Chemotherapy In Breast Cancer Patients Research Articles

Predictive model for pancreatic steatosis after neoadjuvant chemotherapy in breast cancer: Construction and ultrasound validation

ObjectiveTo develop and verify a nomogram model for predicting occurrence of pancreatic steatosis (PS) after chemotherapy in breast cancer patients. MethodsA total of 215 breast cancer (BC) patients who underwent neoadjuvant chemotherapy in the hospital from February 2018 to November 2018 were prospectively collected for imaging and clinical data. Subjects were randomly divided into a training set (172 cases) and a validation set (43 cases) according to a random number table in a 4:1 ratio, and independent risk factors associated with PS were screened and modeled in a nomogram model after using single-factor and multifactorial logistic regression analyses on the training set. The model's performance was evaluated through various analyses, including subject operating characteristic curve(ROC), calibration curves, decision curve (DCA), and clinical impact curve (CIC), assessing its predictive performance, precision, and practical value in a clinical setting. ResultsThe results of univariate and multifactorial binary logistic regression analyses showed that age, triglycerides, and whether or not neoadjuvant chemotherapy was an independent risk factor for the development of PS, and these three independent risk factors were used to further construct a nomogram model. The model demonstrated strong predictive efficacy with an area under the ROC curve of 0.817 (95% CI = 0.753–0.880) in the training set and 0.762 (95% CI = 0.615–0.901) in the validation set. Calibration curve analysis confirmed accurate prediction of PS, and both DCA and CIC confirmed the model's clinical utility and validity. ConclusionThe nomogram model developed in this study shows strong prediction effect, discriminatory power, and clinical applicability, serving as an intuitive and visual tool for assessing the risk of PS in breast cancer chemotherapy patients.

Sanguinarine highly sensitises breast cancer cells to doxorubicin-induced apoptosis

Breast cancer is the most commonly diagnosed cancer and the second most common cause of cancer death in women. The anthracycline, doxorubicin, is a well-known and highly effective treatment for breast cancer patients; however, many patients present with resistance to chemotherapeutic drugs, which ultimately results in treatment failure and contributes to high mortality rates. It is well established that the mitogen-activated protein kinase phosphatase 1 (MKP-1) mediates the response to chemotherapy, where upregulated MKP-1 is associated with chemoresistance. We investigated whether MKP-1 inhibition or silencing can sensitise triple-negative MDA-MB-231 breast cancer cells to doxorubicin therapy. We found that MKP-1 inhibition and silencing sensitises breast cancer cells to doxorubicin-induced apoptosis. Additionally, the inhibition of MKP-1 in combination with doxorubicin treatment promotes autophagy induction, while doxorubicin and not MKP-1 modulation increased lysosomal acidic compartments. As such, this study demonstrated that MKP-1 inhibition has a potential therapeutic benefit for breast cancer patients by increasing the efficacy of conventional chemotherapy. Therefore, MKP-1 inhibition should be developed as a clinically relevant adjuvant therapy, which could provide a novel avenue for therapeutic intervention in combination with chemotherapy in breast cancer patients.

Predicting pathological complete response to neoadjuvant chemotherapy in breast cancer patients: use of MRI radiomics data from three regions with multiple machine learning algorithms

