860, respectively. CONCLUSION: Based on the radiomics data of T2WI and RS-EPI DWI, the model established by automated machine learning showed a fairly high accuracy in predicting rectal cancer T stage.

Sensitivity of Fecal Immunochemical Test for Colorectal Cancer Detection Differs According to Stage and Location.

BACKGROUND & AIMS: Fecal immunochemical tests (FITs) are widely used for colorectal cancer (CRC) screening. FITs detect most CRCs. Although detection of CRC at early stages is most relevant for reducing CRC mortality, there is limited evidence for the stage-specific sensitivity of the FIT in CRC detection. We estimated stage- and location-specific sensitivities of a quantitative FIT in a large cohort of patients with CRC. METHODS: Fecal samples were collected before treatment from 435 patients with newly diagnosed CRC. Sensitivities of a quantitative FIT (FOB Gold, Sentinel Diagnostics; Milano, Italy) for tumors of different T stages and overall TNM stages (according to Union for International Cancer Control) were calculated at the cutoff recommended by the manufacturer (17 µg/g feces) and at alternative cutoffs, ranging from 10 to 40 µg/g feces, overall and stratified by tumor location. RESULTS: At the cutoff recommended by the manufacturer, the FIT detected T1 tumors with 52% sensitivity (95% CI, 37%-67%), T2 tumors with 79% sensitivity (95% CI, 68%-88%), T3 tumors with 93% sensitivity (95% CI, 89%-95%), and T4 tumors with 84% sensitivity (95% CI, 72%-92%) (Ptrend < .0001). The FIT detected stage I cancers with 68% sensitivity (95% CI, 57%-78%), stage II cancers with 92% sensitivity (95% CI, 87%-96%), stage III cancers with 82% sensitivity (95% CI, 73%-89%), and stage IV cancers with 89% sensitivity (95% CI, 80%-95%) (Ptrend 0.01). The FIT detected T1 colorectal tumors with sensitivity values that were 22%-52% lower than for tumors of other T stages and stage I CRC with sensitivity values that were 11%-33% lower than for later-stage CRCs, at any of the evaluated cutoff values. The FIT detected T1 and stage I CRCs in the distal colon with sensitivity values of 32% and 52%, respectively. CONCLUSIONS: Although the FIT identifies patients with CRC with overall high sensitivity, it can miss approximately one-third of stage I CRCs. Studies are needed to increase noninvasive detection of early-stage CRC.

The variable impact of positive lymph nodes in cervical cancer: Implications of the new FIGO staging system.

OBJECTIVE: The 2018 International Federation of Gynecology and Obstetrics (FIGO) staging system for cervical cancer changed from a clinical system to a clinical/pathologic/radiologic system with stages IIIC1 and IIIC2 indicating positive pelvic and para-aortic lymph nodes, respectively. We evaluated the National Cancer Database (NCDB) for the impact on survival of lymph node metastases (LNM). METHODS: The NCDB from 2004 to 2015 was queried for patients with cervical cancer, yielding 115,819 patients. Patients with FIGO IVB (22,569), non-adeno/squamous cell histologies (5,909), unknown nodal status (60,695), or unknown survival time (9,473) were excluded. Survival was compared using Cox proportional hazard model based on nodal status. Univariate (UVA) and multivariate analyses (MVA) were done for the overall cohort, followed by UVA by individual stage. RESULTS: In 17,173 eligible patients, LNM negatively affected survival (UVA IIIC1 Hazard Ratio [HR] 2.0, p < 0.001, IIIC2 HR 3.9, p < 0.001, MVA IIIC1 HR 1.36, p < 0.001, IIIC2 HR 2.14, p < 0.001). In T1B, the effect of IIIC2 was most pronounced (HR 5.38, p < 0.001 versus HR 1.5 p = 0.001 for IIIC1 disease). In T3, the effect of LNM was markedly less: (HR 1.7, p < 0.001 for IIIC2 versus HR 1.2 p = 0.02 for IIIC1). Within T1B, there was no difference in survival for IIIC1 for the smaller T stages (IB1-2). CONCLUSION: In this study, LNM negatively affects prognosis in cervical cancer. The impact on survival varies by T stage with the greatest effect seen in stage T1B with IIIC2 disease.

Early squamous cell carcinoma of the oral tongue with histologically benign lymph nodes: A model predicting local control and vetting of the eighth edition of the American Joint Committee on Cancer pathologic T stage.

