ObjectiveTo investigate the effect and predictive value of systemic inflammatory markers on pathological complete response (pCR) after neoadjuvant chemotherapy (NACT) for locally advanced breast cancer (LABC). MethodsThe clinicopathologic data of female patients with LABC who received NACT and radical surgical resection in the Department of Breast Surgery, Affiliated Hospital of Southwest Medical University from February 2019 to February 2022 were retrospectively analyzed. The factors affecting pCR after NACT were analyzed by the multivariate logistic regression and the prediction model was established. The efficiency of the prediction model was evaluated by receiver operating characteristic (ROC) curve and area under the ROC curve (AUC). ResultsA total of 98 patients were gathered, of which 29 obtained pCR, with a pCR rate of 29.6%. The multivariate analysis of binary logistic regression showed that the patients with non-menopausal status, negative estrogen receptor (ER), chemotherapy+targeted therapy, and systemic immune-inflammation index (SII) <532.70 (optimal critical value) were more likely to obtain pCR after NACT (P<0.05). The prediction model was established according to logistic regression analysis: Logit (P)=0.697–2.974×(menopausal status)–1.932×(ER status)+3.277×(chemotherapy regimen)–2.652×(SII). The AUC (95%CI) of the prediction model was 0.914 (0.840, 0.961), P<0.001. ConclusionsIt is not found that other inflammatory indicators such as neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, and lymphocyte-to-monocyte ratio are associated with pCR after NACT. But SII is an important predictor of pCR after NACT for LABC and has a good predictive efficiency.
Objective To explore the accuracy of contrast-enhanced magnetic resonance imaging (MRI) in predicting pathological complete remission (pCR) in breast cancer patients after neoadjuvant therapy (NAC). Methods The clinicopathological data of 245 patients with invasive breast cancer who had completed the surgical resection after NAC in the Affiliated Hospital of Southwest Medical University from March 2020 to April 2022 were collected retrospectively. According to the results of hormone receptor (HR) and human epidermal growth factor receptor 2 (HER2) detected by immunohistochemistry, all patients were divided into four subgroups: HR+/HER2–, HR+/HER2+, HR–/HER2+ and HR–/HER2–. The value of MRI in evaluating the efficacy of NAC was analyzed by comparing the postoperative pathological results as the gold standard with the residual tumor size assessed by preoperative MRI. Meanwhile, the sensitivity, specificity and positive predictive value (PPV) of pCR predicted by the evaluation results of enhanced MRI were analyzed, and further analyzed its predictive value for pCR of different subtypes of breast cancer. Results There were 88 cases (35.9%) achieved radiological complete response (rCR) and 106 cases (43.3%) achieved pCR in 245 patients. Enhanced MRI in assessing the size of residual tumors overestimated and underestimated 12.7% (31/245) and 9.8% (24/245) of patients, respectively. When setting rCR as the MRI assessment index the specificity, sensitivity and PPV were 84.2% (117/139), 62.3% (66/106) and 75.0% (66/88), respectively. When setting near-rCR as the MRI assessment index the specificity, sensitivity and PPV were 70.5% (98/139), 81.1% (86/106), and 67.7% (86/127), respectively. The positive predictive value of both MRI-rCR and MRI-near-rCR in evaluating pCR of each subtype subgroup of breast cancer was the highest in the HR–/HER2+ subgroup (91.7% and 83.3%, respectively). In each subgroup, compared with rCR, the specificity of near-rCR to predict pCR decreased to different degrees, while the sensitivity increased to different degrees. Conclusions Breast contrast-enhanced MRI can more accurately evaluate the efficacy of localized breast lesions after NAC, and can also more accurately predict the breast pCR after NAC. The HR–/HER2+ subgroup may be a potentially predictable population with pCR exemption from breast surgery. However, the accuracy of the evaluation of pCR by breast enhancement MRI in HR+/HER2– subgroup is low.
