Cancer Lymph Node Research Articles

Robotic distal gastrectomy using a novel pre-emptive supra-pancreatic approach without duodenal transection in the dissection of D2 lymph nodes for gastric cancer.

Robot-assisted surgery has shown remarkable progress as a minimally invasive procedure for gastric cancer. This study aimed to compare the pre-emptive suprapancreatic approach without duodenal transection and the conventional approach in terms of perioperative feasibility and short-term surgical outcomes. We retrospectively analyzed all patients who underwent robotic distal gastrectomy with D2 lymph node dissection using the da Vinci Xi robotic system between December 2021 and April 2023 and categorized them into two groups for comparison. Patients treated using the pre-emptive suprapancreatic approach (observation group) were compared with those who received the conventional approach (control group). Employing one-to-one propensity score matching, we evaluated the postoperative morbidity and short-term outcomes in these two distinct groups to assess the efficacy and safety of the novel surgical technique. This study enrolled 131 patients: 70 in the observation group and 61 in the control group. After propensity score matching, the operative times were significantly longer in the control group than in the observation group (229.10 ± 33.96 vs. 174.84 ± 18.37, p <0.001). The mean blood loss was lower in the observation group than in the control group (25.20 ± 11.18 vs. 85.00 ± 38.78, p<0.001). Additionally, the observation group exhibited a higher number of retrieved lymph nodes, including suprapyloric, perigastric, and superior pancreatic lymph nodes (28.69 ± 5.48 vs. 19.21 ± 2.89, p <0.001; 4.98 ± 1.27 vs. 4.29 ± 1.21, p = 0.012; 10.52 ± 2.39 vs. 5.50 ± 1.62, p <0.001; 6.26 ± 2.64 vs. 5.00 ± 1.72, p = 0.029). Drain amylase levels in the observation group were significantly lower than those in the control group (30.08 ± 33.74 vs. 69.14 ± 66.81, p <0.001). This study revealed that using the pre-emptive suprapancreatic approach without duodenal transection in the dissection of D2 lymph nodes for gastric cancer is a safe and feasible procedure in terms of surgical outcomes.

Deep-learning features based on F18 fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) to predict preoperative colorectal cancer lymph node metastasis

The objective of this study was to create and authenticate a prognostic model for lymph node metastasis (LNM) in colorectal cancer (CRC) that integrates clinical, radiomics, and deep transfer learning features. In this study, we analyzed data from 119 CRC patients who underwent F18 fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) scanning. The patient cohort was divided into training and validation subsets in an 8:2 ratio, with an additional 33 external data points for testing. Initially, we conducted univariate analysis to screen clinical parameters. Radiomics features were extracted from manually drawn images using pyradiomics, and deep-learning features, radiomics features, and clinical features were selected using Least Absolute Shrinkage and Selection Operator (LASSO) regression and Spearman correlation coefficient. We then constructed a model by training a support vector machine (SVM), and evaluated the performance of the prediction model by comparing the area under the curve (AUC), sensitivity, and specificity. Finally, we developed nomograms combining clinical and radiological features for interpretation and analysis. The deep learning radiomics (DLR) nomogram model, which was developed by integrating deep learning, radiomics, and clinical features, exhibited excellent performance. The area under the curve was (AUC=0.934, 95% confidence interval [CI]: 0.884-0.983) in the training cohort, (AUC=0.902, 95% CI: 0.769-1.000) in the validation cohort, and (AUC=0.836, 95% CI: 0.673-0.998) in the test cohort. We developed a preoperative predictive machine-learning model using deep transfer learning, radiomics, and clinical features to differentiate LNM status in CRC, aiding in treatment decision-making for patients.

Artificial Intelligence Helps Pathologists Increase Diagnostic Accuracy and Efficiency in the Detection of Breast Cancer Lymph Node Metastases.

The detection of lymph node metastases is essential for breast cancer staging, although it is a tedious and time-consuming task where the sensitivity of pathologists is suboptimal. Artificial intelligence (AI) can help pathologists detect lymph node metastases, which could help alleviate workload issues. We studied how pathologists' performance varied when aided by AI. An AI algorithm was trained using more than 32 000 breast sentinel lymph node whole slide images (WSIs) matched with their corresponding pathology reports from more than 8000 patients. The algorithm highlighted areas suspicious of harboring metastasis. Three pathologists were asked to review a dataset comprising 167 breast sentinel lymph node WSIs, of which 69 harbored cancer metastases of different sizes, enriched for challenging cases. Ninety-eight slides were benign. The pathologists read the dataset twice, both digitally, with and without AI assistance, randomized for slide and reading orders to reduce bias, separated by a 3-week washout period. Their slide-level diagnosis was recorded, and they were timed during their reads. The average reading time per slide was 129 seconds during the unassisted phase versus 58 seconds during the AI-assisted phase, resulting in an overall efficiency gain of 55% ( P <0.001). These efficiency gains are applied to both benign and malignant WSIs. Two of the 3 reading pathologists experienced significant sensitivity improvements, from 74.5% to 93.5% ( P ≤0.006). This study highlights that AI can help pathologists shorten their reading times by more than half and also improve their metastasis detection rate.