BACKGROUND: The eighth edition of the American Joint Committee on Cancer staging manual (AJCC8) added depth of invasion to the definition of pathologic T stage (pT). In the current study, the authors assess pT stage migration and the prognostic performance of the updated pT stage and compare it with other clinicopathologic variables in patients with early squamous cell carcinoma of the oral tongue (OTSCC; tumors measuring ≤4 cm) with histologically benign lymph nodes (pN0). METHODS: A multi-institutional cohort of patients with early OTSCC was restaged as per AJCC8. Primary endpoints were local recurrence (LR) and locoregional recurrence (LRR). Influential variables were identified and an LR/LRR prediction model was developed. RESULTS: There were a total of 494 patients, with 49 LR and 73 LRR. AJCC8 pT criteria resulted in upstaging of 37.9% of patients (187 of 494 patients), including 34.5% (64 of 185 patients) from pT2 to pT3, without improving the prognostication for LR or LRR. Both LR and LRR were found to be similar for patients with AJCC8 pT2 and pT3 disease. On multivariate analysis, LR was only found to be associated with distance to the closest margin (hazard ratio, 0.36; 95% CI, 0.20-0.64 [P = .0007]) and perineural invasion (hazard ratio, 1.92; 95% CI, 1.10-0.64 [P = .046]). Based on these 2 predictors, a final proportional hazards regression model (which may be used similar to a nomogram) was developed. The proposed model appeared to be superior to AJCC pT stage for estimating the probability of LR and LRR for individual patients with early OTSCC. CONCLUSIONS: AJCC8 pT criteria resulted in pT upstaging of patients with pN0 disease without improved LR or LRR prognostication. The proposed model based on distance to the closest margin and perineural invasion, status outperformed pT as a predictor of LR and LRR in patients with early OTSCC.

A population-based registry study on relative survival from melanoma in Germany stratified by tumor thickness for each histologic subtype.

BACKGROUND: Differences in relative survival (RS) of melanoma between histologic subtypes were discussed to be mainly caused by tumor thickness. OBJECTIVE: To investigate RS of melanoma, stratified by tumor thickness for each histologic subtype, and identify survival trends. METHODS: With use of cancer registry data on melanoma cases (International Classification of Diseases, 10th Revision, codes C43.0-C43.9) diagnosed in Germany in 1997-2013, 5- and 10-year age-standardized RS stratified by histologic subtype and stratified or standardized by T stage was estimated by standard and modeled period analyses. We restricted 10-year RS analyses to patients younger than 75 years. RESULTS: We analyzed 82,901 cases. Overall, the 5- and 10-year RS rates were 91.7% and 90.8%, respectively. Prognosis worsened with increasing T stage for all histologic subtypes, but T-stage distribution varied substantially. Survival differences by histologic subtype were strongly alleviated after adjustment for T stage but remained significant. Overall, 5-year RS increased significantly (by 3.8 percentage points) between the periods 2002-2005 and 2010-2013. This increase was no longer seen after adjustment for T stage. LIMITATIONS: Exclusion of cases on account of missing information on T stages, changes in the definition of T stages, and lack of information on screening and treatment limit our analyses. CONCLUSION: Differences in RS between histologic subtypes were strongly mediated by tumor thickness. Over time, RS of melanoma increased as a result of changes in T-stage distribution.

MRI-based radiomics of rectal cancer: preoperative assessment of the pathological features.

BACKGROUND: This study aimed to evaluate the significance of MRI-based radiomics model derived from high-resolution T2-weighted images (T2WIs) in predicting tumor pathological features of rectal cancer. METHODS: A total of 152 patients with rectal cancer who underwent surgery without any neoadjuvant therapy between March 2017 and September 2018 were included retrospectively. The patients were scanned using a 3-T magnetic resonance imaging, and high-resolution T2WIs were obtained. Lesions were delineated, and 1029 radiomics features were extracted. Least absolute shrinkage and selection operator was used to select features, and multilayer perceptron (MLP), logistic regression (LR), support vector machine (SVM), decision tree (DT), random forest (RF), and K-nearest neighbor (KNN) were trained using fivefold cross-validation to build a prediction model. The diagnostic performance of the prediction models was assessed using the receiver operating characteristic curves. RESULTS: A total of 1029 features were extracted, and 15, 11, and 11 features were selected to predict the degree of differentiation, T stage, and N stage, respectively. The best performance of the radiomics model for the degree of differentiation, T stage, and N stage was obtained by SVM [area under the curve (AUC), 0.862; 95% confidence interval (CI), 0.750-0.967; sensitivity, 83.3%; specificity, 85.0%], MLP (AUC, 0.809; 95% CI, 0.690-0.905; sensitivity, 76.2%; specificity, 74.1%), and RF (AUC, 0.746; 95% CI, 0.622-0.872; sensitivity, 79.3%; specificity, 72.2%). CONCLUSION: This study demonstrated that the high-resolution T2WI-based radiomics model could serve as pretreatment biomarkers in predicting pathological features of rectal cancer.