ObjectiveTo analyze the association between nutritional and immune-related laboratory indices and pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) in breast cancer patients and focused on constructing a combination of laboratory indices to serve as a clinical predictor of pCR after NAC in breast cancer. MethodsRetrospectively collected the pre-NAC laboratory indices [albumin (ALB), total cholesterol, triglyceride, high density lipoprotein cholesterol (HDL-C), low density lipoprotein cholesterol, apolipoprotein A- Ⅰ, apolipoprotein B, white blood cell, neutrophil, lymphocyte, monocyte (MON), and platelet ] and clinicopathologic data of 310 patients with invasive breast cancer who had received NAC in the Department of Breast Surgery, Affiliated Hospital of Southwest Medical University, from September 1, 2020 to October 31, 2022. Logistic regression analysis was conducted to determine the correlation between laboratory indices and post-NAC pCR. The combinations of laboratory indices were constructed by simple mathematical operation. The area under the receiver operating characteristic curve (AUC) was used to evaluate the efficacy of different combinations of laboratory indices in predicting pCR and to determine the optimal combination of liboratory indices. Multivariate logistic regression analysis was used to analysis the relevance between clinicopathologic features and post-NAC pCR in breast cancer patients and to determine the independent predictor of post-NAC pCR. ResultsAmong the 310 patients, 49.4% (153/310) of them achieved pCR after NAC. Logistic regression analysis revealed that ALB (Z=5.203, P<0.001) and HDL-C (Z=2.129, P=0.033) were positively correlated with post-NAC pCR, while MON (Z=–4.883, P<0.001) was negatively correlated with post-NAC pCR. The AUC analysis of 6 different combinations of laboratory indices showed that the ALB/MON combination (the optimal combination of liboratory indices) had the highest predictive performance (median AUC=0.708) and was determined to be the neoadjuvant therapy predictive index (NTPI). Multivariate logistic regression analysis showed that estrogen receptor (Z=–3.273, P=0.001), human epidermal growth factor 2 (Z=7.041, P<0.001), Ki-67 (Z=2.457, P=0.014), and NTPI (Z=4.661, P<0.001) were the independent predictors for post-NAC pCR. ConclusionNTPI could serve as a predictive index for post-NAC pCR in patients with breast cancer.
Objective To summarize the progress of biological indexes which could predict the efficiency of neoadjuvant chemotherapy for breast cancer. Methods Various related researches were collected to make a review. Results Many indexes linked to the efficiency of neoadjuvant chemotherapy for breast cancer according to several studies. According to many studies, indexes such as human epidermal growth factor receptor-2 (HER-2) gene, estrogen receptor (ER), progesterone receptor (PR), Ki-67, P53 gene, neutrophil to lymphocyte ratio (NLR), platelet level, and mean platelet volume (MPV) may have association with the outcome of neoadjuvant chemotherapy in treatment of breast cancer, and these factors maybe individual biomarkers to predict the efficiency of the treatment, but no coincident conclusion has been reached for these indexes. Conclusion The value of these indexes that predict the efficiency of neoadjuvant chemotherapy is not sure, further study need to be done to solve this topic.
目的探讨低位局部进展期直肠癌新辅助放化疗后完全缓解病例的进一步治疗方案及效果。 方法回顾性分析江苏省中医院肿瘤外科2008年1月至2010年5月期间行新辅助放化疗后初步判断达到病理完全缓解（pCR）的14例低位局部进展期直肠癌患者的临床资料。 结果14例患者中接受手术者10例，术后真正达到pCR者5例；术后2例复发或转移，其中死亡1例，1例带瘤生存，余8例患者均无瘤生存。未行手术的4例患者中，有3例复发或转移，其中2例死亡，1例带瘤生存；余1例无瘤生存。4例未行手术病例中CEA水平正常者（<5 μg/L）2例（1例复发或转移），CEA升高的2例均发生转移；10例手术病例中CEA水平正常者6例（均无瘤生存，4例真正达到pCR），升高者4例（1例真正达到pCR，2例复发或转移）。 结论接受新辅助放化疗后初步判断达到pCR的病例，尤其是CEA值高于正常者，应接受规范的全直肠系膜切除（TME）手术以达到根治的目的。
ObjectiveTo explore the value of a decision tree (DT) model based on CT for predicting pathological complete response (pCR) after neoadjuvant chemotherapy therapy (NACT) in patients with locally advanced rectal cancer (LARC).MethodsThe clinical data and DICOM images of CT examination of 244 patients who underwent radical surgery after the NACT from October 2016 to March 2019 in the Database from Colorectal Cancer (DACCA) in the West China Hospital were retrospectively analyzed. The ITK-SNAP software was used to select the largest level of tumor and sketch the region of interest. By using a random allocation software, 200 patients were allocated into the training set and 44 patients were allocated into the test set. The MATLAB software was used to read the CT images in DICOM format and extract and select radiomics features. Then these reduced-dimensions features were used to construct the prediction model. Finally, the receiver operating characteristic (ROC) curve, area under the ROC curve (AUC), sensitivity, and specificity values were used to evaluate the prediction model.ResultsAccording to the postoperative pathological tumor regression grade (TRG) classification, there were 28 cases in the pCR group (TRG0) and 216 cases in the non-pCR group (TRG1–TRG3). The outcomes of patients with LARC after NACT were highly correlated with 13 radiomics features based on CT (6 grayscale features: mean, variance, deviation, skewness, kurtosis, energy; 3 texture features: contrast, correlation, homogeneity; 4 shape features: perimeter, diameter, area, shape). The AUC value of DT model based on CT was 0.772 [95% CI (0.656, 0.888)] for predicting pCR after the NACT in the patients with LARC. The accuracy of prediction was higher for the non-PCR patients (97.2%), but lower for the pCR patients (57.1%).ConclusionsIn this preliminary study, the DT model based on CT shows a lower prediction efficiency in judging pCR patient with LARC before operation as compared with homogeneity researches, so a more accurate prediction model of pCR patient will be optimized through advancing algorithm, expanding data set, and digging up more radiomics features.
