Neoadjuvant Chemotherapy Response Research Articles

Glutathione dynamics is a potential predictive and therapeutic trait for neoadjuvant chemotherapy response in bladder cancer

Radical cystectomy with preoperative cisplatin-based neoadjuvant chemotherapy (NAC) is the standard care for muscle-invasive bladder cancers (MIBCs). However, the complete response rate to this modality remains relatively low, and current clinicopathologic and molecular classifications are inadequate to predict NAC response in patients with MIBC. Here, we demonstrate that dysregulation of the glutathione (GSH) pathway is fundamental for MIBC NAC resistance. Comprehensive analysis of the multicohort transcriptomes reveals that GSH metabolism and immune-response genes are enriched in NAC-resistant and NAC-sensitive MIBCs, respectively. A machine-learning-based tumor/stroma classifier is applied for high-throughput digitalized immunohistochemistry analysis, finding that GSH dynamics proteins, including glutaminase-1, are associated with NAC resistance. GSH dynamics is activated in cisplatin-resistant MIBC cells, and combination treatment with a GSH dynamics modulator and cisplatin significantly suppresses tumor growth in an orthotopic xenograft animal model. Collectively, these findings demonstrate the predictive and therapeutic values of GSH dynamics in determining the NAC response in MIBCs.

Role of 18 Fluorine-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography Imaging in Assessment of Neoadjuvant Chemotherapy Response in Breast Cancer Patients

Role of 18 Fluorine-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography Imaging in Assessment of Neoadjuvant Chemotherapy Response in Breast Cancer Patients

138P Exome sequencing analysis for the identification of actionable mutations related to neoadjuvant chemotherapy response in locally advanced breast cancer

138P Exome sequencing analysis for the identification of actionable mutations related to neoadjuvant chemotherapy response in locally advanced breast cancer

Proof-of-concept study linking ex vivo sensitivity testing to neoadjuvant anthracycline-based chemotherapy response in breast cancer patients

We developed a functional ex vivo anthracycline-based sensitivity test. Surgical resection material of primary breast cancer (BC) was used to determine criteria for the ex vivo sensitivity assay based on morphology, proliferation and apoptosis. Subsequently, a proof-of-concept study was performed correlating results of this assay on primary BC biopsies with in vivo response after treatment with anthracycline-containing neoadjuvant chemotherapy (NAC). Cut off values for the ex vivo anthracycline-based sensitivity test were established based on analysis of 21 primary breast tumor samples obtained after surgery. In the proof-of-concept study based on a new set of tumor biopsies, 41 patients were included. Eight biopsies did not contain tumor cells and three patients could not be biopsied for various reasons. In the remaining 30 biopsies, the success rate of the ex vivo test was 77% (23/30); six out of seven failed tests were due to excessive apoptosis, our pre-specified test criteria. Of the 23 patients with a successful ex vivo test result, three patients did not undergo NAC after the biopsy. Here we report the ex vivo anthracycline-based sensitivity assay is feasible on biopsy material and shows 75% concordance between ex vivo outcomes and in vivo MRI response. Unfortunately, the percentage of unsuccessful tests is rather high. This study provides the foundation for further development of ex vivo sensitivity assays.

Early changes of platelet‑lymphocyte ratio correlate with neoadjuvant chemotherapy response and predict pathological complete response in breast cancer.

Markers with inflammatory properties, such as the ratio of neutrophils to lymphocytes and the platelet-to-lymphocyte ratio (PLR), have been documented as potential indicators for predicting pathologic complete response (pCR) following neoadjuvant chemotherapy (NACT) in cases of breast cancer. However, whether early changes of PLR (ΔPLR) during NACT can predict pCR has not been reported. A total of 257 breast cancer patients who underwent NACT were retrospectively analyzed. PLR was calculated by evaluating the complete blood cell counts prior to NACT and following two cycles of NACT. The analysis focused on the association between changes in PLR and the response to chemotherapy, as well as the association with pCR. Patients who stayed in or changed to the low PLR level subgroup after two cycles of NACT exhibited a superior response to chemotherapy, in contrast to those who stayed in or changed to the high PLR level subgroup. Of the 257 patients, 75 (29.1%) achieved a pCR after NACT. In the multivariate analysis, there was a significant association between ΔPLR and pCR, whereas pre-treatment and post-treatment PLR did not show any significant association. In multivariate analysis, patients who had a ΔPLR <0 had a notably higher rate of pCR compared with patients with a ΔPLR ≥0. It was concluded that ΔPLR, rather than pre-treatment or post-treatment PLR, is associated with pCR. This suggested that the early changes of PLR after two cycles of NACT might serve as a more accurate predictor for chemotherapy response and pCR in breast cancer.