Risk factors effecting development of metachronous liver metastasis in rectal cancer patients after curative surgical resection. Shaukat Khanum Memorial Cancer Hospital and Research Centre, Lahore experience.

OBJECTIVE: To determine risk factors affecting development of metachronous liver metastasis in rectal cancer patients after curative surgical resection. METHODS: The retrospective cohort study was conducted at Shaukat Khanum Memorial Cancer Hospital and Research Centre, Lahore, Pakistan, and comprised data of patients with histologically proven rectal carcinoma admitted to the department of surgical oncology from January 2005 to December 2015. Clinical data of all patients, including age, gender, clinical presentation, clinical and pathological tumour-nodes-metastasis classification, neoadjuvant chemo-radiotherapy, surgery, adjuvant chemotherapy, pre- and postoperative carcinoembryonic antigen levels, histopathological findings and tumour recurrence were analysed. SPSS 23 was used for data analysis.. RESULTS: Of the 434 patients, 26(6%) developed liver metastasis. Of them, 18(69%) were male and 16(61.5%) were aged below 50 years. On clinical staging, 2(7.7%) patients had stage II disease, 22(84.6%) had stage III, and 2(7.7%) patients had stage IV disease. At last follow-up, 2(7.7%) patients were alive without disease, 7(27%) had expired, while 17(65.4%) were alive with disease.. CONCLUSIONS: Tumour depth, lymph node metastasis, postoperative carcinoembryonic antigen levels, complete tumour response on histopathology were found to beresponsible for metachronous liver metastases in rectal cancer patients following curative resection.

The detrimental effects of radiotherapy interruption on local control after concurrent chemoradiotherapy for advanced T-stage nasopharyngeal carcinoma: an observational, prospective analysis.

BACKGROUND: Previous studies have reported radiotherapy interruption (RTI) is associated with poor local control in two-dimensional radiotherapy (2DRT) era. However, it remains unclear whether RTI still affects local control for advanced T stage (T3-4) in the intensity-modulated radiation therapy (IMRT) era. We aim to evaluate whether RTI affects local control for T3-4 NPC treated with definitive IMRT. METHODS: In this observational prospective study, 447 T3-4 NPC patients treated with IMRT plus concurrent chemotherapy were included. All patients completed the planned radiotherapy course, and RTI was defined as the actual time taken to finish the prescribed course of radiotherapy minus the planned radiotherapy time. Receiver operating characteristic (ROC) curve was used for determined the cutoff point of RTI. The effects of RTI on local control were analyzed in multivariate analysis. RESULTS: At 5 years, the local relapse-free survival (LRFS) and overall survival (OS) rates were 93.7 and 85.7%, respectively. The cutoff RTI for LRFS was 5.5 days by ROC curve. Compared to patients with RTI >  5 days, patients with RTI ≤ 5 days had a significantly lower rate of LRFS (97% vs. 83%; P < 0.001). In multivariate analysis, RTI was a risk factor independently associated with LRFS (HR = 9.64, 95% CI, 4.10-22.65), but not for OS (HR = 1.09, 95% CI, 0.84-1.64). CONCLUSIONS: The current analysis demonstrates a significant correlation between prolonged RTI and local control in NPC, even when concurrent chemotherapy is used. We consider that attention to RTI seems to be warranted for patients with advanced T-stage NPC in the era of IMRT.

Label-free imaging for T staging of gastric carcinoma by multiphoton microscopy.

