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Background: Using a previously unreported Peruvian registry of patients treated for early- stage non-small cell lung cancer (NSCLC), this study explored whether wedge resection and lobectomy were equivalent regarding survival and impact on radiologic-pathologic variables. Methods: This observational, analytical, longitudinal study used propensity scorematched (PSM) analysis of a single-center retrospective registry of 2,570 patients with pathologic stage I–II NSCLC who were treated with wedge resection (n=1,845) or lobectomy (n=725) during 2000–2020. After PSM, 650 cases were analyzed (resection, n=325; lobectomy, n=325) through preoperative and clinical variables, including patients with ≥1 lymph node removed. Kaplan-Meier curves and multivariable Cox proportional hazard models were created for 5-year overall survival (OS), disease-free survival (DFS), and locoregional- recurrence-free survival (LRFS). Results: The principal complication was operative pain persisting >7 days for lobectomy versus wedge resection (58% vs. 23%, p=0.034) and shorter hospital stays for resection than for lobectomy (5.3 days vs. 12.8 days, p=0.009). The 5-year OS (84.3% vs. 81.2%, p=0.09) and DFS (79.1% vs. 74.1%, p=0.07) were similar and statistically insignificant between resections and lobectomies, respectively. LRFS was worse overall following wedge resection than lobectomy (79.8% vs. 91.1%, p10 mm (90.9% vs. 87.3%, p

Introduction: During the Epithelial-mesenchymal Transition (EMT) process, cells undergo a transformation in which they lose their epithelial characteristics and acquire mesenchymal traits. This phenomenon was observed during the metastatic stage of neoplastic disorders, such as cervical cancer. Certain indicators are present during EMT. Aim: To evaluate the role of EMT in utero-cervical carcinoma by analysing the expressions of E-cadherin and vimentin in histopathologically confirmed cases. Materials and Methods: This retrospective cross-sectional study was conducted over a period of two years, from January 1, 2013, to December 31, 2014, at the Department of Pathology in a tertiary care centre and referral hospital. A total of 50 histopathologically confirmed cases of utero-cervical Squamous Cell Carcinoma (SCC) were included in this study. These case were analysed focusing on variables such as age, age at marriage, parity, family history, and hormonal intake. Tumour stages and grades were assessed alongside E-cadherin and vimentin expressions using Immunohistochemistry (IHC). Results were statistically analysed, and the database was created in MS Excel (Version 2007) and analysed using IBM Statistical Package for the Social Sciences (SPSS) Statistics software version 20.0, with significance set at p

Abstract Background The COVID-19 pandemic has had negative drawbacks on the healthcare system worldwide and on individuals other than those directly affected by the virus. Delays in cancer therapy and diagnosis have been reported in the literature. We hypothesized similar effects on patients with lung cancer at our center. Methods We retrospectively analyzed data of patients referred to our center with newly diagnosed lung cancer from 2018 to 2022. We considered distribution of UICC Stages and time from case presentation in our multidisciplinary tumor board or from therapeutic indication from treating physician to therapy initiation (surgery, systemic therapies and radiation) to define delays in diagnosis and treatment. Results 1020 patients with newly diagnosed lung cancer were referred to our center from 2018 to 2022, with a median of 206 cases yearly (range: 200–208). Cases with Stage IV in 2020–2022 were significantly higher than in 2018–2019 (57% vs. 46%, p = 0,001). 228 operative resections took place between 2018 and 2022, 100 from January 2018 to February 2020 and 128 from March 2020 to December 2022. Median time from presentation in our tumor board to resection was also significantly longer after the beginning of the pandemic than before (22 days vs. 15,5 days, p = 0,013). No significant delays were observed for administration of systemic treatment and initiation of radiation. Conclusions During the pandemic higher disease stages were reported for patients with lung cancer, yet there were no clinically relevant delays in treatment. In the context of the post-covid era new diagnostic strategies are necessary to facilitate early diagnosis of lung cancer. Despite the pandemic, for patients with suspicious symptoms prompt access to healthcare facilities is essential for early diagnosis.

