Tumor Size Research Articles

Liver transplantation following two conversions in a patient with huge hepatocellular carcinoma and portal vein invasion: A case report

BACKGROUND Surgical resection and liver transplantation (LT) are the most effective curative options for hepatocellular carcinoma (HCC). However, few patients with huge HCC (> 10 cm in diameter), especially those with portal vein tumor thrombus (PVTT), can receive these treatments. Selective internal radiation therapy (SIRT) can be used as a conversion therapy for them because it has the dual benefit of shrinking tumors and increasing residual hepatic volume. However, in patients with huge HCC, high lung absorbed dose often prevents them from receiving SIRT. CASE SUMMARY A 35-year-old man was admitted because of emaciation and pain in the hepatic region for about 1 month. The computed tomography scan showed a 20.2 cm × 19.8 cm tumor located in the right lobe–left medial lobes with right portal vein and right hepatic vein invasion. After the pathological type of HCC was confirmed by biopsy, two conversions were presented. The first one was drug-eluting bead transarterial chemoembolization plus hepatic arterial infusion chemotherapy and lenvatinib and sintilimab, converted to SIRT, and the second one was sequential SIRT with continued systemic treatment. The tumor size significantly decreased from 20.2 cm × 19.8 cm to 16.2 cm × 13.8 cm, then sequentially to 7.8 cm × 6.8 cm. In the meantime, the ratio of spared volume to total liver volume increased gradually from 34.4% to 55.7%, then to 62.9%. Furthermore, there was visualization of the portal vein, indicating regression of the tumor thrombus. Finally, owing to the new tumor in the left lateral lobe, the patient underwent LT instead of resection without major complications. CONCLUSION Patients with inoperable huge HCC with PVTT could be converted to SIRT first and accept surgery sequentially.

Radiographical consolidation tumor size and preoperative clinical characteristics are significantly correlated with the postoperative survival of patients with part-solid and pure-solid adenocarcinomas: a propensity score-matched analysis

Radiographical consolidation tumor size and preoperative clinical characteristics are significantly correlated with the postoperative survival of patients with part-solid and pure-solid adenocarcinomas: a propensity score-matched analysis

Management and follow-up strategies for patients with head and neck paraganglioma

Abstract Objective Head-neck paragangliomas (HNPGLs) are rare tumors with approximately half arising due to germline pathogenic variants (PVs) in succinate dehydrogenase genes (SDHx). Patients with HNPGL have heterogenous propensity to recur and metastasize. Thus, we aim to assess prevalence and predictors of recurrent (RD) and/or metastatic disease in patients with and without SDHx-related HNPGLs. Design and methods This cross-sectional study used retrospective data of 214 patients enrolled in six referral centers. Data included sex, age, primary tumor treatment, location, and size, biochemical phenotype, germline PVs, presence of RD (locoregional or new tumor), and/or metastasis. Results Patients with and without SDHx-related HNPGLs showed 74% and 40% prevalence of RD, respectively. Patients without SDHx-related HNPGLs presented with recurrent tumors only in head-neck regions. The only independent predictor for RD in the entire cohort was presence of SDHx PVs. Metastatic prevalence reached 9-13%. For patients with SDHx-related HNPGLs, large tumor size (>2.3cm, OR:50.0, CI:2.6-977.6), young age at initial diagnosis (<42years, OR:27.3, CI:1.8-407.2), and presence of SDHB PV (OR:15.6; CI:1.5-164.8) were independent predictors of metastasis. For patients without SDHx-related HNPGLs, only carotid-body location was an independent predictor of metastasis (OR:18.9, CI:2.0-182.5). Conclusions Patients without SDHx-related HNPGLs require long-term follow-up due to high prevalence of RD with imaging largely restricted to head-neck regions. As carotid-body HNPGLs have the highest metastatic risk among sporadic tumors, radical treatment with frequent follow-up is suggested until population-based data are available. Importantly, patients with SDHx-related HNPGLs might benefit from early radical treatment when tumors are still small to reduce metastatic risk.

