The Impact of Surgery on Circulating Malignant Tumour Cells in Oral Squamous Cell Carcinoma

IMPORTANCE

The extent to which surgical management of oral squamous cell carcinoma (OSCC) disseminates cancer is currently unknown.

OBJECTIVE

To determine changes in numbers of malignant cells released into systemic circulation immediately following tumour removal and over the first seven post-operative days.

DESIGN

An observational study from March 2019 to February 2021.

SETTING

This study was undertaken at Queen Mary University Hospital, Hong Kong.

PARTICIPANTS

Patients with biopsy-proven oral SCC were considered for eligibility. Patients under 18 years of age, pregnant or lactating women and those unable to understand the study details or unable to sign the consent form were excluded. Twenty-two patients were enrolled (12 male and 10 female) with mean age of 65.5 years.

INTERVENTION

Primary tumour management was performed in accord with multi-disciplinary team agreement. Anaesthesia and post-operative care were unaltered and provided in accord with accepted clinical practice.

MAIN OUTCOMES AND MEASURES

Three types of malignant cells detected in peripheral blood samples were enumerated and sub-typed based on the presence of chromosomal aneuploidy and immunohistochemical characteristics. To test the hypothesis that malignant cells are released by surgery, the numbers of single circulating tumour cells (CTCs), circulating tumour microemboli (CTM) and circulating endothelial cells (CTECs) were recorded pre-operatively, upon tumour removal and the second and seventh post-operative days.

RESULTS

Of a potential 88 data collection points, specimens were not obtainable in 12 instances. Tumour removal resulted in a statistically significant increase in CTCs and a non-statistically significant rise in CTMs. CTCs, CTMs and CTECs were detected in the majority of patients up to the seventh post-operative day. Individual patients demonstrated striking increases in post-operative CTCs and CTECs numbers.

CONCLUSIONS/RELEVANCE

Surgical management of OSCC has a significant impact on the systemic distribution of cancer cells. Malignant cells persisted post-operatively in a manner independent of recognised staging methods suggesting differences in tumour biology between individuals. Further investigation is warranted to determine whether circulating malignant cell enumeration can be used to refine risk stratification for patients with OSCC.

A "Seed-and-Soil" Radiomics Model Predicts Brain Metastasis Development in Lung Cancer: Implications for Risk-Stratified Prophylactic Cranial Irradiation

Introduction: Brain is a major site of metastasis for lung cancer, and effective therapy for developed brain metastasis (BM) is limited. Prophylactic cranial irradiation (PCI) has been shown to reduce BM rate and improve survival in small cell lung cancer, but this result was not replicated in unselected non-small cell lung cancer (NSCLC) and had the risk of inducing neurocognitive dysfunctions. We aimed to develop a radiomics BM prediction model for BM risk stratification in NSCLC patients. Methods: 256 NSCLC patients with no BM at baseline brain magnetic resonance imaging (MRI) were selected; 128 patients developed BM within three years after diagnosis and 128 remained BM-free. For radiomics analysis, both the BM and non-BM groups were randomly distributed into training and testing datasets at an 70%:30% ratio. Both brain MRI (representing the soil) and chest computed tomography (CT, representing the seed) radiomic features were extracted to develop the BM prediction models. We first developed the radiomic models using the training dataset (89 non-BM and 90 BM cases) and subsequently validated the models in the testing dataset (39 non-BM and 38 BM cases). A radiomics BM score (RadBM score) was generated, and BM-free survival were compared between RadBM score-high and RadBM score-low groups. Results: The radiomics model developed from baseline brain MRI features alone can predict BM development in NSCLC patients. A fusion model integrating brain MRI features with primary tumor CT features (seed-and-soil model) provided synergetic effect and was more efficient in predicting BM (areas under the receiver operating characteristic curve 0.84 (95% confidence interval: 0.80−0.89) and 0.80 (95% confidence interval: 0.71−0.88) in the training and testing datasets, respectively). BM-free survival was significantly shorter in the RadBM score-high group versus the RadBM score-low group (Log-rank, p < 0.001). Hazard ratios for BM were 1.056 (95% confidence interval: 1.044−1.068) per 0.01 increment in RadBM score. Cumulative BM rates at three years were 75.8% and 24.2% for the RadBM score-high and RadBM score-low groups, respectively. Only 1.2% (7/565) of the BM lesions were located within the hippocampal avoidance region. Conclusion: The results demonstrated that intrinsic features of a non-metastatic brain exert a significant impact on BM development, which is first-in-class in metastasis prediction studies. A radiomics BM prediction model utilizing both primary tumor and pre-metastatic brain features might provide a useful tool for individualized PCI administration in NSCLC patients more prone to develop BM.

Decoupling convolution network for characterizing the metastatic lymph nodes of breast cancer patients

PURPOSE

The dual-energy computed tomography (DECT) technique is an emerging imaging tool that can better characterize material features and has the potential to be a noninvasive means of predicting lymph node metastasis. The purpose of this study was to establish a DECT-specified quantitative approach based on a neural network to characterize the sentinel lymph node (SLN).

METHODS

With IRB approval, we retrospectively collected a total of 229 patients (100/229 metastasis) with biopsy proven breast cancer in this study. The chest and axillary spectral CT examinations were performed prior to the axillary lymph node (ALN) surgery. A decoupling convolution network with 11 ROIs from sequential keV (40 to 140 keV with 10 keV increment) was proposed to explicitly extract the spectral and spatial features in a DECT to predict the lymph node status. Focal loss was introduced as the loss function. The metric of the slope of the spectral Hounsfield unit curve measured at the venous phase was used as the baseline approach in comparison to our approach. In additional, a logistic model with radiomic features was also compared to our approach. The area under ROC curve (AUC) was used as the figure of merit to evaluate the classification performance.