Background Whole-body magnetic resonance imaging is accurate, efficient and cost-effective for cancer staging. Machine learning may support radiologists reading whole-body magnetic resonance imaging. Objectives To develop a machine-learning algorithm to detect normal organs and cancer lesions. To compare diagnostic accuracy, time and agreement of radiology reads to detect metastases using whole-body magnetic resonance imaging with concurrent machine learning (whole-body magnetic resonance imaging + machine learning) against standard whole-body magnetic resonance imaging (whole-body magnetic resonance imaging + standard deviation). Design and participants Retrospective analysis of (1) prospective single-centre study in healthy volunteers > 18 years (n = 51) and (2) prospective multicentre STREAMLINE study patient data (n = 438). Tests Index: whole-body magnetic resonance imaging + machine learning. Comparator: whole-body magnetic resonance imaging + standard deviation. Reference standard Previously established expert panel consensus reference at 12 months from diagnosis. Outcome measures Primary: difference in per-patient specificity between whole-body magnetic resonance imaging + machine learning and whole-body magnetic resonance imaging + standard deviation. Secondary: per-patient sensitivity, per-lesion sensitivity and specificity, read time and agreement. Methods Phase 1: classification forests, convolutional neural networks, and a multi-atlas approaches for organ segmentation. Phase 2/3: whole-body magnetic resonance imaging scans were allocated to Phase 2 (training = 226, validation = 45) and Phase 3 (testing = 193). Disease sites were manually labelled. The final algorithm was applied to 193 Phase 3 cases, generating probability heatmaps. Twenty-five radiologists (18 experienced, 7 inexperienced in whole-body magnetic resonance imaging) were randomly allocated whole-body magnetic resonance imaging + machine learning or whole-body magnetic resonance imaging + standard deviation over two or three rounds in a National Health Service setting. Read time was independently recorded. Results Phases 1 and 2: convolutional neural network had best Dice similarity coefficient, recall and precision measurements for healthy organ segmentation. Final algorithm used a ‘two-stage’ initial organ identification followed by lesion detection. Phase 3: evaluable scans (188/193, of which 50 had metastases from 117 colon, 71 lung cancer cases) were read between November 2019 and March 2020. For experienced readers, per-patient specificity for detection of metastases was 86.2% (whole-body magnetic resonance imaging + machine learning) and 87.7% (whole-body magnetic resonance imaging + standard deviation), (difference −1.5%, 95% confidence interval −6.4% to 3.5%; p = 0.387); per-patient sensitivity was 66.0% (whole-body magnetic resonance imaging + machine learning) and 70.0% (whole-body magnetic resonance imaging + standard deviation) (difference −4.0%, 95% confidence interval −13.5% to 5.5%; p = 0.344). For inexperienced readers (53 reads, 15 with metastases), per-patient specificity was 76.3% in both groups with sensitivities of 73.3% (whole-body magnetic resonance imaging + machine learning) and 60.0% (whole-body magnetic resonance imaging + standard deviation). Per-site specificity remained high within all sites; above 95% (experienced) or 90% (inexperienced). Per-site sensitivity was highly variable due to low number of lesions in each site. Reading time lowered under machine learning by 6.2% (95% confidence interval −22.8% to 10.0%). Read time was primarily influenced by read round with round 2 read times reduced by 32% (95% confidence interval 20.8% to 42.8%) overall with subsequent regression analysis showing a significant effect (p = 0.0281) by using machine learning in round 2 estimated as 286 seconds (or 11%) quicker. Interobserver variance for experienced readers suggests moderate agreement, Cohen’s κ = 0.64, 95% confidence interval 0.47 to 0.81 (whole-body magnetic resonance imaging + machine learning) and Cohen’s κ = 0.66, 95% confidence interval 0.47 to 0.81 (whole-body magnetic resonance imaging + standard deviation). Limitations Patient whole-body magnetic resonance imaging data were heterogeneous with relatively few metastatic lesions in a wide variety of locations, making training and testing difficult and hampering evaluation of sensitivity. Conclusions There was no difference in diagnostic accuracy for whole-body magnetic resonance imaging radiology reads with or without machine-learning support, although radiology read time may be slightly shortened using whole-body magnetic resonance imaging + machine learning. Future work Failure-case analysis to improve model training, automate lesion segmentation and transfer of machine-learning techniques to other tumour types and imaging modalities. Study registration This study is registered as ISRCTN23068310. Funding This award was funded by the National Institute for Health and Care Research (NIHR) Efficacy and Mechanism Evaluation (EME) programme (NIHR award ref: 13/122/01) and is published in full in Efficacy and Mechanism Evaluation; Vol. 11, No. 15. See the NIHR Funding and Awards website for further award information. Plain language summary Whole-body magnetic resonance imaging demonstrates the entire body and can detect the spread of tumour, without the burden of ionising radiation. Recently, the STREAMLINE study reported that whole-body magnetic resonance imaging is accurate, efficient and cost-effective for cancer staging. However, whole-body magnetic resonance imaging is complex to report. Machine learning is a type of artificial intelligence whereby a computer learns from being given previous data to undertake a task, using techniques such as classification forests, convolutional neural networks, and multi-atlas approaches. Our aim was to develop a machine-learning method to automatically detect lesions on whole-body magnetic resonance imaging to support radiologists by potentially improving their ability to correctly detect disease and reduce the reading time of whole-body magnetic resonance imaging scans in patients with cancer. Firstly, whole-body magnetic resonance imaging scans from 51 healthy volunteers were used to develop machine-learning methods to automatically detect normal organs. Secondly, machine-learning methods were trained to detect cancer lesions, using 271 whole-body magnetic resonance imaging scans from a previous study. Finally, the refined machine-learning technique was tested in 188 patient scans from a previous study, to see if the technique could improve radiology reporting by increasing accuracy and speed in detecting disease. We designed a system to test the accuracy of radiologists reading whole-body magnetic resonance imaging with or without machine-learning support in a near-real clinical National Health Service setting. Twenty-five independent radiologists (18 experienced in reading whole-body magnetic resonance imaging and 7 radiologists inexperienced in whole-body magnetic resonance imaging) were randomly allocated whole-body magnetic resonance imaging scans to read with or without machine-learning support. We found that machine-learning support resulted in similar accuracy for detecting disease and was slightly more efficient in the reading time than for radiological reads without machine-learning support. Differences in interpretation between experienced readers were considered moderate in both cases. Overall, the study was an ambitious attempt to undertake a highly complex machine-learning task, to detect cancer on whole-body magnetic resonance imaging. Many important steps have been taken but the current machine-learning algorithm did not result in a significant improvement in the radiologist’s accuracy for disease detection, although it may have slightly reduced the time taken to read the study. Future work is advocated to further develop machine-learning tools to improve the accuracy of tumour detection. Scientific summary Background Whole-body magnetic resonance imaging (WB-MRI) has been developed in the last decade and it has been proposed as an alternative to multimodality cancer staging pathways. The STREAMLINE study has demonstrated that staging of lung and colon cancer via WB-MRI was of similar accuracy to standard care staging pathways but resulted in fewer tests being required, and a reduction in staging time and cost. However, this technique has not widely translated into clinical practice, being limited to a few expert centres. A barrier to translation may be the complexity of interpretation by inexperienced readers. A machine-learning (ML) algorithm for automated detection of cancer lesions, to assist radiologists, may allow clinical translation of WB-MRI for the benefit of patient care. Objective The objective of Phases 1 and 2 was to develop a ML method for automatic detection of cancer lesions on WB-MRI. The primary objective of Phase 3 was to compare the diagnostic test accuracy of WB-MRI in patients being staged for cancer with and without ML support, when read by independent, experienced readers. The reference standard was the consensus reference panel from the STREAMLINE study, which had recorded the sites of disease in the STREAMLINE patients using all available clinical information for a 12-month follow-up from time of enrolment. The planned secondary objectives of this study were: to compare the reading time of WB-MRI scans, with and without ML support; to determine the interobserver variability of WB-MRI diagnosis by different radiologists, with and without ML support; to evaluate the diagnostic accuracy of WB-MRI read by non-experienced readers, with and without ML support; to evaluate different combinations of acquired MRI sequences; all the above with and without ML support (not achieved); to determine any difference in costs related to radiology reading time by means of a simple cost-effectiveness analysis (not achieved). Design This was an observational study (study limited to working with data), using different patient cohorts, and methodologies, being evaluated in series during three consecutive phases. Phase 1: Previously acquired WB-MRI scans in 51 healthy volunteers were stitched into imaging volumes. The normal organs and skeleton were segmented by a trained radiologist. We compared and tested several state-of-the-art medical image segmentation methods to train an algorithm to automatically detect and segment the organs. This included a multi-atlas (MA) approach, classification forests (CFs) and convolutional neural networks (CNNs) methods. For the CFs method, we used 50 trees with a maximum tree depth of 30 in the segmentation. The stopping criterion for growing trees was if either the objective function (information gain) could not be further improved or the number of training samples in a leaf fell below a threshold of four samples. For the CNNs method, we employed a dual pathway (2 resolutions), 11-layer deep CNN, where the last 2 layers correspond to fully connected layers, which combine the features extracted on the 2 resolution pathways. We adopted 50–70 feature maps (different kernels) for each layer. The network architecture was fully convolutional and there were no max-pooling layers, which we found to increase segmentation accuracy. The CNN architecture was a balance between model capacity, training efficiency, and memory demands. The third algorithm was based on a MA label propagation approach. MA segmentation uses a set of atlases (images with corresponding segmentations) that represent the interparticipant variability of the anatomy to be segmented. Each atlas was registered to the new image to be segmented using a deformable image registration. The MA approach accounts for anatomical shape variability and is more robust than single-atlas propagation methods, in that at any errors associated with propagation were averaged out when combining multiple atlases. The approach employed here makes use of efficient 3D–3D intensity-based image registration with free-form deformations as the transformation model and correlation coefficient as the similarity measure. Majority voting is used to derive the final tissue label at each voxel. Phase 2: Available WB-MRI scans from the STREAMLINE study (n = 438) were allocated by the study statistician to Phase 2 (model training) and clinical validation (hold-out set of 193 cases). The visible lesions in cases allocated to Phase 2 were segmented by trained radiologists, using the defined consensus reference standard for the sites of disease. Using 226 evaluable and annotated cases, we trained a model for lesion detection and localisation rather than attempting accurate automated segmentation. We found that lesion detection in WB-MRI was suboptimal with the CNN alone. An optimal ‘two-stage’ ML method was developed and tested. In the first stage of the ‘two-stage’ method, the information from Phase 1 was used to identify the position of organs and bones and in stage two, the lesions were detected. Stage two could be modular with respect to the anatomical location in which the suspected lesion could be found. The architecture and configuration of the used CNN were modified to achieve optimal performance. Phase 3: The final two-stage ML algorithm from Phase 2 was applied to the 193 cases that were held out for Phase 3, generating probability heatmap volumes. WB-MRI scans were reported by 25 independent radiologists (18 radiologists experienced in reading WB-MRI and 7 radiologists inexperienced in WB-MRI). All radiologists took part in the 1st and 2nd round reads which incorporated inter-rater reads, and 8 participated in the 3rd round read for intrarater reads. All reads were undertaken in an NHS radiology reporting room, although using a separate cloud-based picture archiving and communication system (PACS). Based on experience level, 10–16 cases were randomly allocated by the study statistician to the reading lists for inexperienced or experienced radiologists. Each radiologist read both lung and colon cases with and without ML as a paired cohort. There were at least 4 weeks interval between each reading round for individual radiologists. Cases allocated to round 1 would be a random mixture of cases with and without ML support and then in round 2 the cases would be reversed such that each case was read once with and once without ML support. The number of cases with and without ML output was balanced in each reading round. A scribe recorded reading time. Results Phase 1 results We found that CNNs outperformed CFs and the MA algorithm when T2w volumes were used as input to the algorithms and when using pooled overlap-evaluation metrics [Dice similarity coefficient (DSC), recall (RE), precision (PR)] to assess the accuracy of segmentation. When the performance of the algorithms was assessed, with pooled surface distance metrics [average surface distance (ASD), root-mean-square surface distance (RMSSD), and Hausdorff distance (HD)], it was the MA algorithm that performed best. Single misinterpreted voxels in CFs and CNNs can greatly elevate ASD, RMSSD, and HD; these metrics are particularly sensitive to outliers. We then assessed the pooled metrics performance of CFs and CNNs when using all imaging combinations [T2w + T1w + diffusion-weighted imaging (DWI)] as input, arguing that maximisation of training information to the algorithms might improve the performance of segmentation. We found that the performance of CFs was improved, however not significantly, when using all imaging combinations as input for training. The opposite was observed for CNNs. The findings for the pooled metrics analysis, described above, were corroborated by a ‘per-organ’ quantitative analysis of the commonly used DSC, to assess the performance of our segmentation algorithms. This analysis confirmed that for all individual anatomical structures (except for the bladder), the algorithm that returned the greatest DSC was CNNs with T2w images only used as input. As our morphological T2w and T1w scans were acquired using breath-holds and the DWI sequence was acquired with free breathing, we found that there was significant displacement between soft tissues in anatomical areas adjacent to the diaphragm between these types of scans. As the employed affine registration method could not fully compensate for nonlinear motions caused by breathing, we assumed that misregistration could be the reason why the performance of CNNs, despite performing better than the other two algorithms when using T2w volumes as input only, was degraded when using all imaging combinations as input for training. A more robust, nonlinear registration method could improve the accuracy of CNNs and further improve the performance of CFs. The performance of our methods cannot be directly compared to similar methods in the literature because there is no previous work describing automatic, simultaneous segmentation of healthy organs and bones in multiparametric WB-MRI. We believe, however, that our methods may compare favourably to other ML methods for detection and segmentation in medical imaging because our classifiers are inherently multilabel and effective training was achieved when using a relatively small number of data sets, something that is very important in the clinical setting. However, we still need to address the performance limitations of our algorithms when segmenting organs with big variability in appearance (e.g. the gallbladder or the pancreas). Phase 2 results We tested different ML methods for lesion detection. We found that using CNNs was not optimal for lesion detection on WB-MRI. This may have been due to the small fraction of lesion volume occupying the scanned space, when compared to the WB volume. It may also have been due to the complexity of intensities in background tissue and the lesion with weak boundaries causing challenges for the CNNs. We, therefore, adapted our process to become an optimal two-stage process, with an initial stage for detection of organs as per the Phase 1 technique followed by a CNN to detect lesions. Stage two could be modular with respect to the anatomical location where the suspected lesion can be found. The architecture and configuration of the used CNN could be modified to achieve optimal performance for lesion detection. Phase 3 results All radiology reads were completed between November 2019 and March 2020. Among the 193 cases allocated to Phase 3, 188 WB-MRI scans were evaluable (117 colon and 71 lung cancer) and 50/188 cases had metastases. Per-patient specificity for detection of metastases within experienced readers was 86.2% (WB-MRI + ML) and 87.7% [WB-MRI + standard deviation (SD)], [difference −1.5%, 95% confidence interval (CI) −6.4%, 3.5%; p = 0.387]. Per-patient sensitivity produced results of 66.0% (WB-MRI + ML) and 70.0% (WB-MRI + SD) (difference −4.0%, 95% CI −13.5% to 5.5%; p = 0.344). For inexperienced readers (53 reads, 15 with metastases), per-patient specificity was 76.3% in both groups with sensitivities of 73.3% (WB-MRI + ML) and 60.0% (WB-MRI + SD). Per-site specificity remained high within all sites; above 95% (experienced) or 90% (inexperienced). Per-site sensitivity was highly variable due to the low number of lesions in each site, hampering interpretation. Reading time was lowered under ML by 6.2% (95% CI −22.8% to 10.0%). Read time was primarily influenced by read round with round 2 read times reduced by 32% (95% CI 20.8% to 42.8%) overall with subsequent regression analysis showing a significant effect (p = 0.0281) by using ML in round 2 estimated as 286 seconds (or 11%) quicker. Interobserver variance for experienced readers suggests moderate agreement, Cohen’s κ = 0.64, 95% CI 0.47 to 0.81 (WB-MRI + ML) and Cohen’s κ = 0.66, 95% CI 0.47 to 0.81 (WB-MRI + SD). Conclusion Phase 1 In Phase 1, we developed and evaluated three state-of-the-art algorithms that automatically segment healthy organs and bones in WB-MRI with accuracy comparable to the one achieved manually by clinical experts, using relatively sparse training data. An algorithm based on CNNs and trained using T2w-only images as input performs favourably when compared to CFs or a MA algorithm, trained with either T2w-only images or a combination of imaging inputs (T2w + T1w + DWI). Using multimodal MRI data as input for training did not improve the segmentation performance in this work, but it is anticipated to improve the segmentation performance if more effective WB registration between the various imaging modalities can be performed. This investigation was the first step towards developing robust algorithms for the automatic detection and segmentation of benign and malignant lesions in WB-MRI scans for staging of cancer patients. Phase 2 There were many challenges in training the algorithm for lesion detection, with a very heterogeneous data set, acquired at 16 sites on different machines. The low number of metastatic lesions were scattered through many anatomic sites. Identification of lesions of relatively small volume in relation to the large volume of the WB-MRI required a two-stage approach for lesion detection, with initial organ identification followed by lesion detection. Phase 3 We undertook a robust diagnostic test accuracy study comparing WB-MRI with ML support (index text) with standard WB-MRI, without ML support (comparator test), with paired reads separated by a wash-out period. Although there was no clear statistical difference with or without ML support in terms of diagnostic test accuracy, either in experienced or inexperienced hands, we found that ML reads were likely to be a little shorter in reading time. Implications for health care Machine learning support for image interpretation is a rapidly expanding area of research. With continually increasing demand for diagnostic imaging and a radiology workforce crisis in the NHS, as well as globally, ML techniques may offer support to allow complex imaging modalities, such as WB-MRI, to be ready for translation into clinical care for the benefit of patients. Phase 1 demonstrated that with relatively sparse data, we could develop very successful organ segmentation tools in highly complex data sets and this has many potential future roles. In this study, we used this as a first step prior to lesion detection and this generic method could be applied to a wider range of disease areas, including other tumour types. We found that ML support slightly shorted the reading time, although with no improvement in diagnostic accuracy. However, we have shown that lesion detection using ML is achievable on heterogeneous and complex multiparametric WB-MRI scans. With further model training, we believe that ML support is feasible in the future, with the potential to improve translation of WB-MRI more widely. In addition, many of the techniques that have been developed in the course of the study have the potential to be applied to other areas of diagnostic imaging. Recommendations for future research Future research should investigate: Further improvement in lesion detection accuracy, with evaluation of failure cases for improved model training. Evaluate lesion detection technique on other cancers, notably breast, prostate and myeloma. Further developments in understanding the marrow composition in healthy aging and in metastatic cancer. Developing further understanding and methods to overcome registration challenges between breath-hold and non-breath-hold MRI sequences in order to improve ML tissue characterisation. Study registration This study is registered as ISRCTN23068310. Funding This award was funded by the National Institute for Health and Care Research (NIHR) Efficacy and Mechanism Evaluation (EME) programme (NIHR award ref: 13/122/01) and is published in full in Efficacy and Mechanism Evaluation; Vol. 11, No. 15. See the NIHR Funding and Awards website for further award information.