Survival prediction in sigmoid-Colon-cancer patients with liver metastasis: a prospective cohort study

Abstract Purpose Sigmoid colon cancer (SCC) is a common type of colorectal cancer, frequently leading to liver metastasis. Predicting cause-specific survival (CSS) and overall survival (OS) in SCC with liver metastasis (SCCLM) patients is challenging due to the lack of suitable models. Methods Data from SCCLM patients (2010-2017) in the Surveillance, Epidemiology, and End Results (SEER) Program were recruited. Patients were split into training and validation groups (7:3). Prognostic factors were identified using competing risk and Cox proportional hazards models, and nomograms for CSS and OS were developed. Model performance was evaluated with the concordance index and calibration curves, with a two-sided p < .05 was considered statistically significant. Results 4,981 SCCLM patients were included, with a median follow-up of 20 months (IQR: 9-33 months). During follow-up, 72.25% of patients died (68.44% from SCC, 3.81% from other causes). Age, race, grade, T stage, N stage, surgery, chemotherapy, CEA, tumor deposits, lung metastasis, and tumor size were prognostic factors for both CSS and OS. The models demonstrated good discrimination and calibration performance, with C-index values of 0.79 (95% CI: 0.78-0.80) for CSS and 0.74 (95% CI: 0.73-0.75) for OS. A web-based application for real-time CSS predictions was created, accessible at https://shuaishao.shinyapps.io/SCCLM/. Conclusion Prognostic factors for SCCLM patients were identified basing on SEER database, and nomograms for CSS and OS showed good performance. A web-based application was developed to predict SCCLM-specific survival, aiding in survival risk stratification.

Effect of Chemotherapy and Surgery Timing on Mortality in Upper and Lower Extremity Osteosarcoma

Abstract Background Surgery with both neoadjuvant and adjuvant chemotherapy represents the standard of care for extremity osteosarcoma despite a lack of high-quality evidence for its use, and trial evidence that suggests that up-front surgery may result in better outcomes. This study estimated the difference in overall survival for the standard of care (“Neoadjuvant First”) vs upfront surgery first followed by adjuvant chemotherapy (“Surgery First”). Patients and methods Using Surveillance, Epidemiology, and End Results data, we identified patients age 5-29, diagnosed with a primary cancer of upper or lower extremity osteosarcoma between 2007 and 2019 who received surgery and chemotherapy. Our primary endpoint was the 5-year survival difference between the Surgery First and Neoadjuvant First groups. Results Adjusted 5-year survival was 74% for Surgery First patients and 67% for Neoadjuvant First patients, with a survival difference of 6.9% (95% CI -4.2% - 16.1%). In sensitivity analyses of 5-year survival, the results were consistent, showing a 6.8% to 13.7% higher 5-year survival in Surgery First patients. Significant mortality risk factors included older age, larger tumor size, the type of resection (salvage vs amputation), and stage 3-4 disease (vs stage 1-2 disease). Conclusion The evidence supporting neoadjuvant therapy in osteosarcoma care is weak. However, there is evidence that pausing chemotherapy in the peri-surgical period might affect outcomes. Consequently, this study, and its consistency with the results from the only randomized trial, suggests that there is reason to revisit a prospective, randomized trial of osteosarcoma treatment regarding the timing of surgery and chemotherapy.

Mesenchymal stem cells in tumor microenvironment: drivers of bladder cancer progression through mitochondrial dynamics and energy production

Mesenchymal stem cells (MSCs) in tumor microenvironment (TME) are crucial for the initiation, development, and metastasis of cancer. The impact and mechanism of MSCs on bladder cancer are uncertain. Here we analyzed 205 patient samples to explore the relationships between tumor-stroma ratio and clinicopathological features. A co-culture model and nude mouse transplantation were used to explore the biological roles and molecular mechanisms of MSCs on bladder cancer cells. We found that a high tumor-stroma ratio was significantly associated with a larger tumor size and higher T stage, pathological grade, number of vascular invasions, and poor overall survival. MSCs in TME promoted the ability of bladder cancer cells to proliferate, migrate, and invade in vitro and in vivo. Next, we demonstrated that MSCs enhance mitochondrial autophagy and mitochondrial biogenesis of bladder cancer cells, and increase energy production, thereby promoting bladder cancer cell progression. Kynurenine (Kyn) produced by MSCs could enhance mitochondrial function by activating the AMPK pathway. IDO1 inhibitor could reverse the tumor‑promoting effects of MSCs in vitro and in vivo. Our results demonstrated that tryptophan metabolites Kyn of MSCs in TME could enhance mitochondrial function by activating the AMPK pathway, thereby promoting bladder cancer cell progression.