A preliminary investigation of precise visualization, localization, and resection of pelvic lymph nodes in bladder cancer by using indocyanine green fluorescence-guided approach through intracutaneous dye injection into the lower limbs and perineum.

This study aimed to investigate the feasibility and effectiveness of using indocyanine green (ICG) injected intracutaneously through the lower limbs and perineum for visualized tracking, localization, and qualitative assessment of pelvic lymph nodes (LNs) in bladder cancer to achieve their accurate resection. First, ICG was injected into the LN metastasis model mice lower limbs, and real-time and dynamic in vivo and ex vivo imaging was conducted by using a near-infrared fluorescence imaging system. Additionally, 26 patients with bladder cancer were enrolled and divided into intracutaneous group and transurethral group. A near-infrared fluorescence imaging device with internal and external imaging probes was used to perform real-time tracking, localization, and resection of the pelvic LNs. The mice normal LNs and the metastatic LNs exhibited fluorescence. The metastatic LNs showed a significantly higher signal-to-background ratio than the normal LNs (3.9 ± 0.2 vs. 2.0 ± 0.1, p < 0.05). In the intracutaneous group, the accuracy rate of fluorescent-labeled LNs was 97.6%, with an average of 11.3 ± 2.4 LNs resected per patient. Six positive LNs were detected in three patients (18.8%). In the transurethral group, the accuracy rate of fluorescent-labeled LNs was 84.4%, with an average of 8.6 ± 2.3 LNs resected per patient. Two positive LNs were detected in one patient (12.5%). Following the intracutaneous injection of ICG into the lower limbs and perineum, the dye accumulates in pelvic LNs through lymphatic reflux. By using near-infrared fluorescence laparoscopic fusion imaging, physicians can perform real-time tracking, localization, and precise resection of pelvic LNs.

“Percentage” and “size” of residual viable tumor in lymph node, the performance in estimating pathologic response of lymph node in non-small cell lung cancer treated with neoadjuvant chemoimmunotherapy

There is no universally accepted method for evaluating lymph node metastasis (LNM) in non-small cell lung cancer (NSCLC) after neoadjuvant chemoimmunotherapy. Different protocols recommend evaluating the percentage of residual viable tumor (RVT%) and metastatic tumor size (MTS). Our aim was to determine the prognostic significance of RVT% and MTS, and identify the more effective parameter for pathological evaluating LNM. Two independent cohorts were collected (derivation, n = 84; external validation, n = 42). All patients exhibited metastatic cancer or treatment response in lymph nodes post-surgery. In the derivation cohort, we assessed the mean and largest values of MTS and RVT% in LNM, estimating their optimal cutoffs for event-free survival (EFS) using maximally selected rank statistics. Validation was subsequently conducted in the external validation cohort. The quality of prognostic factors was evaluated using the Area Under Curve (AUC). A positive association was identified between RVT% and MTS, but an absolute association could not be conclusively established. In the derivation cohort, neither the largest MTS (cutoff = 6 mm, p = 0.28), largest RVT% (cutoff = 75%, p = 0.23), nor mean RVT% (cutoff = 55%, p = 0.06) were associated with EFS. However, mean MTS (cutoff = 4.5 mm) in lymph nodes was statistically associated with EFS (p = 0.018), validated by the external cohort (p = 0.017). The prognostic value of MTS exceeded that of ypN staging in both cohorts, as evidenced by higher AUC values. The mean value of MTS can effectively serve as a parameter for the pathological evaluation of lymph nodes, with a threshold of 4.5 mm, closely linked to EFS. Its prognostic value outperforms that of ypN staging.

Washout CYFRA 21-1: A tool to improve diagnostic accuracy of fine needle aspiration in the diagnosis of metastatic lymph nodes in papillary thyroid cancer