Abstract Background Magnetic resonance imaging (MRI) is recommended in patients with upper tract urothelial carcinoma (UTUC) only when computed tomography (CT) is contraindicated. However, CT does not allow distinguishing ureter wall layers, making impossible to assess muscle invasion, a factor contributing to differentiate high- from low-risk UTUCs, which require different therapeutic approaches. We investigated the feasibility of MRI assessment of UTUC muscle invasion. Methods From June 2022 to March 2023, we prospectively enrolled patients suspected of UTUC, i.e., with positive urinary tract ultrasound and/or ureteroscopy, or positive urinary cytology and/or hematuria but negative cystoscopy and bladder ultrasound at two Italian centers. They underwent CT followed by MRI (≤ 24 h apart), independently reported by two experienced radiologists, blinded from histopathology results. After imaging confirmation, they all underwent nephroureterectomy and histopathology analysis. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and area under the receiver operating characteristic curve (AUC) were calculated. Results Thirty-nine lesions were detected in 30 patients on both CT and MRI. Muscle-invasive UTUC prevalence was 81% (21/26) among patients with MRI suspicion and 8% (1/13) among those without MRI suspicion (p

Abstract Background The predictive correlation of tumor depth of invasion changes after neoadjuvant therapy, and the 8th American Joint Committee on Cancer (AJCC) ypTNM system for gastric cancer may not accurately predict patient prognosis following neoadjuvant therapy. Methods A retrospective analysis was conducted on a total of 258 patients who underwent radical surgery for gastric cancer after neoadjuvant therapy. The Newstage system was established based on tumor regression grade and pathological lymph node status. The 3-year survival rates of patients classified by the Newstage system were compared with those classified by the AJCC ypTNM system. Results In a cohort of 258 patients, the 3-year overall survival rates based on the Newstage system were: (I) 94.6%, (II) 79.3%, (III) 54.5%, and (IV) 30.2%. The Newstage system exhibited a lower Akaike information criterion value (902.57 vs. 912.03). Additionally, the area under the ROC curve (0.756 vs. 0.733) and the C-index (0.731 vs. 0.718) was higher than the AJCC ypTNM system. Furthermore, a multivariate analysis indicated that the Newstage system was an independent prognostic factor (p = 0.001). Conclusion The Newstage system exhibits superior predictive performance in estimating survival rates for neoadjuvant therapy in gastric cancer. It also functions as an independent prognostic factor.

Prompt diagnosis and surgical management of melanoma strongly impact prognosis. Considering the limited resources, emergency closures, and staffing shortages during the COVID-19 pandemic in Canada, our institution implemented a dedicated care pathway to prioritize cancer surgeries. We aim to assess whether this strategy was effective at preventing surgical wait time delays and upstaging of melanoma. We retrospectively collected data of patients aged ≥18 years with biopsy-proven primary melanoma who underwent wide local excision (WLE) ± sentinel lymph node biopsy (SLNB) between 1 March 2018–29 February 2020 (pre-pandemic) and 1 March 2020–22 March 2022 (pandemic). Patients with distant metastasis, recurrence, in situ disease, and unknown primary were excluded. Wait time from consult to surgery, tumour (T) and nodal (N) stage, and overall stage were collected. Results: We included 419 patients [pre-pandemic (n = 204) and pandemic (n = 215)]. Median wait time (days) [interquartile range] to surgery was 36 [22–48] pre-pandemic and 35 [24–49] during the pandemic (p = 0.888). There were no differences found in T stage (p = 0.060), N stage (p = 0.214), or overall melanoma stage (p = 0.192). We highlight the importance of streamlining melanoma surgery during a pandemic. As the need arises to meet surgical backlogs including benign surgery, dedicated cancer surgery should maintain a priority to not negatively affect cancer outcomes.

Background and Objectives: The new molecular classification of endometrial cancer continuously changes the management of the disease in everyday clinical practice. Recently, FIGO released a new staging system for endometrial cancer, which incorporates molecular substages and subdivides further early-stage disease. The aim of this study was to investigate the differences between the two FIGO staging systems and evaluate the prognostic precision of the new one. Materials and Methods: We retrospectively analyzed the records of patients with endometrial cancer that were fully treated in the 1st Department of Obstetrics & Gynecology, in 2012–2023. Patient characteristics, oncological outcome, and follow-up information were collected. The primary outcomes were the stage shifts and the survival data. Results: Sixty-seven (15.5%) patients had a stage shift and the majority of them concerned early-stage disease and specifically an upshift from 2009 stages IA and IB to 2023 stage IIC. Concerning survival, a better median and 5-year PFS was present in stage II disease, and when comparing the prognostic precision of the two FIGO staging systems no significant difference was present. Conclusions: The new 2023 FIGO staging system better distinguishes early-stage endometrial cancer into its prognostic groups and seems to be as precise as the old 2009 FIGO staging system.