Clinical Significance of Biliary Invasion at Diagnosis in Barcelona Clinic Liver Cancer Stage B-C Hepatocellular Carcinoma: a Nationwide Cohort Analysis in South Korea

Introduction Evidence of biliary invasion as a prognostic factor in patients with hepatocellular carcinoma is unclear. We aimed to verify the significance of clinically diagnosed biliary involvement in patients with Barcelona Clinic Liver Cancer stage B-C (BCLC B-C) hepatocellular carcinoma. Methods The Korean Liver Cancer Study Group randomly extracted data of patients with hepatocellular carcinoma enrolled in the Korean Central Cancer Registry between 2011–2016 from approximately 50 hospitals nationwide. After excluding records without information regarding serum bilirubin level, alpha-fetoprotein level, and Child-Pugh class, a pre-propensity score matching cohort comprising 4077 patients was included. Considering age, sex, body mass index, viral cause, serum bilirubin level, alpha-fetoprotein level, Child-Pugh class, tumor size, multiplicity, portal invasion, and extrahepatic metastasis, patients with and without bile duct invasion at initial imaging diagnosis were matched at a ratio of 1:2 from the pre- propensity score matching cohort to form a matched cohort (propensity score matching cohort). Results The pre-propensity score matching cohort included 4077 patients with BCLC B-C and 165 (4.0%) with biliary invasion at diagnosis. Regarding biliary invasion at diagnosis, one- and two-year overall survival rates were 41.2% and 29.1% (with invasion) and 54% and 40.9% (without invasion), respectively (p<0.0001). Corresponding cancer-specific survival rates at one and two years were 43.4% and 30.7% (with invasion) and 56.6% and 44% (without invasion), respectively (p<0.0001). Although biliary invasion was a significant factor affecting overall and cancer-specific survival rates in a univariate analysis, it was not statistically significant in multivariate analyses for overall (p=0.153) and cancer-specific (p=0.198) survival rates. The propensity score matching cohort included 165 patients with biliary invasion at diagnosis and 330 without biliary invasion. In the propensity score matching cohort, biliary invasion at diagnosis was not a significant factor affecting overall (p=0.603) or cancer-specific (p=0.960) survival rates in the univariate analyses. One- and two-year overall survival were 41.2% and 29.1% (with invasion) and 36.1% and 28.2% (without invasion), respectively. The corresponding cancer-specific survival at one and two years were 43.4% and 30.7% (with invasion) and 39.8% and 31.4% (without invasion), respectively. Multivariate analyses revealed that alpha-fetoprotein levels, Child-Pugh class, tumor singularity, tumor size, portal invasion, lymph node metastases, and distant metastases significantly affected both overall and cancer-specific survival rates. Conclusion Biliary invasion at diagnosis in patients with BCLC B-C does not affect overall or cancer-specific survival rates; however, other prognostic factors associated with biliary invasion could have a greater impact.

Simulating the impact of tumor mechanical forces on glymphatic networks in the brain parenchyma

AbstractThe brain glymphatic system is currently being explored in the context of many neurological disorders and diseases, including traumatic brain injury, Alzheimer’s disease, and ischemic stroke. However, little is known about the impact of brain tumors on glymphatic function. Mechanical forces generated during tumor development and growth may be responsible for compromised glymphatic transport pathways, reducing waste clearance and cerebrospinal fluid (CSF) transport in the brain parenchyma. One such force is solid stress, i.e., growth-induced forces from cell hyperproliferation and excess matrix deposition. Because there are no prior studies assessing the impact of tumor-derived solid stress on glymphatic system structure and performance in the brain parenchyma, this study serves to fill an important gap in the field. We adapted a previously developed Electrical Analog Model using MATLAB Simulink for glymphatic transport coupled with Finite Element Analysis for tumor mechanical stresses and strains in COMSOL. This allowed simulation of the impact of tumor mechanical force generation on fluid transport within brain parenchymal glymphatic units—which include perivascular spaces, astrocytic networks, interstitial spaces, and capillary basement membranes. We conducted a parametric analysis to compare the contributions of tumor size, tumor proximity, and ratio of glymphatic subunits to the stress and strain experienced by the glymphatic unit and corresponding reduction in flow rate of CSF. Mechanical stresses intensify with proximity to the tumor and increasing tumor size, highlighting the vulnerability of nearby glymphatic units to tumor-derived forces. Our stress and strain profiles reveal compressive deformation of these surrounding glymphatics and demonstrate that varying the relative contributions of astrocytes vs. interstitial spaces impact the resulting glymphatic structure significantly under tumor mechanical forces. Increased tumor size and proximity caused increased stress and strain across all glymphatic subunits, as does decreased astrocyte composition. Indeed, our model reveals an inverse correlation between extent of astrocyte contribution to the composition of the glymphatic unit and the resulting mechanical stress. This increased mechanical strain across the glymphatic unit decreases the venous efflux rate of CSF, dependent on the degree of strain and the specific glymphatic subunit of interest. For example, a 20% mechanical strain on capillary basement membranes does not significantly decrease venous efflux (2% decrease in flow rates), while the same magnitude of strain on astrocyte networks and interstitial spaces decreases efflux flow rates by 7% and 22%, respectively. Our simulations reveal that solid stress from growing brain tumors directly reduces glymphatic fluid transport, independently from biochemical effects from cancer cells. Understanding these pathophysiological implications is crucial for developing targeted interventions aimed at restoring effective waste clearance mechanisms in the brain. This study opens potential avenues for future experimental research in brain tumor-related glymphatic dysfunction.

