| 1. |
- Borg, Gunilla, et al.
(författare)
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Risk factors for seroma formation after axillary lymph node dissection with special focus on the impact of early shoulder exercise
- 2023
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Ingår i: Acta Oncologica. - 0284-186X. ; 62:5, s. 444-450
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Tidskriftsartikel (refereegranskat)abstract
- Background: Shoulder and arm dysfunction such as reduced range of motion (ROM) and seroma formation, are common complications following axillary lymph node dissection (ALND). There are conflicting results on the effect of early postoperative exercise on the risk of seroma. This study aims to present incidence of symptomatic seroma formation in a large, population-based cohort, and assesses whether early shoulder mobilization, and other common patient and treatment-related factors are predictors of seroma. Methods: This observational cohort study at the Surgical clinic at Lund University Hospital in Sweden, included 217 consecutive patients who underwent ALND due to breast cancer, cutaneous malignant melanoma (CMM), or carcinoma of unknown primary. A shoulder exercise program was introduced on the first postoperative day and data were collected at routine follow-up 4–6 weeks postsurgery. Main outcome was the strength of the associations between postsurgery exercise and seroma incidence based on logistic regression analyses, supported by data on seroma volume and number of aspirations. Results: Two hundred patients completed the study. The overall seroma incidence was 67.5% and the odds of seroma were lower for patients practicing ROM exercise two times/day versus 0–1 time/day (OR 0.42, 95% CI 0.18–0.96, p =.038). ROM exercise greater than two times/day did not increase the volume, neither did the arm cycling exercise. ALND combined with mastectomy and CMM surgery were associated with larger seroma volumes (1116 ± 1068ml, p =.006) and (1318 ± 920 ml, p <.001), respectively, compared to the breast conserving surgery (537 ± 478ml) while neoadjuvant chemotherapy showed no influence. The effect of age, patients ≥60 years compared to younger, or BMI ≥ 30.0 were weaker (p =.08). Conclusions: Extensive surgical treatments for breast cancer and malignant melanoma produces more seroma, and higher age and obesity may also influence the risk. ROM exercises twice daily predict a lower incidence of seroma following ALND, and more frequent shoulder exercise do not increase the volumes.
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| 2. |
- Dihge, Looket, et al.
(författare)
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Artificial neural network models to predict nodal status in clinically node-negative breast cancer
- 2019
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Ingår i: BMC Cancer. - : Springer Science and Business Media LLC. - 1471-2407. ; 19:1
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Background: Sentinel lymph node biopsy (SLNB) is standard staging procedure for nodal status in breast cancer, but lacks therapeutic benefit for patients with benign sentinel nodes. For patients with positive sentinel nodes, individualized surgical strategies are applied depending on the extent of nodal involvement. Preoperative prediction of nodal status is thus important for individualizing axillary surgery avoiding unnecessary surgery. We aimed to predict nodal status in clinically node-negative breast cancer and identify candidates for SLNB omission by including patient-related and pathological characteristics into artificial neural network (ANN) models. Methods: Patients with primary breast cancer were consecutively included between January 1, 2009 and December 31, 2012 in a prospectively maintained pathology database. Clinical- and radiological data were extracted from patient's files and only clinically node-negative patients constituted the final study cohort. ANN-based models for nodal prediction were constructed including 15 risk variables for nodal status. Area under the receiver operating characteristic curve (AUC) and Hosmer-Lemeshow goodness-of-fit test (HL) were used to assess performance and calibration of three predictive ANN-based models for no lymph node metastasis (N0), metastases in 1-3 lymph nodes (N1) and metastases in ≥ 4 lymph nodes (N2). Linear regression models for nodal prediction were calculated for comparison. Results: Eight hundred patients (N0, n = 514; N1, n = 232; N2, n = 54) were included. Internally validated AUCs for N0 versus N+ was 0.740 (95% CI = 0.723-0.758); median HL was 9.869 (P = 0.274), for N1 versus N0, 0.705 (95% CI = 0.686-0.724; median HL: 7.421; P = 0.492) and for N2 versus N0 and N1, 0.747 (95% CI = 0.728-0.765; median HL: 9.220; P = 0.324). Tumor size and vascular invasion were top-ranked predictors of all three end-points, followed by estrogen receptor status and lobular cancer for prediction of N2. For each end-point, ANN models showed better discriminatory performance than multivariable logistic regression models. Accepting a false negative rate (FNR) of 10% for predicting N0 by the ANN model, SLNB could have been abstained in 27.25% of patients with clinically node-negative axilla. Conclusions: In this retrospective study, ANN showed promising result as decision-supporting tools for estimating nodal disease. If prospectively validated, patients least likely to have nodal metastasis could be spared SLNB using predictive models. Trial registration: Registered in the ISRCTN registry with study ID ISRCTN14341750. Date of registration 23/11/2018. Retrospectively registered.