Thyroid carcinoma has an increasing incidence of endocrine system cancers. Fine needle aspiration cytology (FNAC) and thyroglobulin (Tg) are the primary diagnostic modalities employed for assessing metastatic lymph nodes (LNs) in thyroid cancer. Due to the limited accuracy, rare patients benefited from these procedures. In this research, we aimed to discover a dependable biomarker that could increase the accuracy of FNAC's ability to diagnose metastatic LNs among patients suffering from papillary thyroid cancer (PTC). From March 2021 to July 2023, 99 LNs from PTC patients who had thyroid ultrasonography suspicions of metastases were examined. All patients underwent FNAC, washout Tg and CYFRA 21-1 measurements. Surgical histology and a subsequent FNAC were utilized to validate the outcomes of LNs. In our study, the optimal cut-off value for CYFRA 21-1 washout fluid was 1.145 ng/mL, with a specificity of 94.00 % (slightly lower than Tg and FNAC at 98 %). However, CYFRA 21-1 demonstrated significantly higher diagnostic sensitivity (85.71 %) and accuracy (86.41 %) compared to Tg (71.43 %, 81.55 %) and FNAC (69.39 %, 80.58 %). Furthermore, FNAC plus washout CYFRA 21-1 performed better in diagnosing the metastatic LNs in PTC than FNAC plus Tg, which may indicate a novel solution for metastatic LNs diagnosis in PTC.

The 8th AJCC classification is inferior to a new neck stage based on intraparotid lymph node in parotid gland cancer

BackgroundLymph node (LN) status is an important prognostic factor for parotid gland cancer (PGC). This study aimed to analyze the impact of extranodal extension (ENE) of intraparotid LN and LN metastasis burden on survival in PGC.MethodsPatients with surgically treated PGC and at least one metastatic cervical LN were retrospectively enrolled. Primary outcome variables were distant metastasis-free survival (DMFS), disease-specific survival (DSS), and overall survival (OS). The impact of ENE and LN metastasis burden was assessed using the Cox model.ResultsA total of 292 patients were included. ENE in cervical or intraparotid LN was not associated with DMFS, DSS, or OS. Intraparotid LN metastasis had a significant impact on prognosis, and the presence of only one metastatic intraparotid LN offered an approximately 1.5-fold risk of distant metastasis. Prognostic models based on the number of positive LNs (1 vs. 2–3 vs. 4+) were superior to the AJCC N stage in terms of DMFS, DSS, and OS.ConclusionsENE of cervical or intraparotid LN has a limited effect on the prognosis of PGC, and the number of positive LNs is better than the AJCC N stage in LN status evaluation.

Prognostic significance and value of further classification of lymphovascular invasion in invasive breast cancer: a retrospective observational study

PurposeTo investigate the prognostic significance of lymphovascular invasion in invasive breast cancer and the value of using specific vascular endothelial markers to further classify lymphovascular invasion.MethodsWe collected 2124 patients with invasive breast cancer who were hospitalized at the First Hospital of Dalian Medical University from 2012 to 2020. Statistical methods were used to investigate the relationship between lymphovascular invasion and clinicopathological characteristics of breast cancer, and the correlation between lymphovascular invasion on overall survival (OS) and disease-free survival (DFS) of various categories of breast cancers. Immunohistochemical staining of breast cancer samples containing lymphovascular invasion using specific vascular endothelial markers D2-40 and CD34 was used to classify lymphovascular invasion and to investigate the relationship between lymphovascular invasion and breast cancer progression.ResultsThere was a high correlation between lymphovascular invasion and T stage, N stage and nerve invasion. Survival analyses showed that patients with lymphovascular invasion, especially luminal B, triple-negative, and Her-2 overexpression breast cancer patients, had poorer OS and DFS prognosis, and that lymphovascular invasion was an independent prognostic factor affecting OS and DFS in breast cancer. The immunohistochemical staining results showed that positive D2-40 staining of lymphovascular invasion was linked to the N stage and localized recurrence of breast cancer.ConclusionLymphovascular invasion is associated with aggressive clinicopathological features and is an independent poor prognostic factor in invasive breast cancer. Breast cancer localized recurrence rate and lymph node metastases are influenced by lymphatic vessel invasion. Immunohistochemical techniques should be added to the routine diagnosis of lymphovascular invasion.

Porphyrin-Based Self-Assembled Nanoparticles for PET/MR Imaging of Sentinel Lymph Node Metastasis.

Diagnosing of lymph node metastasis is challenging sometimes, and multimodal imaging offers a promising method to improve the accuracy. This work developed porphyrin-based nanoparticles (68Ga-F127-TAPP/TCPP(Mn) NPs) as PET/MR dual-modal probes for lymph node metastasis imaging by a simple self-assembly method. Compared with F127-TCPP(Mn) NPs, F127-TAPP/TCPP(Mn) NPs synthesized by amino-porphyrins (TAPP) doping can not only construct PET/MR bimodal probes but also improve the T1 relaxivity (up to 456%). Moreover, T1 relaxivity can be adjusted by altering the molar ratio of TAPP/TCPP(Mn) and the concentration of F127. However, a similar increase in T1 relaxivity was not observed in the F127-TCPP/TCPP(Mn) NPs, which were synthesized using carboxy-porphyrins (TCPP) doping. In a breast cancer lymph node metastasis mice model, subcutaneous injection of 68Ga-F127-TAPP/TCPP(Mn) NPs through the hind foot pad, the normal lymph nodes and metastatic lymph nodes were successfully distinguished based on the difference of PET standard uptake values and MR signal intensities. Furthermore, the dark brown F127-TAPP/TCPP(Mn) NPs demonstrated the potential for staining and mapping lymph nodes. This study provides valuable insights into developing and applying PET/MR probes for lymph node metastasis imaging.