A clinical prediction model for distant metastases of pediatric neuroblastoma: an analysis based on the SEER database

BackgroundPatients with distant metastases from neuroblastoma (NB) usually have a poorer prognosis, and early diagnosis is essential to prevent distant metastases. The aim was to develop a machine-learning model for predicting the risk of distant metastasis in patients with neuroblastoma to aid clinical diagnosis and treatment decisions.MethodsWe built a predictive model using data from the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2018 on 1,542 patients with neuroblastoma. Seven machine-learning methods were employed to forecast the likelihood of neuroblastoma distant metastases. Univariate and multivariate logistic regression analyses were used to identify independent risk factors for building machine learning models. Secondly, the subject operating characteristic area under the curve (AUC), Precision-Recall (PR) curves, decision curve analysis (DCA), and calibration curves were used to assess model performance. To further explain the optimal model, the Shapley summation interpretation method (SHAP) was applied. Ultimately, the best model was used to create an online calculator that estimates the likelihood of neuroblastoma distant metastases.ResultsThe study included 1,542 patients with neuroblastoma, multifactorial logistic regression analysis showed that age, histology, tumor size, tumor grade, primary site, surgery, chemotherapy, and radiotherapy were independent risk factors for distant metastasis of neuroblastoma (P < 0.05). Logistic regression (LR) was found to be the optimal algorithm among the seven constructed, with the highest AUC values of 0.835 and 0.850 in the training and validation sets, respectively. Finally, we used the logistic regression model to build a network calculator for distant metastasis of neuroblastoma.ConclusionThe study developed and validated a machine learning model based on clinical and pathological information for predicting the risk of distant metastasis in patients with neuroblastoma, which may help physicians make clinical decisions.

Differentially expressed microRNA in prognosis of gastric cancer with Lauren classification.

Gastric cancer (GC) is one of the most common tumors. There were several classifications of GC recently. The value of Lauren classification in evaluating the prognosis after radical gastrectomy was still unclear and the prognosis of gastric cancer remained relatively poor in the absence of prognostic biomarkers. This study aimed to explore microRNA (miRNA) in the prognosis of GC with different Lauren classification. A retrospective study of 1144patients was performed in this study. Quantificational reverse transcription-PCR (qRT-PCR) was used to examine the expression of miRNAs. Univariate and multivariate analysis were performed to evaluate prognosis value of Lauren classification. Total 1144 GC patients were recruited in this cohort, including 302 diffuse type (26.4%), 436 intestinal type (38.1%) and 406 mixed type (35.5%) GC. Multivariate analysis showed that Lauren classification, patients' age, tumor size, tumor infiltrating depth, vascular nerve infiltrating and metastatic lymph nodes ration were significantly correlated with GC patients' OS and DFS. The miR-141-3p, miR-200b-3p and miR-133a-5p were significantly down-regulated in diffuse type compared to intestinal type GC tissues, the miR-105-5p had significant lower expression in diffuse type compared with intestinal type and mixed type GC tissues. As a consequence of univariate analysis, low miR-141-3p in diffuse type GC showed significant worse OS and DFS than high miR-141-3p. Lauren classification was an independent prognostic factor in GC. MiR-141-3p was an independent prognostic factor and a promising prognostic biomarker in Lauren classification GC.