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| 3. |
- Dihge, Looket, et al.
(författare)
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Epidermal growth factor receptor (EGFR) and the estrogen receptor modulator amplified in breast cancer (AIB1) for predicting clinical outcome after adjuvant tamoxifen in breast cancer.
- 2008
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Ingår i: Breast Cancer Research and Treatment. - : Springer Science and Business Media LLC. - 1573-7217 .- 0167-6806. ; 109:2, s. 255-262
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Tidskriftsartikel (refereegranskat)abstract
- The epidermal growth factor receptor (EGFR) and the estrogen receptor (ER) modulator Amplified In Breast cancer-1 (AIB1) have been reported to be of importance for the prognosis of breast cancer patients. We have analyzed AIB1 and EGFR by immunohistochemistry in primary breast cancers (n = 297) arranged in a tissue microarray in order to predict outcome after adjuvant endocrine therapy with tamoxifen for two years. High expression of AIB1 was associated with DNA-nondiploidy, high S-phase fraction, HER2 amplification, and short term (≤2 years) distant disease-free survival (DDFS), independent of ER status. High expression of EGFR was strongly associated to ER negativity and also correlated with progesterone receptor negativity, high S-phase fraction, and inversely correlated with nodal metastases. In univariate analysis, high EGFR was associated with shorter DDFS (hazard ratio 2.1; P = 0.017), and reached borderline significance in a multivariate analysis, adjusting for ER, menopausal and lymph node status, tumor size, and HER2 (P = 0.057). In conclusion, both AIB1 and EGFR were associated to DDFS for breast cancer patients treated with two years of adjuvant tamoxifen; AIB1 with the development of early distant recurrences, indicating association between high AIB1 and resistance to tamoxifen during treatment, and EGFR with distant recurrences up to a follow up of five years.
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| 4. |
- Dihge, Looket, et al.
(författare)
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Prediction of lymph node metastasis in breast cancer by gene expression and clinicopathological models: Development and validation within a population based cohort.
- 2019
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Ingår i: Clinical Cancer Research. - 1078-0432. ; 25:21, s. 6368-6381
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: More than 70% of patients with breast cancer present with node-negative disease, yet all undergo surgical axillary staging. We aimed to define predictors of nodal metastasis using clinicopathological characteristics (CLINICAL), gene expression data (GEX), and mixed features (MIXED) and to identify patients at low risk of metastasis who might be spared sentinel lymph node biopsy (SLNB).Experimental Design: Breast tumors (n = 3,023) from the population-based Sweden Cancerome Analysis Network–Breast initiative were profiled by RNA sequencing and linked to clinicopathologic characteristics. Seven machine-learning models present the discriminative ability of N0/N+ in development (n = 2,278) and independent validation cohorts (n = 745) stratified as ER+HER2−, HER2+, and TNBC. Possible SLNB reduction rates are proposed by applying CLINICAL and MIXED predictors.Results: In the validation cohort, the MIXED predictor showed the highest area under ROC curves to assess nodal metastasis; AUC = 0.72. For the subgroups, the AUCs for MIXED, CLINICAL, and GEX predictors ranged from 0.66 to 0.72, 0.65 to 0.73, and 0.58 to 0.67, respectively. Enriched proliferation metagene and luminal B features were noticed in node-positive ER+HER2− and HER2+ tumors, while upregulated basal-like features were observed in node-negative TNBC tumors. The SLNB reduction rates in patients with ER+HER2− tumors were 6% to 7% higher for the MIXED predictor compared with the CLINICAL predictor accepting false negative rates of 5% to 10%.Conclusions: Although CLINICAL and MIXED predictors of nodal metastasis had comparable accuracy, the MIXED predictor identified more node-negative patients. This translational approach holds promise for development of classifiers to reduce the rates of SLNB for patients at low risk of nodal involvement.