Abstract Analyzing highly individual-specific genomic data to understand genetic interactions in cancer development is still challenging, with significant implications for the discovery of individual biomarkers as well as personalized medicine. With the rapid development of deep learning, graph neural networks (GNNs) have been employed to analyze a wide range of biomolecular networks. However, many neural networks are limited to black box models, which are only capable of making predictions, and they are often challenged to provide reliable biological and clinical insights. In this research, for sample-specific networks, a novel end-to-end hierarchical graph neural network with interpretable modules is proposed, which learns structural features at multiple scales and incorporates a soft mask layer in extracting subgraphs that contribute to classification. The perturbations caused by the input graphs' deductions are used to evaluate key gene clusters, and the samples are then grouped into classes to produce both sample- and stage-level explanations. Experiments on four gene expression datasets from The Cancer Genome Atlas (TCGA) show that the proposed model not only rivals the advanced GNN methods in cancer staging but also identifies key gene clusters that have a great impact on classification confidence, providing potential targets for personalized medicine.

The Ann Arbor system is disadvantaged in utilizing information from additional prognostic factors. In this study, we applied the Ensemble Algorithm for Clustering Cancer Data (EACCD) to create a prognostic system for lymphoma that integrates additional prognostic factors. Hodgkin and non-Hodgkin lymphoma survival data were extracted from the Surveillance, Epidemiology, and End Results Program of the National Cancer Institute and divided into the training set (131,725 cases) and the validation set (15,683 cases). Five prognostic factors were studied: Ann Arbor stage, type, site, age, and sex. EACCD was applied to the training set to produce a prognostic system, called an EACCD system, for convenience. The EACCD system stratified patients into eight prognostic groups with well-separated survival curves. These eight prognostic groups had significantly higher accuracies in survival prediction than the 24 Ann Arbor substages. A higher-risk group in the EACCD system roughly corresponds to a higher Ann Arbor substage. The proposed system shows a good performance in risk stratification and survival prediction on both the training and the validation sets. The EACCD system expands the traditional Ann Arbor staging system by leveraging additional prognostic information and is expected to advance treatment management for lymphoma patients.

Background: Squamous cell carcinoma is the most prevalent kind of tumor of the buccal mucosa. For the purpose of planning surgery and radiotherapy for tumors of the tongue, floor of the mouth, and oropharyx, imaging to locate the size and extent of the original tumor is essential. Therefore, it is beneficial to determine the radiation field, to ensure adequate tumor margin excision, and overall to improve the patient’s prognosis. Aims and Objectives: The aim of the study was to evaluate the role of computed tomography (CT) scan in diagnosis of buccal mucosal malignancies and its characteristics. Materials and Methods: Total 50 patients suspecting buccal mucosa cancer were referred to Radiology Department. They were subjected for CT scan examination on 16 slice CT scanner. CT scan evaluation was made for size and extent of primary mass lesion. The staging of disease was performed with TNM classification. Results: In our study, out of 50 patients, the fourth decade (30%) and fifth decade (26%) saw the highest incidence of buccal mucosa cancer. The majority of the patients were suffering from severe disease; 44% had T4 stage and 58% had Stage IV. Conclusion: CT plays a very important role in staging of buccal mucosa malignancy and effectively detects bone erosion (98% sensitive) and invasion to infratemporal fossa. However, during the early stages of cancer, a CT scan might not be very helpful.

Background and Objectives: Diagnostic accuracy of breast MR is higher than the routinely used sonomammography and digital mammography. There are mixed reports regarding usefulness of breast MR imaging regarding the tumor size estimation. The objective of this study was to determine which modality can assess the exact tumor dimension in comparison with corresponding tumor dimension at pathologic examinations. Materials and Methods: In a prospective study, 68 patients who came for screening diagnostic mammogram and who had breast lesions of Breast Imaging Reporting and Data System (BIRADS) category 3 and more were evaluated. All patients underwent bilateral digital mammography and targeted high-frequency sonography of the primary lesion. Those patients who were thought to possibly have breast cancer and to be candidates for surgical management were offered bilateral contrast-enhanced breast MRI. Tumor size was evaluated by digital mammography, sonomammography, contrast-enhanced mammography, and compared with histopathology reports. Results: Size of the lesions as measured by DM or SM correlated well with the size determined through breast MR (r = 0.975, P < 0.01). There was a statistically significant difference between the measured sizes of lesions between breast MR and HPE (P = 0.76). Breast MR imaging, true size was overestimated in 28 breasts with range of 0–0.6 cm and underestimated in 25 breasts with range of 0–0.9 cm. Conclusion: Breast MR imaging depicts more accurate dimensions of the tumor and its extensions than digital and sonomammography.

Abstract Purpose This retrospective study evaluated our hypothesis that high tumor budding (≥10 buds) may help determine the appropriate T category for more accurate staging of intrahepatic cholangiocarcinoma (ICC). Methods We analyzed the clinical and histopathologic data of 235 consecutive patients with histologically confirmed ICC following hepatectomy at five university hospitals in the Kansai region of Japan between January 2009 and December 2020. ICC staging was based on the Liver Cancer Study Group of Japan (LCSGJ) staging system, 6th edition. Results Patients with ICC with high budding showed significantly shorter disease‐specific survival (DSS) and disease‐free survival (DFS) than patients with low/intermediate budding. Cox proportional hazards regression analysis showed a hazard ratio of 2.2‐2.3 (P

Abstract Background Age is an independent prognostic factor for small cell lung cancer (SCLC). We aimed to construct a nomogram survival prediction for elderly SCLC patients based on the Surveillance, Epidemiology, and End Results (SEER) database. Methods A total of 2851 elderly SCLC patients from the SEER database were selected as a primary cohort, which were randomly divided into a training cohort and an internal validation cohort. Additionally, 512 patients from two institutions in China were identified as an external validation cohort. We used univariate and multivariate to determine the independent prognostic factors and establish a nomogram to predict survival. The value of the nomogram was evaluated by calibration plots, concordance index (C-index) and decision curve analysis (DCA). Results Ten independent prognostic factors were determined and integrated into the nomogram. Calibration plots showed an ideal agreement between the nomogram predicted and actual observed probability of survival. The C-indexes of the training and validation groups for cancer-specific survival (CSS) (0.757 and 0.756, respectively) based on the nomogram were higher than those of the TNM staging system (0.631 and 0.638, respectively). Improved AUC value and DCA were also obtained in comparison with the TNM model. The risk stratification system can significantly distinguish individuals with different survival risks. Conclusion We constructed and externally validated a nomogram to predict survival for elderly patients with SCLC. Our novel nomogram outperforms the traditional TNM staging system and provides more accurate prediction for the prognosis of elderly SCLC patients.

Objective We investigated the feasibility of laparoscopic restaging surgery in patients with unexpected uterine cancer. Methods This retrospective study included eight patients who underwent laparoscopic restaging surgery for Iran University uterine cancer after a prior hysterectomy or myomectomy. Results The median age of the patients and their body mass index were 55 years (range, 44–78) and 23.8 kg/m2 (range, 20.75–31.89), respectively. The median interval between the prior hysterectomy and the restaging surgery was 21 days (range, 10–35). The median operating time and time for the return of bowel activity were 325 minutes (range, 200–475) and 35 hours (range, 18–50), respectively. The median numbers of harvested pelvic and para-aortic lymph nodes were 17.5 (range, 14–29) and 20.5 (range, 7–36), respectively. In seven of the eight patients, uterine extraction was performed with vaginal or electronic morcellation. The final International Federation of Gynecology and Obstetrics stage was IA in all patients. Intraoperative and postoperative complications did not occur in any of the patients, except for the need for transfusion. Patient 4 had synchronous primary cancer (stage IA) of the endometrium and left ovary. Two of the eight patients with clear cell carcinoma received chemotherapy, and none received radiotherapy. All patients survived without disease recurrence. Conclusion Restaging surgery might be necessary for highly selective patients with unexpected uterine malignancies. This would be an alternative surgical modality for complete staging and planning tailored adjuvant treatments. However, lymphadenectomy might not be performed in patients with early uterine cancer.