ObjectiveTo construct a multi-region MRI radiomics model for predicting pathological complete response (pCR) in breast cancer (BCa) patients who received neoadjuvant chemotherapy (NACT) and provide a theoretical basis for the peritumoral microenvironment affecting the efficacy of NACT.MethodsA total of 133 BCa patients who received NACT, including 49 with confirmed pCR, were retrospectively analyzed. The radiomics features of the intratumoral region, peritumoral region, and background parenchymal enhancement (BPE) were extracted, and the most relevant features were obtained after dimensional reduction. Then, combining different areas, multivariate logistic regression analysis was used to select the optimal feature set, and six different machine learning models were used to predict pCR. The optimal model was selected, and its performance was evaluated using receiver operating characteristic (ROC) analysis. SHAP analysis was used to examine the relationship between the features of the model and pCR.ResultsFor signatures constructed using three individual regions, BPE provided the best predictions of pCR, and the diagnostic performance of the intratumoral and peritumoral regions improved after adding the BPE signature. The radiomics signature from the combination of all the three regions with the XGBoost machine learning algorithm provided the best predictions of pCR based on AUC (training set: 0.891, validation set: 0.861), sensitivity (training set: 0.882, validation set: 0.800), and specificity (training set: 0.847, validation set: 0.84). SHAP analysis demonstrated that LZ_log.sigma.2.0.mm.3D_glcm_ClusterShade_T12 made the greatest contribution to the predictions of this model.ConclusionThe addition of the BPE MRI signature improved the prediction of pCR in BCa patients who received NACT. These results suggest that the features of the peritumoral microenvironment are related to the efficacy of NACT.

Impact of residual microcalcifcations on prognosis after neoadjuvant chemotherapy in breast cancer patients

BackgroundResidual microcalcifications after neoadjuvant chemotherapy (NAC) are challenging for deciding extent of surgery and questionable for impact on prognosis. We investigated changes in the extent and patterns of microcalcifications before and after NAC and correlated them with pathologic response. We also compared prognosis of patients depending on presence of residual microcalcifications after NAC.MethodsA total of 323 patients with invasive breast carcinoma treated with neoadjuvant chemotherapy at Kangbuk Samsung Hospital and Samsung Medical center from March 2015 to September 2018 were included. Patients were divided into four groups according to pathologic response and residual microcalcifications. Non-pCRw/mic group was defined as breast non-pCR with residual microcalcifications. Non-pCRw/o mic group was breast non-pCR without residual microcalcifications. pCRw/mic group was breast pCR with residual microcalcifications. pCRw/o mic group was breast pCR without residual microcalcifications. The first aim of this study is to investigate changes in the extent and patterns of microcalcifications before and after NAC and to correlate them with pathologic response. The second aim is to evaluate oncologic outcomes of residual microcalcifications according to pathologic response after NAC.ResultsThere were no statistical differences in the extent, morphology, and distribution of microcalcifications according to pathologic response and subtype after NAC (all p > 0.05). With a median follow-up time of 71 months, compared to pCRw/o mic group, the hazard ratios (95% confidence intervals) for regional recurrence were 5.190 (1.160–23.190) in non-pCRw/mic group and 5.970 (1.840–19.380) in non-pCRw/o mic group. Compared to pCRw/o mic group, the hazard ratios (95% CI) for distant metastasis were 8.520 (2.130–34.090) in non-pCRw/mic group, 9.120 (2.850–29.200) in non-pCRw/o mic group. Compared to pCRw/o mic, the hazard ratio (95% CI) for distant metastasis in pCRw/mic group was 2.240 (0.230–21.500) without statistical significance (p = 0.486).ConclusionsRegardless of residual microcalcifications, patients who achieved pCR showed favorable long term outcome compared to non-pCR group.

O152: Prognostic value of receptor positivity loss following neoadjuvant chemotherapy in breast cancer patients: a systematic review and meta-analysis