Magnetic resonance imaging-based lymph node radiomics for predicting the metastasis of evaluable lymph nodes in rectal cancer.

Lymph node (LN) staging in rectal cancer (RC) affects treatment decisions and patient prognosis. For radiologists, the traditional preoperative assessment of LN metastasis (LNM) using magnetic resonance imaging (MRI) poses a challenge. To explore the value of a nomogram model that combines Conventional MRI and radiomics features from the LNs of RC in assessing the preoperative metastasis of evaluable LNs. In this retrospective study, 270 LNs (158 nonmetastatic, 112 metastatic) were randomly split into training (n = 189) and validation sets (n = 81). LNs were classified based on pathology-MRI matching. Conventional MRI features [size, shape, margin, T2-weighted imaging (T2WI) appearance, and CE-T1-weighted imaging (T1WI) enhancement] were evaluated. Three radiomics models used 3D features from T1WI and T2WI images. Additionally, a nomogram model combining conventional MRI and radiomics features was developed. The model used univariate analysis and multivariable logistic regression. Evaluation employed the receiver operating characteristic curve, with DeLong test for comparing diagnostic performance. Nomogram performance was assessed using calibration and decision curve analysis. The nomogram model outperformed conventional MRI and single radiomics models in evaluating LNM. In the training set, the nomogram model achieved an area under the curve (AUC) of 0.92, which was significantly higher than the AUCs of 0.82 (P < 0.001) and 0.89 (P < 0.001) of the conventional MRI and radiomics models, respectively. In the validation set, the nomogram model achieved an AUC of 0.91, significantly surpassing 0.80 (P < 0.001) and 0.86 (P < 0.001), respectively. The nomogram model showed the best performance in predicting metastasis of evaluable LNs.

Papillary thyroid cancer and its gene polymorphism; A molecular mechanistic perspective

Thyroid cancer stands as the predominant malignancy within the endocrine system, comprising about 1% of newly identified cancer instances. Papillary Thyroid Cancer (PTC) is the predominant form of thyroid cancer, representing 80% or more of thyroid malignancies. Thyroid carcinoma harbours assorted genetic alterations which are highly prevalent, several of these characteristics are unique to this form of cancer. The conventional oncogenic genetic modifications frequently observed in thyroid carcinoma encompass RAS mutations RET/PTC rearrangements and PAX8-peroxisome proliferator-activated receptor g (PPARg) fusion oncogene. The lately discovered activating mutation in BRAF (the gene for the B-type RAF kinase, BRAF) the most widespread genetic modification in thyroid cancer (30-83%). RKIP (RAF kinase inhibitory protein) had formerly been delineated as a phospholipid binding protein. Mammalian RKIP/PEBP differs from other identified proteins and its role is still being clarified. RKIP over-expression can inhibit MEK interaction with RAF-1 and B-RAF. It plays a role in thyroid cancer progression and lymph node metastasis. So, elucidating mutational profile and protein expression of above cell signalling molecules will be very useful in determining a proper therapeutic target for anti-cancer molecules. Given that tumors often possess numerous genetic and cell signalling abnormalities, thus inhibiting a single signalling pathway is often therapeutically inefficacious, more success could be foreseen with agents directed against multiple cellular pathways.By determining the genetic profile and protein expression of mentioned MAP Kinase pathway molecules new targets can be identified for chemotherapic drugs and novel strategies will be charted out to make modifications in the map kinase pathway with the aim to stop the occurrence and distant metastasis of thyroid cancer.

Active surveillance is a feasible and safe strategy in selected patients with papillary thyroid cancer and suspicious cervical lymph nodes detected after thyroidectomy.

After initial treatment, up to 30% of patients with papillary thyroid cancer (PTC) have incomplete response, mainly cervical lymph node (LN) disease. Previous studies have suggested that active surveillance (AS) is a possible option for these patients. Our aim was to report the results of AS in patients with PTC and cervical LN disease. In this retrospective observational study, we included adult patients treated and followed for PTC, who presented with cervical LN disease and were managed with AS. Growth was defined as an increase ≥ 3mm in either diameter. We included 32 patients: 27 (84.4%) women, age of 39 ± 14 years, all initially treated with total thyroidectomy, and 22 (69%) with therapeutic neck dissection. Cervical LN disease was diagnosed 1 year (0.3-12.6) after initial management, with a diameter of 9.0 mm (6.0-19.0). After a median AS of 4.3 years (0.6-14.1), 4 (12.5%) patients had LNgrowth: 2 (50%) of whom were surgically removed, 1 (25%) was effectively treated with radiotherapy, and 1 (25%) had a scheduled surgery. Tg increase was the only predictive factor of LN growth evaluated as both the delta Tg (p < 0.0366) and percentage of Tg change (p < 0.0140). None of the included patients died, had local complications due to LN growth or salvage therapy, or developed distant metastases during follow-up. In selected patients with PTC and suspicious cervical LNs diagnosed after initial treatment, AS is a feasible and safe strategy as it allows effective identification and treatment of the minority of patients who progress.