Lysosomal cyst(e)ine storage potentiates tolerance to oxidative stress in cancer cells

Cyst(e)ine is a key precursor for the synthesis of glutathione (GSH), which protects cancer cells from oxidative stress. Cyst(e)ine is stored in lysosomes, but its role in redox regulation is unclear. Here, we show that breast cancer cells upregulate major facilitator superfamily domain containing 12 (MFSD12) to increase lysosomal cyst(e)ine storage, which is released by cystinosin (CTNS) to maintain GSH levels and buffer oxidative stress. We find that mTORC1 regulates MFSD12 by directly phosphorylating residue T254, while mTORC1 inhibition enhances lysosome acidification that activates CTNS. This switch modulates lysosomal cyst(e)ine levels in response to oxidative stress, fine-tuning redox homeostasis to enhance cell fitness. MFSD12-T254A mutant inhibits MFSD12 function and suppresses tumor progression. Moreover, MFSD12 overexpression correlates with poor neoadjuvant chemotherapy response and prognosis in breast cancer patients. Our findings reveal the critical role of lysosomal cyst(e)ine storage in adaptive redox homeostasis and suggest that MFSD12 is a potential therapeutic target.

Machine Learning Predicts Pathologic Complete Response to Neoadjuvant Chemotherapy for ER+HER2- Breast Cancer: Integrating Tumoral and Peritumoral MRI Radiomic Features.

This study aimed to predict pathologic complete response (pCR) in neoadjuvant chemotherapy for ER+HER2- locally advanced breast cancer (LABC), a subtype with limited treatment response. We included 265 ER+HER2- LABC patients (2010-2020) with pre-treatment MRI, neoadjuvant chemotherapy, and confirmed pathology. Using data from January 2016, we divided them into training and validation cohorts. Volumes of interest (VOI) for the tumoral and peritumoral regions were segmented on preoperative MRI from three sequences: T1-weighted early and delayed contrast-enhanced sequences and T2-weighted fat-suppressed sequence (T2FS). We constructed seven machine learning models using tumoral, peritumoral, and combined texture features within and across the sequences, and evaluated their pCR prediction performance using AUC values. The best single sequence model was SVM using a 1 mm tumor-to-peritumor VOI in the early contrast-enhanced phase (AUC = 0.9447). Among the combinations, the top-performing model was K-Nearest Neighbor, using 1 mm tumor-to-peritumor VOI in the early contrast-enhanced phase and 3 mm peritumoral VOI in T2FS (AUC = 0.9631). We suggest that a combined machine learning model that integrates tumoral and peritumoral radiomic features across different MRI sequences can provide a more accurate pretreatment pCR prediction for neoadjuvant chemotherapy in ER+HER2- LABC.

Value of spectral CT parameters in predicting the efficacy of neoadjuvant chemotherapy for gastric cancer

To investigate the value of pre-chemotherapy spectral computed tomography (CT) parameters in predicting neoadjuvant chemotherapy (NAC) response in gastric cancer (GC). Sixty patients with GC who received NAC and underwent spectral CT examination before chemotherapy were enrolled retrospectively and divided into a responsive group and a non-responsive group according to the postoperative pathological tumour regression grade. Clinical characteristics were collected. The iodine concentration (IC), water concentration (WC), and effective atomic number (Eff-Z) of the portal venous phases were measured before chemotherapy, and IC was normalised to that of the aorta to provide the normalised IC (NIC). An independent samples t-test, Mann-Whitney U-test, or chi-square test was used to analyse the differences between the two groups, and the receiver operating curve (ROC) was used to evaluate the predictive performance of different variables. The neutrophil-to-lymphocyte ratio (NLR) was lower in the responsive group than in the non-responsive group (p<0.05). IC, NIC, and Eff-Z values were significantly higher in the responsive group than in the non-responsive group (p<0.01). The areas under the ROC curves for the NLR, IC, NIC, and Eff-Z were 0.694, 0.688, 0.799, and 0.690, respectively. The combination of NIC, Eff-Z, and NLR values showed good diagnostic performance in predicting response to NAC in GC, with an area under the ROC curve of 0.857, 76.92% sensitivity, 80% accuracy, and 85.71% specificity. Spectral CT parameters may serve as non-invasive tools for predicting the response to NAC in patients with GC.