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| 5. |
- Dihge, Looket
(författare)
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Predictors of Lymph Node Metastasis in Primary Breast Cancer - Risk Models for Tailored Axillary Management
- 2018
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Most patients with breast cancer present with low-risk tumors, node-negative disease, and excellent prognosis. For these patients, routine axillary nodal staging by sentinel lymph node biopsy (SLNB) has no therapeutic benefit. For patients with limited sentinel lymph node metastasis, completion axillary nodal dissection is controversial. Furthermore, those with heavy-burden metastasis could benefit from preoperative selection for neoadjuvant treatment and/or more extensive axillary nodal excision rather than SLNB.This thesis present results on the utility of axillary ultrasonography (AUS), as well as novel prediction models for the estimating disease-free axilla, limited axillary nodal metastasis, and heavy-burden axillary nodal metastasis.Study I The sensitivity of AUS to detect metastatic nodal disease was low with a high false negative rate. Axillary metastatic burden, defined by metastatic size and number of involved nodes, was the most important predictor of an abnormal AUS. This suggest that AUS is unreliable in patients with low metastatic burden. Histological grade was found to be an independent factor that effected the accuracy of AUS performance. Patients with HER2-positive tumors were found to have higher rates of AUS abnormalities. The overall axillary metastatic burden was higher in patients with preoperative verified nodal metastasis by AUS-guided biopsy compared with those with normal AUS findings but with metastatic sentinel lymph node.Study II Breast cancer surrogate molecular subtypes, age, mode of detection, tumor size, multifocality, and vascular invasion were identified as predictors of nodal disease in patients with T1-T2 breast cancer. Three nomograms that included these predictors were developed to predict disease-free axilla N0, limited axillary nodal metastasis (1-2 positive lymph nodes), and heavy-burden axillary nodal metastasis (≥ 3 positive lymph nodes). Area under the ROC curves (AUCs) ranged from 0.70–0.81. The increase in tumor size was found to be less often associated with metastatic nodal involvement in the TNBC subtype than in other non-TNBC subtypes.Study III Clinicopathological characteristics were incorporated into artificial neural network models to predict disease-free axilla N0, low-burden metastasis (1-3 positive nodes), and heavy-burden metastasis (≥ 4 positive nodes) in patients with clinically node-negative breast cancer. Tumor size, LVI, and multifocality displayed linear correlation patterns to the nodal status end-points, while other predictors (age, histological type, ER status, PR status, Ki-67 values, mode of detection, and tumor localization in the breast) revealed non-linear dynamic associations. The clinical utility of reducing unnecessary SLNB was assessed; a cut-off value according to maximum negative predictive value or false-negative rate of 5–10% in a model to discriminate disease-free axilla yielded a SLNB reduction rate of 8–27%.Study IV Predictors of nodal metastasis were assessed using clinicopathological characteristics, gene expression data, and combined features. In the overall validation cohort, the predictor with combined features showed the highest discriminative performance (AUC 0.72). However, discriminatory performances were highly similar using clinicopathological predictors alone across the surrogate molecular subtypes based on the ER, PR, and HER2 status. Higher proportions of the luminal B intrinsic features and proliferation-related genes were observed in predicted node-positive ER+HER2- and HER2+ tumors, while low-expression of basal-like markers were observed in predicted node-positive TNBC tumors. In conclusion, these studies demonstrate the ability to estimate axillary nodal burden using preoperatively obtainable predictors and highlight nonlinear associations between clinicopathological variables and nodal metastasis. Preoperative prediction of the nodal status would facilitate individualized axillary management.
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| 6. |
- Dihge, Looket, et al.
(författare)
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The accuracy of preoperative axillary nodal staging in primary breast cancer by ultrasound is modified by nodal metastatic load and tumor biology
- 2016
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Ingår i: Acta Oncologica. - 1651-226X. ; 55:8, s. 976-982
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Tidskriftsartikel (refereegranskat)abstract
- Background The outcome of axillary ultrasound (AUS) with fine-needle aspiration biopsy (FNAB) in the diagnostic work-up of primary breast cancer has an impact on therapy decisions. We hypothesize that the accuracy of AUS is modified by nodal metastatic burden and clinico-pathological characteristics. Material and methods The performance of AUS and AUS-guided FNAB for predicting nodal metastases was assessed in a prospective breast cancer cohort subjected for surgery during 2009-2012. Predictors of accuracy were included in multivariate analysis. Results AUS had a sensitivity of 23% and a specificity of 95%, while AUS-guided FNAB obtained 73% and 100%, respectively. AUS-FNAB exclusively detected macro-metastases (median four metastases) and identified patients with more extensive nodal metastatic burden in comparison with sentinel node biopsy. The accuracy of AUS was affected by metastatic size (OR 1.11), obesity (OR 2.46), histological grade (OR 4.43), and HER2-status (OR 3.66); metastatic size and histological grade were significant in the multivariate analysis. Conclusions The clinical utility of AUS in low-risk breast cancer deserves further evaluation as the accuracy decreased with a low nodal metastatic burden. The diagnostic performance is modified by tumor and clinical characteristics. Patients with nodal disease detected by AUS-FNAB represent a group for whom neoadjuvant therapy should be considered.