Head-to-head comparison of [68Ga]Ga-DOTA-FAPI-04 and [18F]FDG PET/CT for the evaluation of tonsil cancer and lymph node metastases: a single-centre retrospective study

BackgroundThis study aimed to compare the diagnostic value of [68 Ga]Ga-DOTA-FAPI-04 and [18F]FDG PET/CT imaging for primary lesions and metastatic lymph nodes in patients with tonsil cancer.MethodTwenty-one tonsil cancer patients who underwent [68 Ga]Ga-DOTA-FAPI-04 and [18F]FDG PET/CT scans within two weeks in our centre were retrospectively enrolled. The maximum standardized uptake value (SUVmax) and tumor-to-background ratio (TBR) of the two tracers were compared by using the Mann‒Whitney U test. In addition, the sensitivity, specificity, and accuracy of the two methods for diagnosing metastatic lymph nodes were analysed.ResultsIn detecting primary lesions, the efficiency was higher for [68 Ga]Ga-DOTA-FAPI-04 PET/CT (20/22) than for [18F]FDG PET/CT (9/22). Although [68 Ga]Ga-DOTA-FAPI-04 uptake (SUVmax, 5.03 ± 4.06) was lower than [18F]FDG uptake (SUVmax, 7.90 ± 4.84, P = 0.006), [68 Ga]Ga-DOTA-FAPI-04 improved the distinction between the primary tumor and contralateral normal tonsillar tissue. The TBR was significantly higher for [68 Ga]Ga-DOTA-FAPI-04 PET/CT (3.19 ± 2.06) than for [18F]FDG PET/CT (1.89 ± 1.80) (p < 0.001). In lymph node analysis, SUVmax and TBR were not significantly different between [68 Ga]Ga-DOTA-FAPI-04 and [18F]FDG PET/CT (7.67 ± 5.88 vs. 8.36 ± 6.15, P = 0.498 and 5.56 ± 4.02 vs. 4.26 ± 3.16, P = 0.123, respectively). The specificity and accuracy of [68 Ga]Ga-DOTA-FAPI-04 PET/CT were higher than those of [18F]FDG PET/CT in diagnosing metastatic cervical lymph nodes (all P < 0.05).ConclusionThe availability of [68 Ga]Ga-DOTA-FAPI-04 complements the diagnostic results of [18F]FDG by improving the detection rate of primary lesions and the diagnostic accuracy of cervical metastatic lymph nodes in tonsil cancer compared to [18F]FDG.

Radiological Cutoff Values for Diagnosis of Lymph Node Metastasis in Colorectal Cancer With Multilevel Analysis.

A cutoff value for lymph node diameter in colorectal cancer lymph node metastases has not been established. This prospective study aimed to investigate the direct association between swollen lymph nodes identified on preoperative computed tomography (CT) and pathological findings and proposed a cutoff value. We enrolled patients scheduled to undergo curative surgery with lymph node dissection for colorectal adenocarcinoma who underwent preoperative contrast-enhanced CT and had swollen lymph nodes ≥7 mm in diameter. Two gastrointestinal surgeons intraoperatively identified the target lymph nodes to assess the association between lymph node diameter and pathological findings. The diagnostic performance for lymph node metastasis was determined using multi-level logistic modelling. A total of 109 patients were enrolled, and 225 swollen lymph nodes were pathologically evaluated. Using a cutoff value of ≥9 mm for the short diameter, the positive and negative predictive values, sensitivity, and specificity were 100.0% (99.6%-100.0%), 99.9% (99.1%-100.0%), 62.0% (45.6%-76.0%), and 84.9% (67.0%-94.0%), respectively. The cutoff value for improving the positive predictive value for the preoperative lymph node metastasis diagnosis in colorectal cancer patients should be at least 9 mm in diameter.