The heterogeneity of tumour immune microenvironment revealing the CRABP2/CD69 signature discriminates distinct clinical outcomes in breast cancer.

It has been acknowledged that the tumour immune microenvironment (TIME) plays a critical role in determining therapeutic responses and clinical outcomes in breast cancer (BrCa). Thus, the identification of the TIME features is essential for guiding therapy and prognostic assessment for BrCa. The heterogeneous cellular composition of the TIME in BrCa by single-cell RNA sequencing (scRNA-seq). Two subtype-special genes upregulated in the tumour-rich subtype and the immune-infiltrating subtype were extracted, respectively. The CRABP2/CD69 signature was established based on CRABP2 and CD69 expression, and its predictive values for the clinical outcome and the neoadjuvant chemotherapy (NAT) responses were validated in multiple cohorts. Moreover, the oncogenic role of CRABP2 was explored in BrCa cells. Based on the heterogeneous cellular composition of the TIME in BrCa, the BrCa samples could be divided into the tumour-rich subtype and the immune-infiltrating subtype, which exhibited distinct prognosis and chemotherapeutic responses. Next, we extracted CRABP2 as the biomarker for the tumour-rich subtype and CD69 as the biomarker for the immune-infiltrating subtype. Based on the CRABP2/CD69 signature, BrCa samples were re-divided into three subtypes, and the CRABP2highCD69low subtype exhibited the worst prognosis and the lowest chemotherapeutic response, while the CRABP2lowCD69high subtype showed the opposite results. Furthermore, CARBP2 functioned as a novel oncogene in BrCa, which promoted tumour cell proliferation, migration, and invasion, and CRABP2 inhibition triggered the activation of cytotoxic T lymphocytes (CTLs). The CRABP2/CD69 signature is significantly associated with the TIME features and could effectively predict the clinical outcome. Also, CRABP2 is determined to be a novel oncogene, which could be a therapeutic target in BrCa.

Pathologic response to neoadjuvant chemotherapy in ovarian cancer and its association with outcome: A surrogate marker of survival

ObjectiveWe aimed to validate whether pathologic response (pR) to neoadjuvant chemotherapy (NACT) using a three-tier chemotherapy response score (CRS) is associated with clinical outcome in ovarian cancer (OC) and could be used as surrogate marker for survival. MethodsWe conducted a retrospective study of OC patients with FIGO stage III/IV disease who received NACT and graded response as no or minimal (CRS 1), partial (CRS 2), or complete/near-complete (CRS 3) pR using tissue specimens obtained from omentum. Uni- and multivariate survival analyses were performed accounting for age, FIGO stage, debulking and BRCA status as well as neoadjuvant use of bevacizumab. ResultsCRSs 1, 2 and 3 were found in 41(31%), 62 (47%) and 30 (22%) of the 133 examined cases. Response to NACT was associated with significantly improved progression-free (PFS, p < 0.001) and overall survival (OS, p = 0.011). Complete/ near-complete pathologic response (CRS3) was associated with improved PFS (median 24.8 vs. 12.5 months, unadjusted HR 0.28 [95%CI 0.15–0.54], p < 0.001; adjusted hazard ration (aHR) 0.31 [95% CI 0.14–0.72], p = 0.007) and OS (median 63.3 vs. 32.1 months, unadjusted HR 0.27 [95%CI 0.10–0.68], p = 0.006; aHR 0.32 [95% CI 0.09–1.11], p = 0.072) when compared to no or minimal response (CRS1). ConclusionsWe validate a three-tier CRS for assessment of pathologic response to NACT in OC and demonstrate its prognostic independence of BRCA status or neoadjuvant bevacizumab use. Improving pR rates may be a useful goal of NACT in OC with the expectation of improved survival. The CRS may be a useful endpoint in clinical trials in OC.

The potential role of combined shear wave elastography and superb microvascular imaging for early prediction the pathological response to neoadjuvant chemotherapy in breast cancer.