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| 7. |
- Dihge, Looket, et al.
(författare)
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The implementation of NILS : A web-based artificial neural network decision support tool for noninvasive lymph node staging in breast cancer
- 2023
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Ingår i: Frontiers in Oncology. - : Frontiers Media SA. - 2234-943X. ; 13
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Tidskriftsartikel (refereegranskat)abstract
- Objective: To implement artificial neural network (ANN) algorithms for noninvasive lymph node staging (NILS) to a decision support tool and facilitate the option to omit surgical axillary staging in breast cancer patients with low-risk of nodal metastasis. Methods: The NILS tool is a further development of an ANN prototype for the prediction of nodal status. Training and internal validation of the original algorithm included 15 clinical and tumor-related variables from a consecutive cohort of 800 breast cancer cases. The updated NILS tool included 10 top-ranked input variables from the original prototype. A workflow with four ANN pathways was additionally developed to allow different combinations of missing preoperative input values. Predictive performances were assessed by area under the receiver operating characteristics curves (AUC) and sensitivity/specificity values at defined cut-points. Clinical utility was presented by estimating possible sentinel lymph node biopsy (SLNB) reduction rates. The principles of user-centered design were applied to develop an interactive web-interface to predict the patient’s probability of healthy lymph nodes. A technical validation of the interface was performed using data from 100 test patients selected to cover all combinations of missing histopathological input values. Results: ANN algorithms for the prediction of nodal status have been implemented into the web-based NILS tool for personalized, noninvasive nodal staging in breast cancer. The estimated probability of healthy lymph nodes using the interface showed a complete concordance with estimations from the reference algorithm except in two cases that had been wrongly included (ineligible for the technical validation). NILS predictive performance to distinguish node-negative from node-positive disease, also with missing values, displayed AUC ranged from 0.718 (95% CI, 0.687-0.748) to 0.735 (95% CI, 0.704-0.764), with good calibration. Sensitivity 90% and specificity 34% were demonstrated. The potential to abstain from axillary surgery was observed in 26% of patients using the NILS tool, acknowledging a false negative rate of 10%, which is clinically accepted for the standard SLNB technique. Conclusions: The implementation of NILS into a web-interface are expected to provide the health care with decision support and facilitate preoperative identification of patients who could be good candidates to avoid unnecessary surgical axillary staging.
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| 8. |
- Grabau, Dorthe, et al.
(författare)
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Completion axillary dissection can safely be omitted in screen detected breast cancer patients with micrometastases. A decade's experience from a single institution.
- 2013
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Ingår i: European Journal of Surgical Oncology. - : Elsevier BV. - 1532-2157 .- 0748-7983. ; 39:6, s. 601-607
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: The need for completion axillary lymph node dissection (ALND) in breast cancer patients with micrometastases in the sentinel nodes (SNs) is controversial. The aim of this retrospective observational study is to determine if the method of detection of early breast cancer is predictive for additional positive nodes in patients with micrometastases in the SNs. METHODS: Between 2001 and 2011 a total of 1993 women with primary unilateral breast cancer had surgery at Skåne University Hospital, Lund. Of 1993 patients, 1458 had an SN biopsy and nearly all patients with micro- and macrometastases had ALND. RESULTS: Micrometastases defined as >0.2 mm/>200 cells and ≤2.0 mm were found in 62 of 757 screen-detected patients and in 81 of 701 patients with symptomatic breast cancer. Only 3 of the screen-detected patients with micrometastases, all with tumour size >15 mm (range 18-39 mm), had metastases in the completion ALND whereas this was found in 18 of the symptomatic patients with micrometastases (p = 0.01), (tumour size, range 10-30 mm). Logistic regression analysis adjusted for method of detection, tumour size and histological grade showed 5 times higher odds for further metastases in ALND in patients with symptomatic presentation vs. screen-detected breast cancer. CONCLUSION: Despite the small number of patients with micrometastases in this large cohort of breast cancer patients, these results support the contention that completion ALND can safely be omitted in screen-detected breast cancer patients with micrometastases in the SNs.
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| 9. |
- Hjärtström, Malin, et al.