Abstract Background Endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) is established as the preferred method of mediastinal lymph node (LN) staging in non-small cell lung cancer (NSCLC). Selective (targeted) LN sampling is most commonly performed however studies in early stage NSCLC and locally advanced NSCLC confirm systematic EBUS-TBNA evaluation improves accuracy of mediastinal staging. This study aims to establish the rate of detection of positron emission tomography (PET)-occult LN metastases following systematic LN staging by EBUS-TBNA, and to determine the utility of systematic mediastinal staging for accurate delineation of radiation treatment fields in patients with locally advanced NSCLC. Methods Consecutive patients undergoing EBUS-TBNA for diagnosis/staging of locally advanced NSCLC will be enrolled in this international multi-centre single arm study. Systematic mediastinal LN evaluation will be performed, with all LN exceeding 6 mm to be sampled by TBNA. Where feasible, endoscopic ultrasound staging (EUS-B) may also be performed. Results of minimally invasive staging will be compared to FDG-PET. The primary end-point is proportion of patients in whom systematic LN staging identified PET-occult NSCLC metastases. Secondary outcome measures include (i) rate of nodal upstaging, (ii) false positive rate of PET for mediastinal LN assessment, (iii) analysis of clinicoradiologic risk factors for presence of PET-occult LN metastases, (iv) impact of systematic LN staging in patients with discrepant findings on PET and EBUS-TBNA on target coverage and dose to organs at risk (OAR) in patients undergoing radiotherapy. Discussion With specificity of PET of 90%, guidelines recommend tissue confirmation of positive mediastinal LN to ensure potentially early stage patients are not erroneously denied potentially curative resection. However, while confirmation of pathologic LN is routinely sought, the exact extent of mediastinal LN involvement in NSCLC in patient with Stage III NSCLC is rarely established. Studies examining systematic LN staging in early stage NSCLC report a significant discordance between PET and EBUS-TBNA. In patients with locally advanced disease this has significant implications for radiation field planning, with risk of geographic miss in the event of PET-occult mediastinal LN metastases. The SEISMIC study will examine both diagnostic outcomes following systematic LN staging with EBUS-TBNA, and impact on radiation treatment planning. Trial registration ACTRN12617000333314, ANZCTR, Registered on 3 March 2017.

Abstract Background A scoring system focusing on the risk of muscle layer invasion by Bladder cancer (BCa) has been released, Vesical Imaging - Radiological and Data System (VI-RADS), with a growing interest in evaluating its diagnostic accuracy. Our goal was to assess the accuracy and reproducibility of the VI-RADS score for assessment of the vesical muscular layer with (multiparametric-mp) and without (biparametric-bp) a dynamic-contrast enhancement (DCE) sequence. Methods Retrospective study conducted from July 2018 to July 2020. All patients had suspicions of BCa and underwent Magnetic Resonance Imaging (MRI) before any intervention. MRI was interpreted by two radiologists with different levels of experience, and a VI-RADS score assigned in two different sessions (3 months apart) without and with DCE. After exclusions, 44 patients with 50 lesions were enrolled. The standard of reference was transurethral resection in 18 patients (40.9%) and cystectomy in 26 patients (59.1%). Results Twenty-five lesions (50%) were muscle-invasive. There was no significant difference between the two groups for gender and presence of a stalk, but mean age of NMIBCa group was significantly higher (p = 0.01). The sizes of lesions were significantly different between groups for both readers at 2.42+/− 1.58 vs. 5.70+/− 2.67 cm for reader 1 (p

In this report, we look at the relationship between prior circumcision and presenting stage of penile cancer. We performed an analysis of an international, multicenter database of 1254 penile cancer patients diagnosed from 1980 to 2019 in the United States, Europe, Brazil, and China, and analyzed the relationship between circumcision and presenting T and N stage. A total of 710 patients met the inclusion criteria and were statistically analyzed. We found that uncircumcised men with locally advanced tumors (T3–T4) had significantly higher risk of lymph node metastasis compared with circumcised men.

We compared the accuracy of preoperative transvaginal ultrasound (TVUS) versus magnetic resonance imaging (MRI) for the assessment of myometrial invasion (MI) in patients with endometrial cancer (EC), while definitive histopathological diagnosis served as a reference method.

Osseous sarcoidosis can masquerade as metastatic prostate cancer. Awareness of this is helpful in the staging of prostate cancer in a patient with sarcoidosis.

Breast cancer is commonly staged using the American Joint Committee on Cancer (AJCC) staging system. The 7th edition of the AJCC Staging Manual, was a purely anatomic staging method, which uses primary tumor size (T), nodal involvement (N), and metastasis (M) based on clinical and pathological evaluations. Advancements in tumor biology and prognostic biological markers, such as estrogen receptor (ER)/progesterone receptor (PR), HER2/neu, and Ki-67, have allowed clinicians to understand why similarly staged patients had significantly different outcomes. The most recent update to the staging system integrates molecular markers with disease extent for more optimal estimation of prognosis. This change improves the prognosis of breast cancer patients and better informs physicians in the planning of treatments. This review summarizes the changes in the AJCC Staging Manual, 8th edition and their impact on practicing radiologists in breast cancer management.

Breast cancer incidence is rising in low-income countries, but there is limited information regarding health resource allocation for its care. We assessed the cost of care during the first three years after diagnosis in a low-income country (Mozambique; n = 162 women) and compared it with a high-income country (Portugal, n = 703 women). Local currency prices were converted to 2019 international dollars (Int$). In Mozambique, the median cost was lower than in Portugal (2888 vs 18,533 Int$, respectively) and did not vary across stage or tumor subtype. These findings may help improving resource allocation for breast cancer care in Sub-Saharan Africa, despite reflecting an underfunding of treatment in this setting.

BackgroundOral squamous cell carcinoma (OSCC) is a common head and neck cancer with high morbidity and mortality. Currently, treatment decisions are guided by TNM staging, which omits important negative prognosticators such as lymphovascular invasion, perineural invasion (PNI), and histologic differentiation. We proposed nomogram models based on adverse pathological features to identify candidates suitable for treatment escalation within each risk group according to the National Comprehensive Cancer Network (NCCN) guidelines.MethodsAnonymized clinicopathologic data of OSCC patients from 5 tertiary healthcare institutions in Asia were divided into 3 risk groups according to the NCCN guidelines. Within each risk group, nomograms were built to predict overall survival based on histologic differentiation, histologic margin involvement, depth of invasion (DOI), extranodal extension, PNI, lymphovascular, and bone invasion. Nomograms were internally validated with precision–recall analysis and the Kaplan–Meier survival analysis.ResultsLow-risk patients with positive pathological nodal involvement and/or positive PNI should be considered for adjuvant radiotherapy. Intermediate-risk patients with gross bone invasion may benefit from concurrent chemotherapy. High-risk patients with positive margins, high DOI, and a high composite score of histologic differentiation, PNI, and the American Joint Committee on Cancer (AJCC) 8th edition T staging should be considered for treatment escalation to experimental therapies in clinical trials.ConclusionNomograms built based on prognostic adverse pathological features can be used within each NCCN risk group to fine-tune treatment decisions for OSCC patients.

Objectives The aims of this study were to assess the feasibility of single-port laparoscopic surgical staging (SPLS) in early ovarian cancer and to compare the surgical outcomes of SPLS with those of staging laparotomy. Methods Between January 2014 and December 2018, 40 patients underwent SPLS and 41 patients underwent staging laparotomy at Yonsei Cancer Center. The patients were diagnosed with International Federation of Gynecology and Obstetrics (FIGO) stage I ovarian cancer. Variables such as patient age, body mass index (BMI), tumor size, FIGO stage, and perioperative surgical outcomes and survival outcomes of SPLS and laparotomy were compared. Results The total operation time was similar between the 2 groups (SPLS: 201.4 vs. laparotomy: 203.0 minutes, P=0.806). The median tumor diameters in the SPLS and laparotomy groups were 11.0 (2.5–28 cm) and 15.4 (6–40 cm), respectively (P=0.001). The SPLS group had lower tumor spillage rate (5.0% vs. 19.5%, P=0.047), less intraoperative blood loss (102.0 vs. 371.5 mL, P

Objectives: Prostate cancer incidence is increasing in the Middle East (ME); however, the data of stage at the diagnosis and treatment outcomes are lacking. In developed countries, the incidence of de novo metastatic prostate cancer ranges between 4% and 14%. We hypothesized that the rates of presentation with advanced disease are significantly higher in the ME based on clinical observation. This study aims to examine the stage at the presentation of patients with prostate cancer at a large tertiary center in the ME. Methods: After Institutional Review Board approval, we identified the patients diagnosed with prostate adenocarcinoma and presented to a tertiary care center between January 2010 and July 2015. Clinical, demographic, and pathological characteristics were abstracted. Patients with advanced disease were stratified according to tumor volume based on definitions from practice changing clinical trials. Descriptive and Kaplan–Meier survival analysis was used. Results: A total of 559 patients were identified, with a median age at the diagnosis of 65 years and an age range of 39–94 years. Median prostate-specific antigen (PSA) at the presentation was 10 ng/ml, and almost a quarter of the men (23%) presented with metastatic disease. The most common site of metastasis was the bone (34/89, 38%). High-volume metastasis was present in 30.3%, 9%, and 5.2% of the cohort based on STAMPEDE, CHAARTED, and LATITUDE trial criteria, respectively. Conclusion: This is the first report showing the high proportion of men from ME presenting with de novo metastasis. This could be due to many factors, including the highly variable access to specialist multidisciplinary management, lack of awareness, and lack of PSA screening in the region. There is a clear need to raise the awareness about prostate cancer screening and early detection and to address the rising burden of advanced prostate cancer affecting men in the ME region.