Abstract Introduction Receptor status of breast cancer (BC) has been reported to change after neoadjuvant treatment leading to discordance between diagnostic biopsy and surgical specimens. This meta-analysis aims to investigate the prognostic significance of a loss of human epidermal growth factor receptor 2 (HER2) and hormone receptor positivity in residual disease after neoadjuvant therapy. Methods A systematic search of electronic databases was conducted to identify studies that investigated the impact loss of receptor status had on prognosis. Studies reporting disease free survival (DFS) and/or overall survival (OS) hazard ratios (HRs) for loss of HER2 or hormone receptor positivity following neoadjuvant treatment for BC were included. A meta-analysis was undertaken for relevant studies. Pooled hazard ratios were calculated using 95% confidence intervals (CIs). Results Eight studies with a total of 2673 patients were included in the meta-analysis. Patients who lost HER2 positivity had significantly worse OS (pooled HR 1.80, 95% CI 1.01-3.22, p=0.05) and DFS (pooled HR 2.03, 95% CI 1.47-2.82, p<0.001), compared to those who maintained positivity. Similarly, patients with a loss of hormone receptor positivity had significantly worse OS (pooled HR 2.49, 95% CI 1.65-3.77, p<0.001) and DFS (pooled HR 3.71, 95% CI 1.79-7.69, p<0.001) than those who remained hormone receptor positive after neoadjuvant therapy. Conclusion This meta-analysis found that a loss of either HER2 or hormone receptor positivity following neoadjuvant therapy negatively impacted survival outcomes. These findings suggest that retesting of receptor status may offer prognostic information, in addition to potentially altering adjuvant treatment regimens.

Cardiotoxicity of Anthracycline-based Chemotherapy in Breast Cancer Patients: A Case Series

In Kazakhstan, breast cancer (BC) remains the leading cause of cancer morbidity and mortality among women. The presented case series aims to summarize cardiovascular events that resulted in anthracycline-based chemotherapy discontinuation or suspension during the ongoing project on studying the cardiotoxicity effects. Case 1. Classic acute cardiotoxicity with asystole. Patient Sh., 46 years old, was admitted with a baseline LVEF of 64% and GLS of 22.4%. After the first dose of doxorubicin 60 mg/m2 and cyclophosphamide 600 mg/m2, the patient experienced two episodes of asystole. She was prescribed Trimetazidine at a dose of 80 mg. Eventually, Sh. completed the courses of anthracycline therapy after a 1-month delay at a cumulative dose of 455 mg/m2. Case 2. Subacute cardiotoxicity with ventricular extrasystole. Patient Zh., aged 47, developed single, paired, and group ventricular extrasystoles after the 2nd course of chemotherapy with doxorubicin 200 mg/m2 (23 days after admission). Carvedilol was prescribed at 25 mg twice daily and Trimetazidine at 80 mg once a day. After 1 month of monitoring, ventricular extrasystoles disappeared. With a month’s delay, the patient completed chemotherapy at a cumulative dose of 400 mg/m2. Case 3. Severe cardiotoxicity due to pre-existing cardiovascular disease with discontinuation of chemotherapy. Patient M., aged 58, was referred to the very highrisk group for developed atrial fibrillation and heart failure with LVEF 51%. M. received Enalapril 5 mg two times per day, Bisoprolol 5 mg, Eplerenone 50 mg, Dapagliflozin 10 mg, and Dabigatran 150 mg twice daily. After 3 months, anthracycline therapy was canceled at a cumulative dose of 260 mg/m2 due to the deterioration of the patient’s condition (LVEF 41%.). Discontinuation or the delay of vitally needed chemotherapy in BC patients deteriorate their prognosis for survival. Patients should be constantly monitored during and after anticancer treatment.

Pathological complete response and associated factors in breast cancer after neoadjuvant chemotherapy: A retrospective study