Abstract PO4-07-07: The construction and clinical research value of the prediction model of sentinel lymph node metastasis based on indocyanine green fluorescence quantification in breast cancer

Abstract Objective: To establish a prediction model of sentinel lymph node metastasis by quantitative indocyanine green fluorescence analysis in breast cancer. Methods: From May 2023 to June 2023, 20 patients with breast cancer underwent modified radical mastectomy in the General Surgery Department of Jiangsu Provincial People's Hospital were chosen for research and analysis. Sentinel lymph node were taken from all the patients. The sentinel lymph nodes were located by using the double staining method of subcutaneous injection of Methylene blue and intravenous injection of indocyanine green. The postoperative pathology is used as the standard for evaluating the presence or absence of metastatic cancer in lymph nodes. At the same time, the transcutaneous fluorescence of sentinel lymph node was recorded and the peak time of fluorescence intensity curve was plotted. To establish a logistic diagnostic prediction model and fluorescence threshold, the sentinel lymph node was incised during operation and the fluorescence of sentinel lymph node was recorded again. The criteria for ICG positive determination are determined by the Youden index, which was used to judge the lymph nodes above the threshold in the Logistic diagnostic prediction model. Lymph nodes below the threshold determined in the Logistic diagnostic prediction model are the criteria for ICG negative determination. Cases with less than 3 sentinel lymph node were excluded. Results: 19 cases were ultimately selected. Methylene blue staining data was taking as the judgment standard of sentinel lymph node. Fluorescence imaging was 100% in 14 patients, 66.7% (2/3) in 1 patient, and 125%-150% (5/4-6/4) in 4 patients. Fluorescence quantification values below 2.91 are negative lymph nodes, and values above 4.69 are positive lymph nodes. Moreover, caution is required between 2.91 and 4.69. Combining with the fluorescence dynamic attenuation curve, the positive group has a plateau curve, while the negative group mostly has a decline curve. Combining fluorescence quantification with fluorescence dynamic attenuation curve, the diagnostic rate of positive lymph nodes has a lower false negative rate compared to simple fluorescence quantification and fluorescence dynamic attenuation curve. Conclusion: The quantitative difference value of indocyanine green fluorescence angiography can be used to predict the positive metastasis of sentinel lymph node in breast cancer, making it an attempt to exempt sentinel lymph node biopsy. Citation Format: Yang Wu, Wei Zhang, Jinhai Tang. The construction and clinical research value of the prediction model of sentinel lymph node metastasis based on indocyanine green fluorescence quantification in breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-07-07.

Abstract PO2-07-03: Retrospective Validation Study of an Artificial Neural Network-Based Preoperative Decision-Support Tool for Noninvasive Lymph Node Staging (NILS) in Women with Primary Breast Cancer (ISRCTN14341750)

Abstract Background Surgical sentinel lymph node biopsy (SLNB) is routinely used to reliably stage axillary lymph nodes in early breast cancer (BC). However, SLNB may be associated with postoperative arm morbidities. For most patients with BC undergoing SLNB, the findings are benign, and the procedure is currently questioned. A decision-support tool for the prediction of benign sentinel lymph nodes based on preoperatively available data has been developed using artificial neural network modelling [1,2]. Methods This was a retrospective geographical and temporal validation study of the noninvasive lymph node staging (NILS) model, based on preoperatively available data from 586 women consecutively diagnosed with primary BC at two sites. Ten preoperative clinicopathological characteristics from each patient were entered into the web-based calculator (Table 1), and the probability of benign lymph nodes was predicted. Vascular invasion, the tenth feature of the NILS model, was difficult to determine preoperatively. Therefore, a separate ANN model was developed to impute this feature, using the other nine features of the NILS model as predictors. A user-friendly web implementation of the NILS model was tested in this study. The performance of the NILS model was assessed in terms of discrimination with the area under the receiver operating characteristic curve (AUC). The primary endpoint was axillary nodal status (discrimination, benign [N0] vs. metastatic axillary nodal status [N+]) determined by the NILS model compared to nodal status by definitive pathology. Results The mean age of the women in the cohort was 65 years, and most of them (93%) had luminal cancers (Table 2). Approximately three-fourths of the patients had no metastases in SLNB (N0 74%). The AUC for the predicted probabilities for the whole cohort was 0.6741 (95% confidence interval: 0.6255–0.7227). More than one in four patients (n=151, 26%) were identified as candidates for SLNB omission when applying the predefined cut-off for lymph node negativity from the development cohort (Table 3). Conclusion The performance of the NILS model was satisfactory. In approximately every fourth patient, SLNB could potentially be omitted. Considering the shift from postoperatively to preoperatively available predictors in this validation study, we have demonstrated the robustness of the NILS model. The clinical usability of the web interface will be evaluated before its clinical implementation. Trial registration Registered in the ISRCTN registry with study ID ISRCTN14341750.