The potential role of shear wave elastography (SWE) and superb microvascular imaging (SMI) for early assessment of treatment response to neoadjuvant chemotherapy (NAC) in breast cancer remains unexplored. This study aimed to identify potential factors associated with the pathological response to NAC using these advanced ultrasound techniques. Between August 2021 and October 2022, 68 patients with breast cancer undergoing NAC were recruited. Patients underwent conventional ultrasonography, SMI, and SWE examinations at baseline and post-2nd cycle of NAC. Maximum tumor diameter (Dmax), maximum elastic value (Emax), peak systolic velocity (PSV), and resistance index (RI) at baseline and the rate of change of these parameters post-2nd cycle were recorded. After chemotherapy, all patients underwent surgery. Using the Miller-Payne's grade, patients were categorized into response (grades 3, 4, or 5) and non-response (grades 1 or 2) group. Parameters were compared using t-tests at baseline and post-2nd cycle. Binary logistic regression analysis was used to identify variables and their odds ratios (ORs) related to responses and a prediction model was established. ROC curves were drawn to analyze the efficacy of each parameter and their combined model for early NAC response prediction. Among the 68 patients, 15(22.06%) were categorized into the non-response group, whereas 53(77.94%) were categorized into the response group. At baseline, no significant differences were observed between the two groups (p>0.05). Post-2nd cycle of NAC, rates of change of Emax, PSV and RI (ΔEmax, ΔPSV and ΔRI) were higher in responders than non-responders (p<0.05). Binary logistic regression analysis revealed that ΔEmax (OR 0.797 95% CI, 0.683-0.929), ΔPSV (OR 0.926, 95%CI, 0.860-0.998), and ΔRI (OR 0.841, 95%CI, 0.736-0.960) were independently associated with the pathological response of breast cancer after NAC. The combined prediction model exhibited higher accuracy in the early evaluation of the response to NAC (AUC 0.945, 95%CI, 0.873-1.000). SWE and SMI techniques enable early identification of tumor characteristics associated with the pathological response to NAC and may be potentially indicative of an effective response. These factors may eventually be used for the early assessment of NAC treatment for clinical management.

Deep Learning of Multimodal Ultrasound: Stratifying the Response to Neoadjuvant Chemotherapy in Breast Cancer Before Treatment.

Not only should resistance to neoadjuvant chemotherapy (NAC) be considered in patients with breast cancer but also the possibility of achieving a pathologic complete response (PCR) after NAC. Our study aims to develop 2 multimodal ultrasound deep learning (DL) models to noninvasively predict resistance and PCR to NAC before treatment. From January 2017 to July 2022, a total of 170 patients with breast cancer were prospectively enrolled. All patients underwent multimodal ultrasound examination (grayscale 2D ultrasound and ultrasound elastography) before NAC. We combined clinicopathological information to develop 2 DL models, DL_Clinical_resistance and DL_Clinical_PCR, for predicting resistance and PCR to NAC, respectively. In addition, these 2 models were combined to stratify the prediction of response to NAC. In the test cohort, DL_Clinical_resistance had an AUC of 0.911 (95%CI, 0.814-0.979) with a sensitivity of 0.905 (95%CI, 0.765-1.000) and an NPV of 0.882 (95%CI, 0.708-1.000). Meanwhile, DL_Clinical_PCR achieved an AUC of 0.880 (95%CI, 0.751-0.973) and sensitivity and NPV of 0.875 (95%CI, 0.688-1.000) and 0.895 (95%CI, 0.739-1.000), respectively. By combining DL_Clinical_resistance and DL_Clinical_PCR, 37.1% of patients with resistance and 25.7% of patients with PCR were successfully identified by the combined model, suggesting that these patients could benefit by an early change of treatment strategy or by implementing an organ preservation strategy after NAC. The proposed DL_Clinical_resistance and DL_Clinical_PCR models and combined strategy have the potential to predict resistance and PCR to NAC before treatment and allow stratified prediction of NAC response.

Solid-Basaloid Adenoid Cystic Carcinoma of the Breast: An Aggressive Subtype Enriched for Notch Pathway and Chromatin Modifier Mutations With MYB Overexpression