Utilizing machine learning algorithms for predicting risk factors for bone metastasis from right-sided colon carcinoma after complete mesocolic excision: a 10-year retrospective multicenter study

BackgroundBone metastasis (BM) occurs when colon cancer cells disseminate from the primary tumor site to the skeletal system via the bloodstream or lymphatic system. The emergence of such bone metastases typically heralds a significantly poor prognosis for the patient. This study's primary aim is to develop a machine learning model to identify patients at elevated risk of bone metastasis among those with right-sided colon cancer undergoing complete mesocolonectomy (CME).Patients and methodsThe study cohort comprised 1,151 individuals diagnosed with right-sided colon cancer, with a subset of 73 patients presenting with bone metastases originating from the colon. We used univariate and multivariate regression analyses as well as four machine learning algorithms to screen variables for 38 characteristic variables such as patient demographic characteristics and surgical information. The study employed four distinct machine learning algorithms, namely, extreme gradient boosting (XGBoost), random forest (RF), support vector machine (SVM), and k-nearest neighbor algorithm (KNN), to develop the predictive model. Additionally, the model was assessed using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA), while Shapley additive explanation (SHAP) was utilized to visualize and analyze the model.ResultsThe XGBoost algorithm performed the best performance among the four prediction models. In the training set, the XGBoost algorithm had an area under curve (AUC) value of 0.973 (0.953–0.994), an accuracy of 0.925 (0.913–0.936), a sensitivity of 0.921 (0.902–0.940), and a specificity of 0.908 (0.894–0.922). In the validation set, the XGBoost algorithm had an AUC value of 0.922 (0.833–0.995), an accuracy of 0.908 (0.889–0.926), a sensitivity of 0.924 (0.873–0.975), and a specificity of 0.883 (0.810–0.956). Furthermore, the AUC value of 0.83 for the external validation set suggests that the XGBoost prediction model possesses strong extrapolation capabilities. The results of SHAP analysis identified alkaline phosphatase (ALP) levels, tumor size, invasion depth, lymph node metastasis, lung metastasis, and postoperative neutrophil-to-lymphocyte ratio (NLR) levels as significant risk factors for BM from right-sided colon cancer subsequent to CME.ConclusionThe prediction model for BM from right-sided colon cancer developed using the XGBoost machine learning algorithm in this study is both highly precise and clinically valuable.

Machine learning to predict distant metastasis and prognostic analysis of moderately differentiated gastric adenocarcinoma patients: a novel focus on lymph node indicators

BackgroundModerately differentiated gastric adenocarcinoma (MDGA) has a high risk of metastasis and individual variation, which strongly affects patient prognosis. Using large-scale datasets and machine learning algorithms for prediction can improve individualized treatment. The specific efficacy of several lymph node indicators in predicting distant metastasis (DM) and patient prognosis in MDGA remains obscure.MethodsWe collected data from MDGA patients from the SEER database from 2010 to 2019. Additionally, we collected data from MDGA patients in China. We used nine machine learning algorithms to predict DM. Subsequently, we used Cox regression analysis to determine the risk factors affecting overall survival (OS) and cancer-specific survival (CSS) in DM patients and constructed nomograms. Furthermore, we used logistic regression and Cox regression analyses to assess the specific impact of six lymph node indicators on DM incidence and patient prognosis.ResultsWe collected data from 5,377 MDGA patients from the SEER database and 109 MDGC patients from hospitals. T stage, N stage, tumor size, primary site, number of positive lymph nodes, and chemotherapy were identified as independent risk factors for DM. The random forest prediction model had the best overall predictive performance (AUC = 0.919). T stage, primary site, chemotherapy, and the number of regional lymph nodes were identified as prognostic factors for OS. Moreover, T stage, number of regional lymph nodes, primary site, and chemotherapy were also influential factors for CSS. The nomograms showed good predictive value and stability in predicting the 1-, 3-, and 5-year OS and CSS in DM patients. Additionally, the log odds of a metastatic lymph node and the number of negative lymph nodes may be risk factors for DM, while the regional lymph node ratio and the number of regional lymph nodes are prognostic factors for OS.ConclusionThe random forest prediction model accurately identified high-risk populations, and we established OS and CSS survival prediction models for MDGA patients with DM. Our hospital samples demonstrated different characteristics of lymph node indicators in terms of distant metastasis and prognosis.