Abstract PS03-06: Clinical implementation of artificial-intelligence-assisted detection of breast cancer metastases in sentinel lymph nodes: saving costs and time (the CONFIDENT-B trial)

Abstract Background Metastases in sentinel lymph nodes (SN) of breast cancer (BC) patients are strongly associated with a worse survival and consequently guide treatment. If metastases are absent upon pathologist’ assessment of the regular hematoxylin and eosin (HE)-slide, additional immunohistochemistry (IHC)-stains are performed to ensure that no metastases are missed. However, these stains come at high additional costs, which may exceed specimen reimbursement. Fortunately, digital pathology is becoming more common, thereby creating an avenue of opportunities for artificial intelligence (AI) assistance. Although the number of studies on (promising) AI-algorithms increases exponentially, studies on actual clinical implementation are lacking. In this single-center prospective trial, we investigated to which extent an artificial intelligence (AI)-assisted clinical workflow for the detection of SN-metastases reduces IHC-use, while maintaining current diagnostic safety standards. Methods We enrolled 190 SN-specimens of 182 patients with invasive or in situ BC from September 2022 to May 2023. SN-specimens were allocated bi-weekly to either the control-arm (n=90) or the intervention-arm (n=100). In the control-arm, SN-specimens were digitally assessed according to the current clinical workflow, while pathologists in the intervention-arm assessed the SN-specimen with the output the ‘Metastasis-Detection-App’ (Visiopharm©) available. In both groups, IHC was performed in all morphologically negative cases. Main outcome was the relative risk (RR) of IHC-use per detected case of SN-metastases. Case-mix adjustment was performed by log-binomial regression. Results Overall, 59 SN-specimens contained metastases (31.1%). AI-assistance resulted in a significantly lower risk of IHC-use per detected case of SN-metastases (adjusted RR: 0.680, 95% CI: 0.347-0.878). Besides preventing IHC-use, thereby reducing costs, AI-assisted pathologists also spent significantly less time on their assessment of the SN-specimen (3m:41s vs. 6m:04s, p = 0.028). Furthermore, the sensitivity of AI-assisted pathologists was up to 30% higher. The AI-assisted pathologists missed two cases of micro-metastases in the intervention arm, one of which was in retrospect highlighted by the algorithm, while in the other case the tumor cells were located in a heavily cauterized area of the HE-slide and therefore only visible on the (serial) IHC-slides. In the control arm, the algorithm in retrospect picked up all micro- and macro-metastases and nearly half of the isolated tumor cells (ITC). In addition, all participating pathologists stated that AI was easy to use, that they felt confident using AI, and that besides saving them time, AI made their work more enjoyable. Cost reductions on IHC by AI-assistance depend on laboratory policy (i.e. when and on how many levels IHC is performed), but, at a cost of €25,- per IHC-stain, range from €1.500 to €3.500 per 100 SN’s in a scenario where IHC is performed in all morphologically negative cases. In a scenario where IHC is only performed in patients in whom finding ITC has clinical consequences (i.e. patients who received neoadjuvant treatment), cost savings on IHC range from €7.500-€12.500 per 100 SNs, depending on laboratory policy. Conclusion AI-implementation for the detection of SN-metastases in BC-patients leads to a significant reduction of IHC-use and subsequent costs, while saving pathologists time and making their work more enjoyable. Importantly, AI-implementation during this trial was safe and patients were not at risk of an inferior diagnosis. By doing this trial alone, an estimated €3,000 on IHC-use was saved. Such tangible cost savings are crucial to build a viable business case for AI implementation in diagnostic pathology. Citation Format: Carmen van Dooijeweert, Rachel Flach, Natalie ter Hoeve, Celien Vreuls, Roel Goldschmeding, Jan-Erik Freund, Paul Pham, Tri Nguyen, Elsken Van der Wall, Geert Frederix, Nikolas Stathonikos, Paul Van Diest. Clinical implementation of artificial-intelligence-assisted detection of breast cancer metastases in sentinel lymph nodes: saving costs and time (the CONFIDENT-B trial) [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PS03-06.

Abstract PO2-28-07: Artificial Intelligence-based Computer-aided Diagnosis for Mammography: Predicting Occult Invasive Cancer in Women with Biopsy-proven Ductal Carcinoma in Situ