Objective: This study aimed to determine clinical and pathological factors that identify a pathological complete response (pCR) in breast cancer patients undergoing neoadjuvant chemotherapy (NAC). Material and Methods: A retrospective, single-center study was conducted in women over the age of 18 who had been diagnosed with pathologically confirmed invasive breast cancer and who had received NAC between July 2016 and October 2021. Patient demographics, clinical, radiological, treatment, and pathological data were reviewed from the electronic hospital records. The primary outcome of interest was pCR, defined as the absence of residual invasive breast cancer in both the breast and axillary lymph nodes. Multivariable logistic regression analysis was used to identify factors associated with pCR. Results: A total of 119 patients were included in the analysis. The distribution of age was 54.5 ± 11.5 years. pCR was observed in 33 (27.7%) patients. pCR for breast tissue was observed in 43 (36.1%) patients. There was no statistically significant relation between the clinical stage and pCR. Age, age at first labor, extent of disease in the breast, NAC completeness, clinical tumor size (cT) stage, clinical lymph node (cN) stage, and molecular subtype were analyzed in a multivariable model. Analysis showed that molecular subtype was the only independent factor related to pCR. pCR rates across molecular subtypes were: 8.7% in luminal-A, 10.8% in luminal-B, 54.5% in human epidermal growth factor receptor 2 (HER-2)-positive, 42.4% in luminal-B (HER-2 positive) and 46.7% in triple-negative. There was no statistically significant difference between luminal-A and luminal-B subgroups (odds ratio 1.15, 95% confidence interval, 0.19-9.35, p= 0.881). Despite the limited number of patients in HER2-positive and triple-negative groups, both demonstrated statistically significant higher odds compared to reference group. Conclusion: The presented study underscores the relevance of molecular subtypes in determining the response to neoadjuvant chemotherapy in breast cancer patients. Particularly HER2-positive and triple-negative subtypes may demonstrate more favorable response rates.

Clipping a Positive Lymph Node Improves Accuracy of Nodal Staging After Neoadjuvant Chemotherapy for Breast Cancer Patients, but Does It Drive Management Changes?

Sentinel lymph node (SLN) biopsy for cN+ breast cancer patients after neoadjuvant chemotherapy (NAC) is controversial because the false-negative rate (FNR) is high. Identification of three or more SLNs with a dual tracer improves these results, and inclusion of a clipped lymph node (CLN) (targeted axillary dissection [TAD]) may be even more effective. A retrospective, single-institution analysis of consecutive cN+ patients undergoing NAC from 2019 to 2021 was performed. Patients routinely underwent placement of a clip in the positive lymph node before NAC, and TAD was performed after completion of therapy. The study analyzed 73 patients, and the identification rate for CLN was 98.6% (72/73). A complete response in the lymph nodes was achieved for 43 (59%) of the 73 patients. Overall, the CLN was not a SLN in 18 (25%) of 73 cases, and for women who had one or two and those who had three or more SLNs identified, this occurred in 11 (32%) and 7 (21%) of 34 cases, respectively. Failure of SLN or TAD to identify a positive residual lymph node status after NAC occurred in 10 (15%) of 69 and 2 (3%) of 73 cases, respectively (p = 0.01). In four cases, a SLN was not retrieved (5.5%), and two of these cases had a positive CLN. In three cases, the CLN was the only positive node and did not match with a SLN, directing lymphadenectomy and oncologic management change in two cases. Therefore, 7 (10%) of 73 cases had a change in surgical or oncologic management with TAD. For a conservative axillary treatment in this setting, TAD is an effective method. It is more accurate than SLN alone and allows management changes. Further studies are warranted.

Radiomic model based on magnetic resonance imaging for predicting pathological complete response after neoadjuvant chemotherapy in breast cancer patients.

To establish a model combining radiomic and clinicopathological factors based on magnetic resonance imaging to predict pathological complete response (pCR) after neoadjuvant chemotherapy in breast cancer patients. MRI images and clinicopathologic data of 329 eligible breast cancer patients from the Affiliated Hospital of Qingdao University from August 2018 to August 2022 were included in this study. All patients received neoadjuvant chemotherapy (NAC), and imaging examinations were performed before and after NAC. A total of 329 patients were randomly allocated to a training set and a test set at a ratio of 7:3. We mainly studied the following three types of prediction models: radiomic models, clinical models, and clinical-radiomic models. All models were evaluated using subject operating characteristic curve analysis and area under the curve (AUC), decision curve analysis (DCA) and calibration curves. The AUCs of the clinical prediction model, independent imaging model and clinical combined imaging model in the training set were 0.864 0.968 and 0.984, and those in the test set were 0.724, 0.754 and 0.877, respectively. According to DCA and calibration curves, the clinical-radiomic model showed good predictive performance in both the training set and the test set, and we found that we had developed a more concise clinical-radiomic nomogram. We have developed a clinical-radiomic model by integrating radiomic features and clinical factors to predict pCR after NAC in breast cancer patients, thereby contributing to the personalized treatment of patients.