MAOA uVNTR Polymorphism in a Sample of Patients Diagnosed with Papillary Thyroid Cancer

Thyroid gland carcinoma (TGC), though only 1% of all carcinomas, is the most common endocrine neoplasm with an increasing incidence since the 1990s. Of the TGC types, papillary thyroid carcinoma (PTC) is the most common and has the best overall prognosis. Although primarily studied in various neural spectrum disorders, monoamine oxidase A (MAOA) may also contribute to cancer occurrence. This case control study assessed the prevalence of MAOA uVNTR polymorphism in PTC patients, compared its frequency with a healthy control, and assessed the variant’s impact on clinical features. The research participants consisted of 30 PTC patients (20 female, 10 male) over 18 years old who underwent thyroidectomy and radioiodine therapy at a Federal District private clinic and 30 paired and unrelated healthy volunteers (18 female, 12 male). The most frequent MAOA uVNTR alleles were 3R and 4R. Although no significant difference was detected in the genotypic distribution nor the PTC patients’ thyroglobulin, thyroid-stimulating hormone, and antithyroglobulin levels; body mass indexes; administered radiopharmaceutical (131I) doses; or biological sex, the presence of at least one 3R allele was associated with a larger tumor size (T3 + T4 staging). Thus, the 3R allele seems to be associated with PTC pathogenesis severity.

Conditional survival estimates for ependymomas reveal the dynamic nature of prognostication

BackgroundTraditional survival analysis is frequently used to assess the prognosis of ependymomas (EPNs); however, it may not provide additional survival insights for patients who have survived for several years. Thus, the conditional survival (CS) pattern of this disease is yet to be further investigated. This study aimed to evaluate the improvement of survival over time using CS analysis and develop a CS-based nomogram model for real-time dynamic survival estimation for EPN patients.MethodsData on patients with EPN were collected from the Surveillance, Epidemiology, and End Results (SEER) database. In order to construct and validate the model effectively, the selected patients were randomly divided at 7:3 ratio. CS is defined as the probability of surviving for a specified time period (y years) after initial diagnosis, given that the patient has survived x years. The CS pattern of EPN patients were explored. Then, the least absolute shrinkage and selection operator (LASSO) regression method with tenfold cross-validation was employed to identify prognostic predictors. Multivariate Cox regression was employed to develop a CS-based nomogram model, and we used this model to quantify EPN patient risk. Finally, the performance of the prediction model was also evaluated and verified.ResultsIn total, 1829 patients diagnosed with EPN were included in the study, with 1280 and 549 patients in the training and validation cohorts, respectively. The CS analysis demonstrated that patients' OS saw gradual improvements over time. With each additional year of survival post-diagnosis, the 10-year survival rate of EPN patients saw an increase, updating from 74% initially to 79%, 82%, 85%, 87%, 89%, 91%, 93%, 96%, and 98% (after surviving for 1–9 years, respectively). The LASSO regression model, which implements tenfold cross-validation, identified 7 significant predictors (age, tumor grade, tumor site, tumor extension, tumor size, surgery and radiotherapy) to develop a CS-based nomogram model. And further risk stratification was conducted based on nomogram model for these patients. Furthermore, this survival prediction model was successfully validated.ConclusionThis study described the CS pattern of EPN patients and highlighted the gradual improvement of survival observed over time for long-term survivors. We also developed the first novel CS-nomogram model that enabled individualized and real-time prognosis prediction. But patients must be counselled that individual circumstances may not always accurately reflect the findings of the nomogram.

Breast cancer: agreement between residual tumor size after neoadjuvant chemotherapy measurement with different imaging methods and the histological data

BackgroundThe evaluation of residual tumor size post-neoadjuvant chemotherapy (NAC) could have an impact on surgical planning. Accurate measurement may avoid overly radical surgery or reduce the need for repeat surgery. The purpose of this study is to evaluate the size of residual tumor after NAC using mammography and ultrasound or MRI and thereby establish which imaging method shows most agreement with histological result.MethodsData from 125 patients, from 40 to 86 years old, with primary breast cancer and indication for neoadjuvant chemotherapy were analyzed in a retrospective study. All patients underwent conventional imaging (mammography and ultrasound) or MRI in addition to conventional imaging, after NAC and before surgery. Intra-class correlation (ICC) was calculated, and a Bland–Altman plot was used to determine the agreement between imaging residual tumor size (from US/mammography and MRI) and histological residual tumor size. The results were divided into the groups: invasive ductal carcinoma (IDC) + ductal carcinoma in situ (DCIS), invasive ductal carcinoma (IDC), invasive lobular carcinoma (ILC) and “other tumors” (i.e., mucinous carcinoma).ResultsOf 125 analyzed patients, 44 (34.65%) underwent MRI and 81 (65.35%) had conventional imaging only. Agreement between residual tumor size founded on MRI and histological size is stronger than the data from conventional imaging.ConclusionsMRI performance was generally superior to conventional imaging, and it may be considered the most appropriate test to evaluate the residual tumor size post-NAC before surgery.