Abstract Background: Ductal carcinoma in situ (DCIS) is a noninvasive breast cancer confined within the basement membrane. Due to the increase in breast cancer screening, DCIS is increasingly being diagnosed on biopsy. Active surveillance is a novel and evolving treatment strategy for DCIS to reduce overtreatment. Currently ongoing clinical trials of active surveillance include only mammographic findings in their eligibility criteria, however mammography has limited ability to detect occult invasive cancer. The objective of this study was to investigate whether an artificial intelligence-based computer-aided diagnosis (AI-CAD) application for mammography can improve the prediction of occult invasive cancer in women with percutaneous needle biopsy-proven DCIS. Methods: A retrospective search of our database identified consecutive women with percutaneous needle biopsy-proven DCIS who underwent surgery at Seoul National University Hospital (Seoul, Korea) between June 2019 and May 2021. Two board-certified breast radiologists reviewed preoperative mammographic findings of the primary tumor in consensus according to the Breast Imaging Reporting and Data System. Using a commercially available AI-CAD (Lunit INSIGHT for Mammography, Lunit Inc.), quantitative abnormality scores of 0 to 100% of the ipsilateral breast were obtained. Data were collected on age at diagnosis, symptoms, biopsy methods, DCIS grade on biopsy, and presence or absence of invasive cancer and axillary lymph node metastases on surgical pathology. Multivariable logistic regression models were used to evaluate factors associated with prediction of occult invasive cancer. Results: Of 692 biopsy-proven DCIS in 692 women (mean age, 53 years ± 11 [standard deviation]), 298 (43%) presented as calcifications only, 160 (23%) as mass or asymmetry, and 111 (16%) as masses with calcifications. The other 123 (18%) cancers were not demonstrable on mammography. Surgical pathology revealed occult invasive cancer in 278 (40%) women and 18 (3%) had axillary metastasis. Women with occult invasive cancer showed higher abnormality scores on AI-CAD than women with pure DCIS (mean, 70% vs. 51%; P &amp;lt; .001), and women with axillary metastasis had higher abnormality scores on AI-CAD than women without axillary metastasis (mean, 88% vs. 58%; P = .002). High ( &amp;gt;75%) abnormality score on AI-CAD (Odds ratio [OR], 1.8 [95% CI: 1.3, 2.6]; P &amp;lt; .001), age of 55 years or higher (OR, 1.6 [95% CI: 1.2, 2.3]; P =.005), present symptoms in the ipsilateral breast (OR, 2.7 [95% CI: 1.9, 4.0]; P &amp;lt; .001), biopsy performed with a 14-gauge automated gun (OR, 2.5 [95% CI: 1.7, 3.6]; P &amp;lt; .001), and high-grade DCIS at biopsy (OR, 2.5 [95% CI: 1.7, 3.5]; P &amp;lt; .001] were independent predictors of occult invasive cancer with a C-index of 0.75 (95% CI: 0.72, 0.78). Conclusion: The quantitative score of preoperative mammography obtained from a commercially available AI-CAD was able to predict occult invasive cancer in an easy and simple manner. AI-CAD applications for preoperative mammography may be useful in predicting occult invasive cancer in women with biopsy-proven DCIS, especially in those without preoperative breast MRI. Citation Format: Su Hyun Lee, Hajung Kim, Su Min Ha, Soo-Yeon Kim, Jung Min Chang, Nariya Cho, Woo Kyung Moon. Artificial Intelligence-based Computer-aided Diagnosis for Mammography: Predicting Occult Invasive Cancer in Women with Biopsy-proven Ductal Carcinoma in Situ [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-28-07.

1965: Acute toxicity after SABR to the prostate and pelvic lymph nodes for high-risk prostate cancer

1965: Acute toxicity after SABR to the prostate and pelvic lymph nodes for high-risk prostate cancer

The use of longitudinal CT-based radiomics and clinicopathological features predicts the pathological complete response of metastasized axillary lymph nodes in breast cancer

BackgroundAccurate assessment of axillary status after neoadjuvant therapy for breast cancer patients with axillary lymph node metastasis is important for the selection of appropriate subsequent axillary treatment decisions. Our objectives were to accurately predict whether the breast cancer patients with axillary lymph node metastases could achieve axillary pathological complete response (pCR).MethodsWe collected imaging data to extract longitudinal CT image features before and after neoadjuvant chemotherapy (NAC), analyzed the correlation between radiomics and clinicopathological features, and developed models to predict whether patients with axillary lymph node metastasis can achieve axillary pCR after NAC. The clinical utility of the models was determined via decision curve analysis (DCA). Subgroup analyses were also performed. Then, a nomogram was developed based on the model with the best predictive efficiency and clinical utility and was validated using the calibration plots.ResultsA total of 549 breast cancer patients with metastasized axillary lymph nodes were enrolled in this study. 42 independent radiomics features were selected from LASSO regression to construct a logistic regression model with clinicopathological features (LR radiomics-clinical combined model). The AUC of the LR radiomics-clinical combined model prediction performance was 0.861 in the training set and 0.891 in the testing set. For the HR + /HER2 − , HER2 + , and Triple negative subtype, the LR radiomics-clinical combined model yields the best prediction AUCs of 0.756, 0.812, and 0.928 in training sets, and AUCs of 0.757, 0.777 and 0.838 in testing sets, respectively.ConclusionsThe combination of radiomics features and clinicopathological characteristics can effectively predict axillary pCR status in NAC breast cancer patients.