Left ventricular mechanical dyssynchrony after chemotherapy in breast cancer patients with normal rest gated SPECT-MPI.

Early diagnosis of chemotherapy-induced cardiotoxicity plays an important role in preventing heart failure. The main aim of our study was to assess left ventricular (LV) dyssynchrony measured by phase analysis of gated single-photon emission computed tomography (SPECT) as an early sign of cardiotoxicity after breast cancer chemotherapy. This cross-sectional study was conducted on patients with stage ≤ 3 breast cancer and no history of cardiovascular disease or diabetes. After mastectomy, the patients underwent rest gated SPECT myocardial perfusion imaging (MPI). Sixty patients with normal gated SPECT-MPI were selected and the imaging was performed after chemotherapy with doxorubicin, cyclophosphamide and paclitaxel. LV function and contractility parameters were extracted by QGS software and the results were compared with the t test method. The abnormality of at least one of the three phase analysis indices was considered as left ventricular dyssynchrony (LVD). The average LV end-systolic volume and ejection fraction (LVEF) before and after chemotherapy were (16.2 ± 8.0ml and 21.6 ± 11.6ml) and (73.4 ± 7.9% and 67.5 ± 9.2%) respectively, which showed a significant decrease (P < 0.05). In 2 patients (3.3%), the LVEF decreased to less than 50% after chemotherapy. The average parameters of left ventricular contractility before and after chemotherapy were, respectively, as follows: PHB (24.1 ± 7.5 and 33.8 ± 16.4), PSD (9.4 ± 6.1 and 5.7 ± 1.9) and entropy (28.9 ± 7.1 and 35.6 ± 9.7), which showed a significant increase (P < 0.05). LVD was observed in 14 patients (23.4%) after chemotherapy and prevalence of LVD was significantly higher in patients who had received a cumulative dose of doxorubicin of more than 400mg/m2 (P = 0.005). There was no relationship between age and body mass index with the incidence of LVD after chemotherapy (P > 0.05). Using phase analysis of gated SPECT-MPI, chemotherapy-induced LVD was seen in a significant number of patients with breast cancer, especially with a high cumulative dose of doxorubicin. LVD might indicate chemotherapy-induced cardiotoxicity before LVEF becomes abnormal.

Multiparametric MRI-based radiomics combined with pathomics features for prediction of the efficacy of neoadjuvant chemotherapy in breast cancer

PurposeThe aim of this study is to investigate a new method that combines radiological and pathological breast cancer information to predict discrepancies in pathological responses for individualized treatment planning. We used baseline multiparametric magnetic resonance imaging and hematoxylin and eosin-stained biopsy slides to extract quantitative feature information and predict the pathological response to neoadjuvant chemotherapy in breast cancer patients. MethodsWe retrospectively collected data from breast cancer patients who received neoadjuvant chemotherapy in our hospital from August 2016 to January 2018; multiparametric magnetic resonance imaging (contrast-enhanced T1-weighted imaging and diffusion-weighted imaging) and whole slide image of hematoxylin and eosin-stained biopsy sections were collected. Quantitative imaging features were extracted from the multiparametric magnetic resonance imaging and the whole slide image were used to construct a radiopathomics signature model powered by machine learning methods. Models based on multiparametric magnetic resonance imaging or whole slide image alone were also constructed for comparison and referred to as the radiomics signature and pathomics signature models, respectively. Four modeling methods were used to establish prediction models. Model performances were evaluated using receiver operating characteristic curve analysis and the area under the curve, accuracy, sensitivity, specificity, positive predictive value, and negative predictive value. ResultsThe radiopathomics signature model had favourable performance for the prediction of pathological complete response in the training set (the best value: area under the curve 0.83, accuracy 0.84, and sensitivity 0.87), and in the test set (the best value: area under the curve 0.91, accuracy 0.90, and sensitivity 0.88). In the test set, the radiopathomics signature model also significantly outperformed the radiomics signature (the best value: area under the curve 0.83, accuracy 0.64, and sensitivity 0.62), pathomics signature (the best value: area under the curve 0.60, accuracy 0.74, and sensitivity 0.62) (p > 0.05). Decision curve analysis and calibration curves confirmed the excellent performance of these prediction models in discrimination, calibration, and clinical usefulness. ConclusionsThe results of this study suggest that radiopathomics, the combination of both radiological information regarding the whole tumor and pathological information at the cellular level, could potentially predict discrepancies in pathological response and provide evidence for rational treatment plans.