RESULTS

By introducing spectral convolution and focal loss, AUC on test set could be improved by 0.15 and 0.01 separately. Compared to the slope of the spectral curve with the average AUC of 0.611 and radiomic model with AUC of 0.825, the proposed approach demonstrates a considerably better performance, with test set AUC value of 0.837, by using decoupling spectral and spatial convolution together with focal loss function.

CONCLUSIONS

We presented a new decoupling neural network based quantification method for DECT analysis, which might have potential as a noninvasive tool to predict metastasis lymph node status for breast cancer in clinical practice.

Probability analysis of axillary lymph node metastasis in breast cancer patients using particle space-time distribution model

The possibility of axillary lymph node metastasis differs in different breast cancer patients and is the strongest prognostic indicator in breast cancer. The existing studies mainly explored the relationship of axillary ultrasound imaging and axillary lymph node metastasis, without exploring whether ultrasound imaging of breast tumour can affect and perform axillary lymph node prediction. Therefore, this Letter proposes a novel particle space-time distribution model to find the correlation between contrast-enhanced ultrasonography of breast tumour and axillary lymphatic metastasis. Starting from the imaging principle of dynamic contrast-enhanced ultrasonography, the particle space-time distribution model not only comprises space-time features of contrast-enhanced ultrasonography with an encoder-decoder network, but also the flow field information of microbubble particles is integrated into the space-time features that better serves the metastasis prediction by enhancing the particle distribution information. Extensive experiments on real patients have demonstrated that dynamic contrast-enhanced ultrasonography of breast tumour can be used to predict the probability of lymphatic metastasis. This conclusion can be interpretable from the clinical and pathological perspectives.

Establishment and Verification of a Bagged-Trees-Based Model for Prediction of Sentinel Lymph Node Metastasis for Early Breast Cancer Patients

Purpose: Lymph node metastasis is a multifactorial event. Several scholars have developed nomograph models to predict the sentinel lymph nodes (SLN) metastasis before operation. According to the clinical and pathological characteristics of breast cancer patients, we use the new method to establish a more comprehensive model and add some new factors which have never been analyzed in the world and explored the prospect of its clinical application.

Materials and methods: The clinicopathological data of 633 patients with breast cancer who underwent SLN examination from January 2011 to December 2014 were retrospectively analyzed. Because of the imbalance in data, we used smote algorithm to oversample the data to increase the balanced amount of data. Our study for the first time included the shape of the tumor and breast gland content. The location of the tumor was analyzed by the vector combining quadrant method, at the same time we use the method of simply using quadrant or vector for comparing. We also compared the predictive ability of building models through logistic regression and Bagged-Tree algorithm. The Bagged-Tree algorithm was used to categorize samples. The SMOTE-Bagged Tree algorithm and 5-fold cross-validation was used to established the prediction model. The clinical application value of the model in early breast cancer patients was evaluated by confusion matrix and the area under receiver operating characteristic (ROC) curve (AUC).

Results: Our predictive model included 12 variables as follows: age, body mass index (BMI), quadrant, clock direction, the distance of tumor from the nipple, morphology of tumor molybdenum target, glandular content, tumor size, ER, PR, HER2, and Ki-67.Finally, our model obtained the AUC value of 0.801 and the accuracy of 70.3%.We used logistic regression to established the model, in the modeling and validation groups, the area under the curve (AUC) were 0.660 and 0.580.We used the vector combining quadrant method to analyze the original location of the tumor, which is more precise than simply using vector or quadrant (AUC 0.801 vs. 0.791 vs. 0.701, Accuracy 70.3 vs. 70.3 vs. 63.6%).

Conclusions: Our model is more reliable and stable to assist doctors predict the SLN metastasis in breast cancer patients before operation.

Predicting distant recurrence in receptor-positive breast cancer patients with limited clinicopathological risk: using the PAM50 Risk of Recurrence score in 1478 postmenopausal patients of the ABCSG-8 trial treated with adjuvant endocrine therapy alone

BACKGROUND

PAM50 is a 50-gene test that is designed to identify intrinsic breast cancer subtypes and generate a Risk of Recurrence (ROR) score. It has been developed to be carried out in qualified routine hospital pathology laboratories.

PATIENTS AND METHODS

One thousand four hundred seventy-eight postmenopausal women with estrogen receptor (ER)+ early breast cancer (EBC) treated with tamoxifen or tamoxifen followed by anastrozole from the prospective randomized ABCSG-8 trial were entered into this study. Patients did not receive adjuvant chemotherapy. RNA was extracted from paraffin blocks and analyzed using the PAM50 test. Both intrinsic subtype (luminal A/B, HER2-enriched, basal-like) and ROR score were calculated. The primary analysis was designed to test whether the continuous ROR score adds prognostic value in predicting distant recurrence (DR) over and above standard clinical variables.

RESULTS

In all tested subgroups, ROR score significantly adds prognostic information to the clinical predictor (P<0.0001). PAM50 assigns an intrinsic subtype to all cases, and the luminal A cohort had a significantly lower ROR at 10 years compared with Luminal B (P<0.0001). Significant and clinically relevant discrimination between low- and high-risk groups occurred also within all tested subgroups.

CONCLUSION(S)

The results of the primary analysis, in combination with recently published results from the ATAC trial, constitute Level 1 evidence for clinical validity of the PAM50 test for predicting the risk of DR in postmenopausal women with ER+ EBC. A 10-year metastasis risk of <3.5% in the ROR low category makes it unlikely that additional chemotherapy would improve this outcome-this finding could help to avoid unwarranted overtreatment.

CLINICAL TRIAL NUMBER

ABCSG 8: NCT00291759.