Breast cancer is the most common cancer in women. Staging plays an important role in treatment planning. In the staging system published by AJCC in 2002, micrometastasis, the total number of metastatic lymph nodes, internal mammarian and supraclavicular lymph node metastases, and level III (apical) lymph node metastases were incorporated to the staging system. These modifications are likely to cause upstaging and changes in treatment plans, precisely in patients previously considered as stage II. This study aimed to investigate the outcomes of update in staging system in terms of mortality and morbidity. The records of 150 patients who underwent surgery for stage II breast cancer were retrospectively reviewed. The patients who were accepted as stage II according to the previous staging systems and planned to be treated accordingly were re-evaluated according to the newly updated staging system. Patients with and without stage change were compared in terms of mortality, local and systemic recurrence, and disease-free survival. We found that 41 (27.3%) of the patients who were accepted as stage II according to the previous staging systems had been upstaged in the new staging system. The relationship between stage migration and tumor diameter was statistically significant. Thirty-one patients (20.7%) had recurrence during follow-up. We found that most of the patients with recurrence (77.4%) had stage migration. There was a statistically significant relationship between apical lymph node involvement and recurrence. Updates in staging system of breast cancer cause stage migration in a significant proportion of patients with stage II breast cancer and are likely to lead to a change in treatment plans in some patients. Stage migration also has an impact on prognosis. In fact, recurrence is more common in patients with stage migration. [Med-Science 2020; 9(3.000): 579-84]

Objective: Understanding variance in global breast cancer incidence rates may help identify risk factors and could lead to increased efforts for prevention. In our previous study on breast cancer in Mongolia from 1998-2005, we reported that the incidence of breast cancer, though on the rise, remains lower in Mongolia than in adjacent Asian countries. Through the addition of almost a decade of data (2006-2014), we provide a study update and describe trends in breast cancer incidence and staging distributions within Mongolia, with further analyses examining women in urban and rural regions. Methods: Age-standardized breast cancer incidence and staging distributions were obtained from the Mongolian National Oncology Center and were used to describe trends in cancer over time. Results: Our findings indicate that the overall incidence of breast cancer has continued to increase in Mongolia, with rural women still experiencing lower risk than their urban counterparts. Given similarities in cancer staging, the observed geographic variation does not appear to result from differences in cancer stage at diagnosis over time or by region. Conclusions: Considering the variation in rural and urban populations, data from this study could be used to better understand the influence of westernization on cancer risk in Asian countries and beyond. Further research on demographic shifts in breast cancer incidence within Mongolia may elucidate novel risk factors explaining variations active among other populations.

Abstract Background Seminoma accounts for 30–50% of testicular germ cell tumors (TGCT)—the most common solid malignancy in men aged 15–35 years. The American Joint Committee on Cancer (AJCC) 8th edition (2018) created the subclassifications pT1a (tumor size 4 cm are considered features associated with a higher recurrence risk, but not formally used for staging. The authors propose further understanding the subclassification’s potential impact in clinical practice, by summarizing current evidence and reviewing clinical cases in their institutions. Methods All consecutive cases of seminoma stage I, pT1 treated in two institutions between January 2005 and December 2016 were included. Clinical data were retrieved, and variables were analyzed using SPSS. Relevant literature on the topic was reviewed. Results Seminoma pT1 was identified in 58 patients. By using newly AJCC criteria, 29 (50%) would have been staged as pT1a and 29 (50%) pT1b. Median age at diagnosis was similar (33 in pT1a vs 32 in pT1b). Median follow-up time 5.8 years. Almost half (45%) of pT1b patients had a tumor size

Abstract Numerous imaging modalities may be used for the staging of women with advanced breast cancer. Although bone scintigraphy and multiplanar-CT are the most frequently used tests, others including PET, MRI and hybrid scans are also utilised, with no specific recommendations of which test should be preferentially used. We review the evidence behind the imaging modalities that characterise metastases in breast cancer and to update the evidence on comparative imaging accuracy.

Squamous cell carcinoma of the oral cavity and oropharynx have been used synonymously and interchangeably in the world literature in the context of head and neck cancers. As the 21st century progresses, divergence between the two have become more evident, particularly due to evidence related to human papillomavirus-associated oropharyngeal squamous cell carcinoma. As such, the American Joint Committee on Cancer recently published the 8th edition Cancer Staging Manual, serving as a continued global resource to clinicians and researchers. Through changes in staging related to T and N clinical and pathologic classifications, the new system is expected to influence current management guidelines of these cancers that have distinct anatomic and etiopathogenic characteristics. This article aims to review such impactful changes in a time of critical transition of the staging of head and neck cancer and how these changes may affect clinicians and researchers worldwide.

Abstract Purpose In this study, we aimed to investigate the viability of utilizing CytoSorter® system to detect circulating tumor cells (CTCs) and to evaluate the diagnostic value of CTCs in breast cancer (BC). Methods A total of 366 females patients suspected of having BC and 30 healthy female volunteers were enrolled in this study. CTCs were enriched by CytoSorter®, a microfluidic‐based CTCs capturing platform. CTC detection was performed before operation or biopsy. Based on the biopsy results, patients were divided into two groups, namely patients with BC and patients with benign breast diseases (BBD). Patients with BBD and healthy volunteers were serving as controls. The correlation between CTC enumeration and patients' clinicopathological characteristics was evaluated. The receiver operating characteristic (ROC) curve was plotted to assess the diagnostic potency of CytoSorter® system in BC. Results Based on the biopsy results, 130 BC patients at different cancer stages and 236 patients with BBD were enrolled in the study. Seven subjects were dropped out from the study. CTCs were detected in 109 of 128 BC patients, in one of 29 healthy volunteers, and in 37 of 232 patients with BBD. Maximum CTC counts detected in BC patients, healthy volunteers, and patients with BBD were 8, 1, and 4, respectively. Statistical analysis showed CTCs could be used to distinguish BC patients from healthy volunteers and patients with BBD (P

Cancer staging provides a common language that is used to describe the severity of an individual's cancer, which plays a critical role in optimizing cancer treatment. Recursive partitioning analysis (RPA) is the most widely accepted method for cancer staging. Despite its widespread use, to date, only limited tools have been developed to implement the RPA algorithm for cancer staging. Moreover, most of the available tools can be accessed only from command lines and also lack visualization, making them difficult for clinical investigators without programing skills to use. Therefore, we developed a web server called autoRPA that is dedicated to supporting the construction of prognostic staging models and performance comparisons among different staging models. Based on the RPA algorithm and log-rank test statistics, autoRPA can establish a decision-making tree from survival data and provide clinicians an intuitive method to further prune the decision tree. Moreover, autoRPA can evaluate the contribution of each submitted covariate that is involved in the grouping process and help identify factors that significantly contribute to cancer staging. Four indicators, including hazard consistency, hazard discrimination, percentage of variation explained, and sample size balance, are introduced to validate the performance of the designed staging models. In addition, autoRPA can also be used to compare the performance of different prognostic staging models using a standard bootstrap evaluation method. The web server of autoRPA is freely available at http://rpa.renlab.org.

Prostate cancer is the most common noncutaneous malignancy in American men. Its lymphatic drainage is very well established throughout literature. We report the case of a 72-year-old Caucasian male with elevated serum prostate-specific antigen and biopsy-confirmed high-risk prostate cancer who underwent multiparametric magnetic resonance imaging (MRI) for staging and treatment planning. The imaging revealed suspicious lymph nodes in the left ischiorectal and right obturator fossae that were biopsy confirmed as metastatic prostate adenocarcinoma. Herein, we present the divergence from the well-established lymphatic drainage of prostate cancer and the role of MRI in detecting this prostate cancer site of spread.

BackgroundThis study investigates the effects of COVID-19 on the breast cancer stage and the volume of breast cancer surgery in a specialized breast institute.MethodsData of 332 patients who were diagnosed and treated for breast cancer between December 2019 and November 2020 were evaluated retrospectively according to periods of pandemic.ResultsA significant decrease in the number of operations, especially upfront surgeries rather than surgeries after neoadjuvant chemotherapy, was detected in the early period of the COVID-19 pandemic. It was found that patients with complaints were mostly admitted during this period (p = 0.024). No statistical significance was found for age, sex, side of the tumor, type of tumor, surgery to breast, and axilla. Following the early period of the pandemic, it was observed that patients with mostly luminal, early-stage, and less axillary nodal involvement (p < 0.05) were admitted, and as a result, it was founded that upfront surgeries increased, although no change in TNM staging was observed. However, it did affect the decision of initial treatment. Thus, the number of upfront surgeries was significantly higher than the NCT group (p = 0.027) following the early period.ConclusionSurgical volume is significantly affected in the early period of the COVID-19 pandemic. To overcome overload due to delayed surgeries related to pandemics, some hospitals should be spared for oncological treatments. Following the early period, mostly luminal type, early-stage patients were admitted, probably because of increased self-awareness and short wave duration, but the breast cancer stage was not affected.

Background: Nasopharyngeal carcinoma (NPC) is a common tumor in China. Accurate stages of NPC are crucial for treatment. We therefore aim to develop radiomics models for discriminating early-stage (I–II) and advanced-stage (III–IVa) NPC based on MR images. Methods: 329 NPC patients were enrolled and randomly divided into a training cohort (n = 229) and a validation cohort (n = 100). Features were extracted based on axial contrast-enhanced T1-weighted images (CE-T1WI), T1WI, and T2-weighted images (T2WI). Least absolute shrinkage and selection operator (LASSO) was used to build radiomics signatures. Seven radiomics models were constructed with logistic regression. The AUC value was used to assess classification performance. The DeLong test was used to compare the AUCs of different radiomics models and visual assessment. Results: Models A, B, C, D, E, F, and G were constructed with 13, 9, 7, 9, 10, 7, and 6 features, respectively. All radiomics models showed better classification performance than that of visual assessment. Model A (CE-T1WI + T1WI + T2WI) showed the best classification performance (AUC: 0.847) in the training cohort. CE-T1WI showed the greatest significance for staging NPC. Conclusion: Radiomics models can effectively distinguish early-stage from advanced-stage NPC patients, and Model A (CE-T1WI + T1WI + T2WI) showed the best classification performance.