Targeting P53 Gene in Breast Cancer

Infectious diseases and breast cancer rely heavily on p53, a tumor suppressor that protects genetic integrity. It is present on chromosome 17p13 and controls angiogenesis, programmed cell death, and cell proliferation. The expression of p53 and its isoforms is more predictive of treatment outcomes and responses to chemotherapy for breast cancer patients than the presence or absence of TP53 mutations. Subtype-dependent mutations of p53's negative regulator MDM2 can both be signs of p53 deactivation in breast cancer. Targeting both wild-type and p53 mutant across various subtypes of breast cancer is one possible treatment strategy. Notably, through paracrine signaling, mutant p53 in stromal fibroblasts speeds up the formation of breast tumors. In the progression and trajectory of breast cancer, the pivotal involvement of the p53 gene, alongside the bcl-2 proto-oncogene, is noteworthy. Addressing the role of p53 is crucial in the context of breast cancer development and its course.

FAKTOR USIA BERHUBUNGAN DENGAN KECEMASAN PADA PASIEN KANKER PAYUDARA YANG MENJALANI KEMOTERAPI

Breast cancer is a disease caused by irregular division of body cells so that cell growth cannot be controlled and will grow into a tumor lump. The need for cancer therapy is increasing along with the increasing prevalence of breast cancer incidence. One of the effects of chemotherapy for breast cancer patients is anxiety. Anxiety is a vague fear and feelings of insecurity, isolation, helplessness, and uncertainty. This condition often arises due to a fear of imagining the side effects of chemotherapy and the future due to the disease will cause changes in his life. The purpose of this study was to determine the relationship with anxiety in breast cancer patients undergoing chemotherapy at Baladhika Husada Jember Hospital. The design used in this study was cross sectional. The population in this study was 311 people and the sample used was 76 people. The sampling technique used is probability sampling with a simple random sampling method. Based on the chi square test, it is known that the p&lt;0.001 (p&lt;0.05), meaning that there is a significant relationship between age and anxiety in breast cancer undergoing chemotherapy at Baladhika Husada Jember Hospital.

Delta Radiomics Based on Longitudinal Dual-modal Ultrasound Can Early Predict Response to Neoadjuvant Chemotherapy in Breast Cancer Patients

To develop a monitoring model using radiomics analysis based on longitudinal B-mode ultrasound (BUS) and shear wave elastography (SWE) to early predict pathological response to neoadjuvant chemotherapy (NAC) in breast cancer patients. In this prospective study, 112 breast cancer patients who received NAC between September 2016 and March 2022 were included. The BUS and SWE data of breast cancer were obtained prior to treatment as well as after two and four cycles of NAC. Radiomics features were extracted followed by measuring the changes in radiomics features compared to baseline after the second and fourth cycles of NAC(△R[C2], △R [C4]), respectively. The delta radiomics signatures were established using a support vector machine classifier. The area under receiver operating characteristic curve (AUC) values of △RBUS (C2) and △RBUS (C4) for predicting the response to NAC were 0.83 and 0.84, while those of △RSWE (C2) and △RSWE (C4) were 0.88 and 0.90, respectively. △RSWE exhibited significantly superior performance to △RBUS for predicting NAC response (Delong test, p<0.01). No significant differences were observed in the performances between △R (C2) and △R (C4) based on BUS or SWE data. The longitudinal dual-modal ultrasound radiomics (LDUR) model had an excellent discrimination, good calibration and clinical usefulness, with the AUC, sensitivity and specificity of 0.97, 95.52% and 91.11%, respectively. The LDUR model achieved excellent performance in predicting the pathological response to chemotherapy during the early stages of NAC for breast cancer.