Objective To systematically evaluate the efficacy and safety of dose-dense neoadjuvant chemotherapy (ddNACT) and conventional neoadjuvant chemotherapy (cNACT) for locally advanced breast cancer (LABC). Methods PubMed, Embase, Web of Science, CNKI, Wanfang Data, and VIP databases were searched for randomized controlled trials (RCT) comparing ddNACT regimen with cNACT regimen for breast cancer. The time limit for retrieval was from establishment to March 1st, 2021. Two reviewers independently screened literatures, extracted data and assessed risk bias of included studies; then, meta-analysis was performed by using Stata 15.0 software. Results A total of 13 RCTs were included, including 3 258 patients, of which 1 625 patients received ddNACT and 1 633 patients received cNACT. The results of meta-analysis showed that the ddNACT regimen could improve the pathological complete response rate (pCR, P<0.001), objective response rate (ORR, P<0.001), and disease free survival (DFS, P=0.037) as compared with the cNACT regimen, there was no significant difference in the overall survival (OS) between the two groups (P=0.098). The incidences of grade 3 or 4 oral stomatitis (P=0.005) and neurotoxicity (P<0.001) were higher and the incidence of grade 3 or 4 neutropenia was lower (P=0.025) in the patients with ddNACT regimen, there were no significant differences in grade 3 or 4 thrombocytopenia (P=0.152), grade 3 or 4 anemia (P=0.123), chemotherapy completion rate (P=0.161) and breast conservative surgery rate (P=0.186) between the two groups. Patients with hormone receptor (HR) negative (HR–) were more likely to get pCR after neoadjuvant chemotherapy (P<0.001). ConclusionsCurrent evidence shows that the use of anthracycline/taxane-based ddNACT regimen in LABC patients can improve the pCR, ORR, and DFS as compared with cNACT regimen. The pCR after neoadjuvant chemotherapy in the patients with HR– is higher than that with HR+. Prophylactic use of granulocyte-colony stimulating factor could significantly reduce the incidence of neutropenia, and most patients are tolerant to ddNACT regimen, 2 regimens have similar chemotherapy completion rates.
ObjectiveTo summarize the current research progress in the prediction of the efficacy of neoadjuvant therapy of breast cancer based on the application of artificial intelligence (AI) and radiomics. MethodThe researches on the application of AI and radiomics in neoadjuvant therapy of breast cancer in recent 5 years at home and abroad were searched in CNKI, Google Scholar, Wanfang database and PubMed database, and the related research progress was reviewed. ResultsAI had developed rapidly in the field of medical imaging, and molybdenum target, ultrasound and magnetic resonance imaging combined with AI had been deepened and expanded in different degrees in the application research of breast cancer diagnosis and treatment. In the research of molybdenum target combined with AI, the high sensitivity of molybdenum target to microcalcification was mostly used to improve the accuracy of early detection and diagnosis of breast cancer, so as to achieve the clinical purpose of early detection and diagnosis. However, in terms of prediction of neoadjuvant efficacy research of breast cancer, ultrasound and magnetic resonance imaging combined with AI were more prevalent, and their popularity remained unabated. ConclusionIn the monitoring of neoadjuvant therapy for breast cancer, the use of properly designed AI and radiomics models can give full play to its role in the predicting the curative effect of neoadjuvant therapy, and help to guide doctors in clinical diagnosis and treatment and evaluate the prognosis of breast cancer patients.
ObjectiveTo observe the accuracy of magnetic resonance imaging (MRI) for predicting pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) in breast cancer, and to analyze the cause of the prediction error.MethodsData from 157 breast cancer patients who underwent NAC before surgery in Mianyang Central Hospital from January 2017 to January 2019 were analyzed. MRI parameters before and after NAC and pCR conditions were collected to analyze the parameters that produced false positives and false negatives.ResultsOf the 157 patients, 37 (23.6%) achieved pCR after NAC, and 33 (21.0%) achieved radiation complete remission (rCR) after NAC. The accuracy of MRI prediction was 70.7% (111/157), the sensitivity was 82.5% (99/120), and the specificity was 32.4% (12/37). A total of 25 cases did not achieve rCR, but postoperative evaluation achieved pCR (false positive), 21 cases achieved rCR, but postoperative evaluation did not achieve pCR (false negative). Diameter of tumor, peritumoral oedema, and background parenchymal enhancement were associated with MRI false positive prediction (P<0.05); gland density and tumor rim enhancement were associated with MRI false negative prediction (P<0.05).ConclusionMRI can be used as an important method to predict pCR after NAC in breast cancer patients, and its accuracy may be related to diameter of tumor, peritumoral oedema, background parenchymal enhancement, gland density, and tumor rim enhancement.