(författare)
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Noninvasive Staging of Lymph Node Status in Breast Cancer Using Machine Learning : External Validation and Further Model Development
- 2023
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Ingår i: JMIR Cancer. - Toronto, ON : JMIR Publications. - 2369-1999. ; 9
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Tidskriftsartikel (refereegranskat)abstract
- Background: Most patients diagnosed with breast cancer present with a node-negative disease. Sentinel lymph node biopsy (SLNB) is routinely used for axillary staging, leaving patients with healthy axillary lymph nodes without therapeutic effects but at risk of morbidities from the intervention. Numerous studies have developed nodal status prediction models for noninvasive axillary staging using postoperative data or imaging features that are not part of the diagnostic workup. Lymphovascular invasion (LVI) is a top-ranked predictor of nodal metastasis; however, its preoperative assessment is challenging.Objective: This paper aimed to externally validate a multilayer perceptron (MLP) model for noninvasive lymph node staging (NILS) in a large population-based cohort (n=18,633) and develop a new MLP in the same cohort. Data were extracted from the Swedish National Quality Register for Breast Cancer (NKBC, 2014-2017), comprising only routinely and preoperatively available documented clinicopathological variables. A secondary aim was to develop and validate an LVI MLP for imputation of missing LVI status to increase the preoperative feasibility of the original NILS model. Methods: Three nonoverlapping cohorts were used for model development and validation. A total of 4 MLPs for nodal status and 1 LVI MLP were developed using 11 to 12 routinely available predictors. Three nodal status models were used to account for the different availabilities of LVI status in the cohorts and external validation in NKBC. The fourth nodal status model was developed for 80% (14,906/18,663) of NKBC cases and validated in the remaining 20% (3727/18,663). Three alternatives for imputation of LVI status were compared. The discriminatory capacity was evaluated using the validation area under the receiver operating characteristics curve (AUC) in 3 of the nodal status models. The clinical feasibility of the models was evaluated using calibration and decision curve analyses.Results: External validation of the original NILS model was performed in NKBC (AUC 0.699, 95% CI 0.690-0.708) with good calibration and the potential of sparing 16% of patients with node-negative disease from SLNB. The LVI model was externally validated (AUC 0.747, 95% CI 0.694-0.799) with good calibration but did not improve the discriminatory performance of the nodal status models. A new nodal status model was developed in NKBC without information on LVI (AUC 0.709, 95% CI: 0.688-0.729), with excellent calibration in the holdout internal validation cohort, resulting in the potential omission of 24% of patients from unnecessary SLNBs.Conclusions: The NILS model was externally validated in NKBC, where the imputation of LVI status did not improve the model's discriminatory performance. A new nodal status model demonstrated the feasibility of using register data comprising only the variables available in the preoperative setting for NILS using machine learning. Future steps include ongoing preoperative validation of the NILS model and extending the model with, for example, mammography images. © Malin Hjärtström, Looket Dihge, Pär-Ola Bendahl, Ida Skarping, Julia Ellbrant, Mattias Ohlsson, Lisa Rydén.
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| 10. |
- Kjærgaard, Kasper, et al.
(författare)
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Impact of type 2 diabetes on complications after primary breast cancer surgery : Danish population-based cohort study
- 2024
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Ingår i: British Journal of Surgery. - 0007-1323. ; 111:3
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Tidskriftsartikel (refereegranskat)abstract
- Background: Knowledge is sparse on the impact of type 2 diabetes (T2D) on surgical outcomes after breast cancer surgery. This study investigated the association between T2D and risk of complications after primary breast cancer surgery, and evaluated the biological interaction between T2D and co-morbidities. Methods: Using the Danish Breast Cancer Group clinical database, a cohort of all Danish women diagnosed with early-stage breast cancer during 1996-2022 was created. All patients underwent mastectomy or breast-conserving surgery. Information on prevalent T2D was collected from Danish medical and prescription registries. Surgical complications were defined as hospital diagnoses for medical or surgical complications developing within 30 days after primary breast cancer surgery. The 30-day cumulative incidence proportion of complications was calculated, and Cox regression was used to estimate HRs. Interaction contrasts were computed to determine the additive interaction between T2D and co-morbidities on the incidence rate of complications. Results: Among 98 589 women with breast cancer, 6332 (6.4%) had T2D at breast cancer surgery. Overall, 1038 (16.4%) and 9861 (10.7%) women with and without T2D developed surgical complications, yielding cumulative incidence proportions of 16 (95% c.i. 15 to 17) and 11 (10 to 11)% respectively, and a HR of 1.43 (95% c.i. 1.34 to 1.53). The incidence rate of surgical complications explained by the interaction of T2D with moderate and severe co-morbidity was 21 and 42%, respectively. Conclusion: Women with breast cancer and T2D had a higher risk of complications after primary breast cancer surgery than those without T2D. A synergistic effect of T2D and co-morbidity on surgical complications can explain this association.
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