Background and Objectives: The COVID-19 pandemic impacted health systems worldwide, particularly cancer care. Because the actual implications of these changes on gynecological oncology healthcare are still unclear, we aim to evaluate the impact of this pandemic on the diagnosis and management of gynecological cancer. Materials and Methods: This is a single-center retrospective observational study, including patients diagnosed with gynecological malignancies between January 2019 and December 2021. Patients were included into three groups based on the timing of cancer diagnosis: pre-pandemic (2019), pandemic with high restrictions (2020) and pandemic recovery (2021). Results: Overall, 234 patients were diagnosed with gynecological cancer during the period of study. A decrease in the number of newly diagnosed cervical cancers and other rare tumors (leiomyosarcoma, invasive hydatidiform mole) was apparent in 2020. Some aggressive histological types of endometrial and ovarian cancer were more commonly diagnosed in the pandemic recovery group (p < 0.05), although no differences were demonstrated concerning tumor staging in all gynecological cancers. The median time between the first multidisciplinary team meeting and the treatment initiation was higher after the COVID-19 pandemic in endometrial cancer (23.0 vs. 34.0 vs. 36.0 days, p < 0.05). Patients with ovarian cancer were more frequently proposed for neoadjuvant therapy in 2020 compared to the other periods (33.3% vs. 55.0% vs. 10.0% p < 0.05). A significant reduction in the laparoscopic approach was observed during 2020 in endometrial cancer (32.1% vs. 14.3% vs. 36.4%, p < 0.05). No significant differences were registered regarding median hospitalization days or intra- and post-operative complications between these periods. Conclusions: The COVID-19 pandemic had a significant impact on the diagnosis and management of most gynecological malignancies, namely, on time to first treatment, chosen oncological therapies and surgical approaches. These results suggest important clinical and healthcare implications that should be addressed in future prospective studies.

BackgroundClinical staging is essential for clinical decisions but remains imprecise. We purposed to construct a novel survival prediction model for improving clinical staging system (cTNM) for patients with esophageal adenocarcioma (EAC).MethodsA total of 4180 patients diagnosed with EAC were extracted from the Surveillance, Epidemiology, and End Results (SEER) database and included as the training cohort. Significant prognostic variables were identified for nomogram model development using multivariable Cox regression. The model was validated internally by bootstrap resampling, and then subjected to external validation with a separate cohort of 886 patients from 2 institutions in China. The prognostic performance was measured by concordance index (C-index), Akaike information criterion (AIC) and calibration plots. Different risk groups were stratified by the nomogram scores.ResultsA total of six variables were determined related with survival and entered into the nomogram construction. The calibration curves showed satisfied agreement between nomogram-predicted survival and actual observed survival for 1-, 3-, and 5-year overall survival. By calculating the AIC and C-index values, our nomogram presented superior discriminative and risk-stratifying ability than current TNM staging system. Significant distinctions in survival curves were observed between different risk subgroups stratified by nomogram scores.ConclusionThe established and validated nomogram presented better risk-stratifying ability than current clinical staging system, and could provide a convenient and reliable tool for individual survival prediction and treatment strategy making.

Pengpeng Wang,1,2 Kecheng Zhang,2 Hongqing Xi,2 Wenquan Liang,2 Tianyu Xie,1,2 Yunhe Gao,2 Bo Wei,2 Lin Chen1,2 1School of Medicine, Nankai University, Tianjin 300071, People’s Republic of China; 2Department of General Surgery, Chinese People’s Liberation Army General Hospital, Beijing 100853, People’s Republic of ChinaCorrespondence: Bo Wei; Lin ChenDepartment of General Surgery, Chinese People’s Liberation Army General Hospital, Fuxing Road 28#, Haidian District, Beijing 100853, People’s Republic of ChinaEmail weibo@vip.163.com; chenlin@301hospital.com.cnPurpose: To compare the lymph node yields of lymph node packet submission (PS), packet submission after isolation by surgeons (PSI), and en bloc lymph node submission (EBS) after gastrectomy.Methods: We conducted a prospective study including 118 gastric cancer patients who underwent gastrectomy between June 2016 and August 2016. We also retrospectively reviewed 607 patients who underwent gastrectomy from May 2015 to May 2016. Following gastrectomy, lymph node specimens were either submitted en bloc (EBS group), divided into packets with accompanying adipose tissue according to the lymph node stations (PS group), or isolated individually based on the surgeon’s visualization and palpation before submission to the Pathology Department (PSI group).Results: The average lymph node yield was significantly higher in the PSI compared with the PS group in the prospective study (46.5±19.4 vs 31.8±11.1), and significantly higher in the PS compared with the EBS group in the retrospective study (31.5±12.6 vs 23.9±8.9) (both P

Background: Growth arrest-specific 2 (GAS2) gene is implicated in a variety of cellular functions such as cell cycle, apoptosis, and proliferation, and may be potentially involved in cancer progression. However, whether GAS2 is associated with colorectal cancer (CRC) progression and prognosis remains to be uncovered. Thus, this study investigated the association of GAS2 expression in tumor with CRC progression and prognosis. Methods: In the case-control study, surgical tumor and adjacent normal tissues from 40 patients with CRC were collected at Cancer Institute of Imam Khomeini Hospital in Tehran, Iran, and relative expression level of GAS2 in the tissues was assayed using quantitative real-time polymerase chain reaction method. The correlation of tumor GAS2 expression with the clinicopathological features and overall survival rate of patients was determined. Findings: The relative expression level of GAS2 in tumor tissues was significantly elevated compared to the adjacent normal tissues [1.96 (1.17-3.40) vs. 1.10 (1.00-1.31), P < 0.001]. Moreover, the expression levels of GAS2 in tumor tissues were significantly associated with clinicopathological features of cancer including tumor stage in Tumor spread, Lymph node spread, and Metastasis (TNM) staging (P = 0.010), grade (P = 0.010), size (P = 0.030), and lymphatic (P = 0.030) and vascular invasion (P = 0.040) as well as the decreased overall survival (P = 0.040). Conclusion: Elevated expression of GAS2 in CRC is associated with cancer progression indices and poor prognosis; hence it may be served as a prognostic biomarker in CRC.

ObjectivesTo construct a nomogram model that combines clinical characteristics and radiomics signatures to preoperatively discriminate pancreatic ductal adenocarcinoma (PDAC) in stage I-II and III-IV and predict overall survival.MethodsA total of 135 patients with histopathologically confirmed PDAC who underwent contrast-enhanced CT were included. A total of 384 radiomics features were extracted from arterial phase (AP) or portal venous phase (PVP) images. Four steps were used for feature selection, and multivariable logistic regression analysis were used to build radiomics signatures and combined nomogram model. Performance of the proposed model was assessed by using receiver operating characteristic (ROC) curves, calibration curves and decision curve analysis (DCA). Kaplan-Meier analysis was applied to analyze overall survival in the stage I-II and III-IV PDAC groups.ResultsThe AP+PVP radiomics signature showed the best performance among the three radiomics signatures [training cohort: area under the curve (AUC) = 0.919; validation cohort: AUC = 0.831]. The combined nomogram model integrating AP+PVP radiomics signature with clinical characteristics (tumor location, carcinoembryonic antigen level, and tumor maximum diameter) demonstrated the best discrimination performance (training cohort: AUC = 0.940; validation cohort: AUC = 0.912). Calibration curves and DCA verified the clinical usefulness of the combined nomogram model. Kaplan-Meier analysis showed that overall survival of patients in the predicted stage I-II PDAC group was longer than patients in stage III-IV PDAC group (p

Introduction At the time of diagnosis, treatment strategies for cancer are largely based upon clinical staging. However, discrepancy between clinical and pathological staging has been reported. Objective To assess the rate of staging discrepancy in Laryngeal and Hypopharyngeal Squamous Cell Carcinoma (LHSCC), the potential influence of higher interval of time from diagnosis to primary surgical treatment, and whether this has any impact on survival outcomes. Methods Retrospective study of patients with LHSCC proposed for primary surgical treatment. Results The study population included 125 Caucasian patients with LHSCC. The level of agreement between clinical and pathological tumor staging was moderate (Cohen’s Kappa: 0.400; p

Assessing lymph node (LN) status during tumor resection is fundamental for the staging of colorectal cancer. Current guidelines require a minimum of 12 LNs to be harvested during resection and ultra-staging regional lymph nodes by sentinel lymph node (SLN) assessment is being extensively investigated. The current study presents novel near-infrared (NIR) fluorescent dyes for simultaneous pan lymph node (PanLN; regional) and SLN mapping. PanLN-Forte was intravenously injected in mice and assessed for accumulation in regional LNs. SLN800 was injected intradermally in mice, after which the collection and retention of fluorescence in SLNs were measured using indocyanine green (ICG) and its precursor, SLN700, as references. LNs in the cervical, inguinal, jejunal, iliac, and thoracic basins could clearly be distinguished after a low dose intravenous injection of PanLN-Forte. Background fluorescence was significantly lower compared to the parent compound ZW800-3A (p < 0.001). SLN700 and SLN800 specifically targeted SLNs with fluorescence being retained over 40-fold longer than the current clinically used agent ICG. Using SLN700 and SLN800, absolute fluorescence in SLN was at least 10 times higher than ICG in second-tier nodes, even at 1 hour post-injection. Histologically, the fluorescent signal localized in the LN medulla (PanLN-Forte) or sinus entry (SLN700/SLN800). PanLN-Forte and SLN800 appear to be optimal for real-time NIR fluorescence imaging of regional and SLNs, respectively.