SIMAP500: A novel risk score to identify recipients at higher risk of hepatocellular carcinoma recurrence following liver transplantation.

Recurrence of hepatocellular carcinoma (HCC) following liver transplantation (LT) has a devastating influence on recipients' survival; however, the risk of recurrence is not routinely stratified. Risk stratification is vital with a long LT waiting time, as that could influence the recurrence despite strict listing criteria. This study aims to identify predictors of recurrence and develop a novel risk prediction score to forecast HCC recurrence following LT. A retrospective review of LT for HCC recipients at University Hospitals Birmingham between July 2011 and February 2020. Univariate and multivariate analyses were performed to identify recurrence predictors, based on which the novel SIMAP500 (satellite nodules, increase in size, microvascular invasion, AFP > 500, poor differentiation) risk score was proposed. 234 LTs for HCC were performed with a median follow-up of 5.3 years. Recurrence developed in 25 patients (10.7%). On univariate analyses, RETREAT score > 3, α-fetoprotein (AFP) at listing 100-500 and > 500, bridging, increased tumour size between imaging at the listing time and explant histology, increase in the size of viable tumour between listing and explant, presence of satellite nodules, micro- and macrovascular invasion on explant and poor differentiation of tumours were significantly associated with recurrence, based on which, the SIMAP500 risk score is proposed. The SIMAP500 demonstrated an excellent predictive ability (c-index = 0.803) and outperformed the RETREAT score (c-index = 0.73). SIMAP500 is indicative of the time to disease recurrence. SIMAP500 risk score identifies the LT recipients at risk of HCC recurrence. Risk stratification allows patient-centric post-transplant surveillance programs. Further validation of the score is recommended.

Values of apparent diffusion coefficient in pancreatic cancer patients receiving neoadjuvant therapy

BackgroundTo investigate the values of apparent diffusion coefficient (ADC) for the treatment response evaluation in pancreatic cancer (PC) patients receiving neoadjuvant therapy (NAT).MethodsThis study included 103 NAT patients with histologically proven PC. ADC maps were generated using monoexponential diffusion-weighted imaging (b values: 50, 800 s/mm2). Tumors’ minimum, maximum, and mean ADCs were measured and compared pre- and post-NAT. Variations in ADC values measured between pre- and post-NAT completion for NAT methods (chemotherapy, chemoradiotherapy), tumor locations (head/neck, body/tail), tumor regression grade (TRG) levels (0–2, 3), N stages (N0, N1/N2) and tumor resection margin status (R0, R1), were further analyzed.ResultsThe minimum, maximum, and mean ADC values all increased dramatically after NAT, rising from 23.4 to 25.4% (all p < 0.001): mean (average: 1.626 × 10− 3 mm2/s vs. 1.315 × 10− 3 mm2/s), minimum (median: 1.274 × 10− 3 mm2/s vs. 1.034 × 10− 3 mm2/s), and maximum (average: 1.981 × 10− 3 mm2/s vs. 1.580 × 10− 3 mm2/s). The ADCs between the subgroups of all the criteria under investigation did not differ significantly for the minimum, maximum, or mean values pre- or post-NAT (P = 0.08 to 1.00). In the patients with borderline resectable PC (n = 47), the rate of tumor size changes after NAT was correlated with the pre-NAT mean ADC values (Spearman’s coefficient: 0.288, P = 0.049).ConclusionsThe ADC values of PC increased significantly following NAT; however, the percentage increases failed to provide any predictive value for the resection margin status or TRG levels.

Treatment outcomes in retinoblastoma and the effect of tumor topography.