The Role of Ultrasound in Axillary Lymph Node Response to Neoadjuvant Chemotherapy in Breast Cancer Patients

The Role of Ultrasound in Axillary Lymph Node Response to Neoadjuvant Chemotherapy in Breast Cancer Patients

Relative dose intensity of taxane-based chemotherapy in breast cancer patients in a tertiary hospital.

Breast cancer (BC) is the most diagnosed tumor among women worldwide. The aim of this study was to investigate the incidence and causes of low relative dose intensity (RDI) < 85% for taxane-based chemotherapy regimens used in the treatment of BC in Sultan Qaboos University Hospital (SQUH). This was a retrospective study that included 303 BC patients, treated with taxane-based chemotherapy protocols at SQUH. RDI was calculated for each chemotherapy regimen and causes and predictors of low RDI < 85% were identified. Prophylactic and therapeutic supportive measures for certain toxicities were studied. 50.8% of the patients had neoadjuvant chemotherapy, 38% had adjuvant chemotherapy, and 11.2% of patients were given palliative treatment. AC-T and AC-THP were the most used regimens (40.3% and 17.2%). Mean RDI of used taxane-based chemotherapy regimens was 93.4%. Dose delays, dose reductions, and treatment discontinuation occurred in 36.6%, 14.8%, and 11.5%, respectively. Thirty-eight patients (12.5%) had low RDI < 85% which was reduced to 9.9% after the use of an alternative taxane. Age and chemotherapy intent were significant risk factors. 83.8% received primary granulocyte colony stimulating factor. An optimal RDI greater than 85% was achieved in most cases. Furthermore, prophylactic and therapeutic supportive measures were widely used.

Machine learning applied in patient-reported outcome research-exploring symptoms in adjuvant treatment of breast cancer.

Patient-reported outcome (PRO) data may help us better understand the life of breast cancer patients. We have previously collected PRO data in a national Danish breast cancer study in patients undergoing adjuvant chemotherapy. The aim of the present post-hoc explorative study is to apply Machine Learning (ML) algorithms using permutation importance to explore how specific PRO symptoms influence nonadherence to six cycles of planned adjuvant chemotherapy in breast cancer patients. We here investigate ePRO-data from the 347 patients. The ePRO presented 42 PROCTCAE questions on 25 symptoms. Patients completed the ePRO before each cycle of chemotherapy. Number of patients with completion of the scheduled six cycles of chemotherapy were registered. Two ML models were applied. One aimed at discovering the individual relative importance of the different questions in the dataset while the second aimed at discovering the relationships between the questions. Permutation importance was used. Out of 347 patients 238 patients remained in the final dataset, 15 patients dropped out. Two symptoms: aching joints and numbness/tingling, were the most important for dropout in the final dataset, each with an importance value of about 0.04. Model's average ROC-AUC-score being 0.706. In the second model a low performance score made the results very unreliable. In conclusion, this explorative data analysis using ML methodologies in an ePRO dataset from a population of women with breast cancer treated with adjuvant chemotherapy unravels that the symptoms aching joints and numbness/tingling could be important for drop out of planned adjuvant chemotherapy.

68Ga-Labeled Fibroblast Activation Protein Inhibitor PET/CT for the Early and Late Prediction of Pathologic Response to Neoadjuvant Chemotherapy in Breast Cancer Patients: A Prospective Study.