Rationale, aims, and objectives: Different health agencies in Canada including Cancer Care Ontario (CCO) have developed guidelines for the baseline staging of newly diagnosed breast cancer patients but adherence to them is unknown. We sought to investigate adherence to CCO staging guidelines in a single cancer center in addition to the factors that influence this adherence. Method: A retrospective chart review was conducted on 212 newly diagnosed breast cancer patients between 2015 and 2017. Baseline patient demographic and disease characteristics as well as radiologic staging studies and subsequent treatments were recorded. The group of patients in whom the guidelines were observed was compared to the group of patients in whom the guidelines were not followed. Results: Staging guidelines were not followed in 46.7% of the patients in the cohort (99 of 212 patients). In most cases, deviations from the guidelines consisted of performing more than the recommended baseline screening, most commonly in the form of a computerized tomography (CT) scan or a bone scan and chest x-ray (CXR)/ ultrasound (US) of the liver. Less commonly, a recommended staging evaluation was omitted or the suggested timing of the staging procedure (i.e., pre-operatively versus post-operatively) was not followed. Higher stage and grade of the disease and subsequent chemotherapy administration were associated with higher guideline non-adherence. Conclusions: Low adherence to staging guidelines for newly diagnosed breast cancer according to CCO is shown in a community cancer center. Incorporation of arising prognostic factors to staging procedure determination may increase acceptance and adherence to guidelines in the future.

Background: Hospitals lack intuitive methods to monitor their accuracy of clinical cancer staging, which is critical to treatment planning, prognosis, refinements, and registering quality data. Methods: We introduce a tabulation framework to compare clinical staging with the reference-standard pathological staging, and quantify systematic errors. As an example, we analyzed 9,644 2016 U.S. National Cancer Institute SEER surgically-treated non-small cell lung cancer (NSCLC) cases, and computed concordance with different denominators to compare with incompatible past results. Results: The concordance for clinical versus pathological lymph node N-stage is very good, 83.4 ± 1.0%, but the tumor length-location T-stage is only 58.1 ± 0.9%. There are intuitive insights to the causes of discordance. Approximately 29% of the cases are pathological T-stage greater than clinical T-stage, and 12% lower than the clinical T-stage, which is due partly to the fact that surgically-treated NSCLC are typically lower-stage cancer cases, which results in a bounded higher probability for pathological upstaging. Individual T-stage categories Tis, T1a, T1b, T2a, T2b, T3, T4 invariant percent-concordances are 85.2 ± 9.7 + 10.3%; 72.7 ± 1.6 + 11.3%; 46.6 ± 1.8 + 10.9%; 54.6 ± 1.6 – 20.5%; 41.6 ± 3.3 – 0.1%; 54.7 ± 2.8 – 24.1%; 55.2 ± 4.7 + 2.6%, respectively. Each percent-concordance is referenced to an averaged number of pathological and clinical cases. The first error number quantifies statistical fluctuations; the second quantifies clinical and pathological staging biases. Lastly, comparison of over and under staging versus clinical characteristics provides further insights. Conclusions: Clinical NSCLC staging accuracy and concordance with pathological values can improve. As a first step, the framework enables standardizing comparing staging results and detecting possible problem areas. Cancer hospitals and registries can implement the efficient framework to monitor staging accuracy.

Céline Montavon Sartorius,1 Uzma Mirza,1 Andreas Schötzau,2 Gillian Mackay,1 Daniel Fink,3 Neville F Hacker,4 Viola Heinzelmann-Schwarz1,2 1Department of Gynecology and Gynecological Oncology, Hospital for Women, University Hospital Basel, Basel, Switzerland; 2Ovarian Cancer Research, Department of Biomedicine, University Hospital Basel, University of Basel, Basel, Switzerland; 3Department of Gynecology, University Hospital Zurich, Zürich, Switzerland; 4Gynecological Cancer Centre, Royal Hospital for Women, School of Women’s and Children’s Health, University of New South Wales, Randwick, NSW, Australia Purpose: The stage of disease is one of the strongest prognostic factors in epithelial ovarian cancer. The International Federation of Gynecology and Obstetrics (FIGO) classification was revised in 2013; stage IC was subdivided into IC1 (intraoperative surgical spill), IC2 (capsule rupture before surgery or tumor on surface), and IC3 (positive peritoneal washing or ascites). Our aim was to compare the outcome of patients in the new FIGO stage I subgroups, as this might influence adjuvant therapy decisions.Patients and methods: Patient databases of three gynecological oncology centers were retrospectively analyzed. Patients with FIGO stage I ovarian cancers were restaged according to the revised classification, based on operative and pathological reports, and determined patient outcomes.Results: We analyzed 128 patients with ovarian cancers. In FIGO IA, we found 11.3% recurrences and 4.2% deaths. In FIGO IC, 21.8% of the patients recurred and 7.3% died. There was a trend toward a shorter time to recurrence when comparing IA to IC (P=0.076). Within all new subgroups of FIGO IC, there was no difference in time to recurrence (P=0.59). There was also no significant difference in survival when FIGO IA was compared to FIGO IC in comparison with the new individual classifications (IA to IC, IA to IC1, 2, or 3; P=0.60, P=0.15, P=0.61, P=0.66, respectively) or within the different subgroups (P=0.56). Platinum-based chemotherapy was given to the majority (82.6%, n=38/46) of the FIGO IC patients compared to 30.9% in FIGO IA (n=17/55). There was no significant difference within the new subgroups of FIGO IC (P=0.88).Conclusion: In our retrospective analysis, the new FIGO staging of IC ovarian cancers did not predict prognosis, but the use of adjuvant chemotherapy in 82.6% of the stage IC patients may have biased the outcome. Keywords: ovarian neoplasm, cancer staging, survival, recurrence

Background: Hodgkin lymphoma is usually detected in primary care with early signs and symptoms, and is highly treatable with standardised chemotherapy. However, late presentation is associated with poorer outcomes. Aim: To investigate the relationship between markers of advanced disease, emergency admission, and survival following a diagnosis of classical Hodgkin lymphoma (CHL). Design & setting: The study was set within a sociodemographically representative UK population-based patient cohort of ~4 million, within which all patients were tracked through their care pathways, and linked to national data obtained from Hospital Episode Statistics (HES) and deaths. Method: All 971 patients with CHL newly diagnosed between 1 September 2004–31 August 2015 were followed until 18th December 2018. Results: The median diagnostic age was 41.5 years (range 0–96 years), 55.2% of the patients were male, 31.2% had stage IV disease, 43.0% had a moderate–high or high risk prognostic score, and 18.7% were admitted via the emergency route prior to diagnosis. The relationship between age and emergency admission was U-shaped: more likely in patients aged

Multiparametric magnetic resonance is assuming an increasingly important role in the diagnosis, initial assessment and monitoring of patients with prostate cancer. This paper offers a more complex insight into the application of magnetic resonance imaging with prostate cancer, with a current literature overview. The focus is on the problem of initial prostate cancer evaluation which strongly affects further decision-making and therapeutic interventions. Clinical suggestions based on the current guidelines are also offered.

Abstract Background Indications for restaging transurethral resection of the bladder tumor (reTURBT) in patients with non-muscle-invasive bladder cancer (NMIBC) remain controversial. This study was aimed at evaluation of clinical value and safety of reTURBT in different clinical indications. Methods This is a retrospective analysis of consecutive 141 patients who underwent TURBT followed by reTURBT in years 2011–2015 in a single department. Pathological results and surgical complications were analyzed in the whole study cohort and stratified by clinical stage (Ta, T1, Tx (no muscle in the specimen)) and grade (low-grade (LG), high-grade (HG)) of bladder cancer diagnosed at primary TURBT. Results Full data was available for 132 patients. Residual disease was found in 53 patients (40.2%) with highest rate for Ta-HG cases (57.1%) followed by T1-HG (51.4%), Tx-HG (45.2%), T1-LG (32.1%), and Tx-LG (25.8%). In the multivariate analysis, high grade (p = 0.02) was the only independent predictor of residual disease. Upstaging to muscle-invasive bladder cancer was noticed in 9 patients (6.8%). The rate of grade ≥ 2 Clavien-Dindo complications (1.5 vs. 5.3%) did not differ significantly between TURBT and reTURBT cases. Conclusions ReTURBT is a safe procedure that remains crucial for therapeutic and staging purposes in patients with T1, Tx, or high-grade bladder cancer found in the primary resection.