Gastric cancer is one of the most common malignancies worldwide. The accurate diagnosis of tumor invasion depth is critical for therapeutic strategy and prognosis. Without fluorescent labelling, multiphoton microscopy (MPM) imaging could directly reveal tissue architecture based on two-photon excited fluorescence (TPEF) and second harmonic generation (SHG). In this study, we aimed to explore the feasibility of MPM imaging to assess the gastric tumor morphology and infiltration. Unstained slides of 18 fresh gastric tissues with different T staging were examined by multiphoton microscopy. Morphological and quantitative analyses were both conducted. The nuclear area was defined as the area of nuclear boundary. Collagen content was defined as the ratio of SHG pixels to all pixels. Gastric normal and tumor tissues under different T stages visually presented with cellular and subcellular features on fluorescent imaging. The nuclear areas of normal and cancerous cells were 32.01 ± 2.89 and 58.41 ± 6.06 µm2 (P < 0.001), respectively. Collagen content was quantified as 0.087 ± 0.012 in normal mucosa but 0.020 ± 0.007 in cancerous mucosa (P < 0.001). All results were in accord with the paired H&E-stained slides. Our findings suggested the convincing potential of MPM for judging T staging of gastric cancer. Without staining intervention, TPEF and SHG of MPM imaging could objectively and quantitatively indicate the subcellular and molecular changes during carcinogenesis. With the advancement of deep penetration, self-focus imaging and three-dimensional (3D) visualization, label-free MPM imaging compacted with endoscopy could be further introduced to realize the real-time in vivo assessment of tumor invasion clinically.

Multiparametric magnetic resonance imaging versus Partin tables and the Memorial Sloan-Kettering cancer center nomogram in risk stratification of patients with prostate cancer referred to external beam radiation therapy.

PURPOSE: To evaluate the agreement between multiparametric Magnetic Resonance Imaging (mpMRI), Partin tables (PT) and the Memorial Sloan Kettering Cancer Center nomogram (MSKCCn) in assessing risk category in prostate cancer (PCa) patients referred to External Beam Radiotherapy (EBRT). MATERIALS AND METHODS: In this bicentric study, we prospectively enrolled 80 PCa patients who underwent pre-EBRT mpMRI on a 3.0T magnet with a multiparametric protocol including high-resolution, multiplanar T2-weighted sequences, diffusion-weighted imaging and dynamic contrast-enhanced imaging. National comprehensive cancer network risk categories were assessed using prostate-specific-antigen level, Gleason score and the T-stage as defined by mpMRI or nomograms. Cohen's kappa statistic was used to calculate the agreement between mpMRI and nomograms in assessing the T-stage (organ-confined (OC) vs. non-organ-confined (nOC) disease) and risk category (≤ low risk vs. intermediate risk vs. ≥ high risk). RESULTS: mpMRI showed poor agreement with PT and MSKCCn in assessing nOC versus OC (k = 0.16 for both), translating into an mpMRI-induced reclassification of PT- and MSKCCn-related risk category in 36.3% (k = 0.43) and 41.3% (k = 0.31) of cases, respectively, with most changes occurring towards intermediate risk category. CONCLUSIONS: mpMRI showed low agreement with nomograms as a tool to stratify PCa risk, leading to significant risk reclassification. Assuming that mpMRI is a more reliable surrogate standard of reference for pathology, this technique should refine or replace nomograms in risk classification before EBRT.

PREDICT: model for prediction of survival in localized prostate cancer.

PURPOSE: Current models for prediction of prostate cancer-specific survival do not incorporate all present-day interventions. In the present study, a pre-treatment prediction model for patients with localized prostate cancer was developed. METHODS: From 1989 to 2008, 3383 patients were treated with I-125 brachytherapy (n = 1694), external beam radiotherapy (≥74 Gy, n = 336) or radical prostatectomy (n = 1353). Pre-treatment parameters (clinical T-stage, biopsy grade, PSA and age) were related to the hazard of mortality by multivariate Cox proportional hazard analysis. The PRetreatment Estimation of the risk of Death In Cancer of the prosTate (PREDICT) model was developed. The predictive accuracy of the model was assessed by calibration and discrimination and compared to the Ash risk classification system. RESULTS: Of the 3383 patients analyzed, 2755 patients (81 %) were alive at the end of follow-up, 149 patients (4 %) died of prostate cancer and 365 patients (11 %) died of other causes, and for 114 patients (3 %) cause of death was unknown. Median follow-up time was 7.6 years. After correction for overoptimism, the c-statistic of the prediction model for prostate cancer-specific mortality was 0.78 (95 % CI 0.74-0.82), compared to 0.78 (95 % CI 0.75-0.81) for the risk classification system by Ash et al. The PREDICT model showed better calibration than the Ash risk classification system. CONCLUSIONS: The PREDICT model showed a good predictive accuracy and reliability. The PREDICT model might be a promising tool for physicians to predict disease-specific survival prior to any generally accepted intervention in patients with localized prostate cancer.