68Ga-labeled fibroblast activation protein inhibitor (68Ga-FAPI) PET/CT has demonstrated promising clinical results, with a higher SUVmax and tumor-to-background ratio (TBR) in breast cancer (BC) patients than 18F-FDG PET/CT. Here, we aimed to evaluate the suitability of 68Ga-FAPI PET/CT for the early and late prediction of the pathologic response to neoadjuvant chemotherapy (NAC) in BC. Methods: Twenty-two consecutive patients with newly diagnosed BC and an indication for NAC were prospectively included. All patients underwent standard chemotherapy and 68Ga-FAPI PET/CT at baseline, after 2 cycles of NAC (PET2), and 1 wk before surgery (PET3). SUVmax was measured in the primary tumor region and positive regional lymph nodes. The expression of fibroblast activation protein in the primary lesion was analyzed by immunohistochemistry. Results: Seven patients (31.8%) achieved a pathologic complete response (pCR), and 15 (68.2%) had residual tumors. Thirteen patients (59.1%) showed concentric withdrawal of the primary tumor, and 9 (40.9%) showed diffuse withdrawal. Between PET2 and PET3, the ΔSUVmax of the primary tumor (R 2 = 0.822; P = 0.001) and metastatic lymph nodes (R 2 = 0.645; P = 0.002) were significantly correlated. The absolute values of SUVmax and TBR at PET2 and PET3 were lower in patients with pCR than in those without pCR (P < 0.05). Moreover, a larger ΔSUVmax at any time point was strongly associated with pCR (P < 0.05). Similar downward trends in SUVmax, TBR, and ΔSUVmax were observed in the pattern of primary tumor reduction. For predicting pCR, the optimal cutoff values for ΔSUVmax after 2 chemotherapy cycles, ΔSUVmax before surgery, TBR after 2 chemotherapy cycles, and TBR before surgery of the primary tumor were 3.4 (area under the curve [AUC], 0.890), 1.1 (AUC, 0.978), -63.8% (AUC, 0.879), -90.8% (AUC, 0.978), 7.6 (AUC, 0.848), and 1.4 (AUC, 0.971), respectively. Immunohistochemistry showed that the SUVmax and TBR of 68Ga-FAPI PET/CT were positively correlated with fibroblast activation protein expression (P < 0.001 for both). Conclusion: Assessment of early changes in 68Ga-FAPI uptake during NAC by 68Ga-FAPI PET/CT can predict pCR and primary tumor concentric withdrawal in BC patients. 68Ga-FAPI PET/CT has great potential for the early and late prediction of the pathologic response to NAC in BC.

MO9-4 Cognitive decline after receiving endocrine therapy and/or chemotherapy in breast cancer patients: A systematic review

MO9-4 Cognitive decline after receiving endocrine therapy and/or chemotherapy in breast cancer patients: A systematic review

Profile of Cytostatic Drug Use in Breast Cancer Patients (Mammae carcinoma) at Medan Hospital

Breast cancer is the most common type of cancer in the world (2,261,419 cases) and in Indonesia (65,858 cases). This research aims to determine the class and type of cytostatic drugs most frequently used in breast cancer chemotherapy patients from January to March 2023 at RSUP Haji Adam Malik Medan. This study used a descriptive survey method with a saturated sample of 176 medical records. Data collection was done retrospectively by gathering information from the electronic medical records of the patients. The research results show that the characteristics of the patients are as follows: the majority were female, totalling 176 patients (100%). The most common age group was 45-54 years, with 111 patients (63.1%), and the prescription of cytostatic drugs was highest in March, with 80 patients (36.9%). In conclusion, the most commonly used class of cytostatic drugs was antibiotics, with 146 cases (32.88%), and the most commonly used type of cytostatic drug was Doxorubicin, with 114 cases (25.68%). This indicates that the use of antibiotics is a significant part of breast cancer treatment, and it is important to pay attention to the safety and regularity of drug use to prevent drug resistance and the severity of the disease.