Retinoblastoma, the most common intraocular malignancy in children, has high fatality rates if untreated. It is crucial to monitor treatment effectiveness and explore factors influencing favorable outcomes. Our study aims to examine how tumor location impacts the response to standard treatments and the achievement of favorable outcomes among retinoblastoma patients, while controlling for other tumor-related factors. This retrospective study analyzed medical records of retinoblastoma patients from November 2012 to December 2022 enrolled in the retinoblastoma program at the Children's Cancer Center of Lebanon (established in collaboration with St.yJude Children's Research Hospital, Memphis, TN). Data were extracted from the electronic chart reviews and operative reports of examinations under anesthesia (EUAs), and included patient's demographics, tumor characteristics (size, location), and treatment parameters (treatment type, resolution, recurrence). The study included 42 patients with retinoblastoma, with a total of 57 eyes and 115 tumors/lesions. The median age at diagnosis was 12months (range: 2-36months). Among the patients, 26 (61.9%) were males and 16 (38.1%) were females. A minority of patients (21.4%) presented with unilateral involvement, whereas the majority (78.6%) had bilateral involvement. The locations of retinoblastoma lesions were distributed as follows: optic nerve (4.4%), macula (19.1%), superior (16.5%), inferior (17.4%), nasal (27.8%), and temporal (14.8%). Resolution rate tended to be highest for tumors close to optic nerve and temporal lesions, but no statistical significance was attained (p=.45). Macular lesions tended to have the fastest resolution, but again not significantly (p=.5). Multiple logistic regression revealed that the odds for resolution of tumor was not significantly associated with tumor size (p=.57) or location (p=.52). Location of retinoblastoma lesions was not directly associated with recurrence-free resolution in our cohort. Further research in large retinoblastoma databases is needed to explore the association of tumor characteristics with recurrence and the need for secondary therapeutic interventions.

Evaluating segment anything model (SAM) on MRI scans of brain tumors

Addressing the challenge of automatically segmenting anatomical structures from brain images has been a long-standing problem, attributed to subject- and image-based variations and constraints in available data annotations. The Segment Anything Model (SAM), developed by Meta, is a foundational model trained to provide zero-shot segmentation outputs with or without interactive user inputs, demonstrating notable performance on various objects and image domains without explicit prior training. This study evaluated SAM’s performance in brain tumor segmentation using two publicly available Magnetic Resonance Imaging (MRI) datasets. The study analyzed SAM’s standalone segmentation as well as its performance when provided user interaction through point prompts and bounding box inputs. SAM exhibited versatility across configurations and datasets, with the bounding box consistently outperforming others in achieving superior localized precision, with average Dice scores of 0.68 for TCGA and 0.56 for BRATS, along with average IoU values of 0.89 and 0.65, respectively, especially for tumors with low-to-medium curvature. Inconsistencies were observed, particularly in relation to variations in tumor size, shape, and textural features. The conclusion drawn from the study is that while SAM can automate medical image segmentation, further training and careful implementation are necessary for diagnostic purposes, especially with challenging cases such as MRI scans of brain tumors.

PCSK1N as a tumor size marker and an ER stress response protein in corticotroph pituitary adenomas.

Silent corticotroph adenoma (SCA) exhibits more tumor aggressiveness features than functioning adenomas (FCA). We aimed to investigate PCSK1N expression in CA and examine if ER stress-induced responses affect cell survival in a corticotroph tumor cell model. Clinical and imaging characteristics were recorded in 33 patients with FCA (20 women, 11 macroadenomas) and 18 SCA (8 women, all macroadenomas). Gene expression of proopiomelanocortin (POMC), T-box transcription factor 19(TBX19)/TPIT, proprotein convertase subtilisin/kexin type 1(PCSK1)/PC1/3, and its inhibitor PCSK1N, was measured by RT-qPCR in adenoma tissue.Mouse pituitary corticotroph tumor (AtT-20) cells were treated with tanespimycin (17-AAG), a HSP90 chaperone inhibitor, to induce ER stress, followed by gene and protein analyses. POMC, TPIT, and PCSK1 expression were higher, whereas PCSK1N was lower in FCA compared to SCA. PCSK1N correlated with POMC (rs= -0.514, p <0.001), TPIT (rs= -0.386, p = 0.005), PCSK1 (rs= -0.3691, p = 0.008), and tumor largest diameter (rs= 0.645, p <0.001), in all CA. Induction of ER stress by 17-AAG in AtT-20 cells led to a decrease of POMC and an increase of PCSK1N gene expression at 24h. Moreover, a downregulation of cell cycle, apoptosis, and senescence pathways, and alterations in cell adhesion and cytoskeleton were observed at the protein level. PCSK1N is higher in SCA compared with FCA, and associated with corticotroph cell markers and tumor size. PCSK1N is likely to be part of the adaptive response to ER stress, potentially conferring a survival advantage to the corticotroph tumor cell in conjunction with other proteins.