Adenoid cystic carcinoma (AdCC) is a rare triple-negative breast cancer analogous to its extramammary counterparts. Diagnosis of the more aggressive solid-basaloid variant of AdCC (SB-AdCC) can be challenging due to poorly defined histopathologic and molecular features. We characterized 22 invasive and in situ basaloid carcinomas by morphology, immunohistochemistry, genetics, and MYB status using multiple platforms and assessed clinical behavior and neoadjuvant chemotherapy responses. After consensus review, 16/22 cases were classified as SB-AdCC. All SB-AdCC had predominantly solid growth and at least focal myxohyaline stroma and were immune-poor. Eosinophilic squamoid cells (69%, 11/16) and basement membrane-like secretions (69%, 11/16) were common, and intercalated ducts (31%, 5/16) were less frequent. SB-AdCC typically expressed SOX10 (100%, 16/16) and luminal markers (100%, 16/16 CK7; 88%, 14/16 CD117; 93%, 13/14 CAM5.2). SMA (40%, 6/15) expression was less common, and SMM (27%, 3/11), GATA3 (20%, 3/15), and p63 (25%, 4/16) were mostly negative. MYB protein and/or MYB RNA overexpression was universal in evaluable cases (13/13), with RNA in situ hybridization (10/10) more reliable than immunohistochemistry (10/11, plus 4 excisions inconclusive). Fluorescence in situ hybridization and/or next-generation sequencing identified MYB rearrangements (20%, 3/15) and amplifications/copy gains (60%, 9/15) but no MYB::NFIB fusions. SB-AdCC often had aberrations in Notch pathway (60%, including 40% NOTCH1 and 20% NOTCH2) and/or chromatin modifier (60%, including 33% CREBBP) genes, with relatively infrequent TP53 mutations (27%). Unclassified invasive basaloid carcinomas lacking described histologic features of SB-AdCC (n = 4) and basaloid ductal carcinoma in situ (n = 2) showed similar immunoprofiles and genetics as SB-AdCC, including Notch aberrations and MYB overexpression with MYB rearrangements/amplifications. Overall, nodal (22%) and distant (33%) metastases were common, and 23% of patients died of disease (mean follow-up, 35 months; n = 22). Responses were poor in all 7 neoadjuvant chemotherapy–treated patients, without any achieving pathologic complete response. The data highlight the histopathologic spectrum of basaloid carcinomas including SB-AdCC and reveal shared genetics and MYB activation, which can be diagnostically useful. Aggressive behavior and poor treatment responses emphasize a need for additional treatment approaches.

Deep learning model improves tumor-infiltrating lymphocyte evaluation and therapeutic response prediction in breast cancer

Tumor-infiltrating lymphocytes (TILs) have been recognized as key players in the tumor microenvironment of breast cancer, but substantial interobserver variability among pathologists has impeded its utility as a biomarker. We developed a deep learning (DL)-based TIL analyzer to evaluate stromal TILs (sTILs) in breast cancer. Three pathologists evaluated 402 whole slide images of breast cancer and interpreted the sTIL scores. A standalone performance of the DL model was evaluated in the 210 cases (52.2%) exhibiting sTIL score differences of less than 10 percentage points, yielding a concordance correlation coefficient of 0.755 (95% confidence interval [CI], 0.693–0.805) in comparison to the pathologists’ scores. For the 226 slides (56.2%) showing a 10 percentage points or greater variance between pathologists and the DL model, revisions were made. The number of discordant cases was reduced to 116 (28.9%) with the DL assistance (p < 0.001). The DL assistance also increased the concordance correlation coefficient of the sTIL score among every two pathologists. In triple-negative and human epidermal growth factor receptor 2 (HER2)-positive breast cancer patients who underwent the neoadjuvant chemotherapy, the DL-assisted revision notably accentuated higher sTIL scores in responders (26.8 ± 19.6 vs. 19.0 ± 16.4, p = 0.003). Furthermore, the DL-assistant revision disclosed the correlation of sTIL-high tumors (sTIL ≥ 50) with the chemotherapeutic response (odd ratio 1.28 [95% confidence interval, 1.01–1.63], p = 0.039). Through enhancing inter-pathologist concordance in sTIL interpretation and predicting neoadjuvant chemotherapy response, here we report the utility of the DL-based tool as a reference for sTIL scoring in breast cancer assessment.

Impact of retention index on the neoadjuvant chemotherapy effect and the prognosis in oesophageal cancer.

The relationship between retention index calculated from dual-time point 18F-fluorodeoxyglucose positron emission tomography-computed tomography and oesophageal cancer prognosis remains unknown. This study aimed to determine usefulness of retention index as a predictor of long-term prognosis of oesophageal cancer and neoadjuvant chemotherapy efficacy. A total of 151 patients with oesophageal cancer who underwent esophagectomy were evaluated retrospectively in this study. We acquired positron emission tomography scans 60 and 120min (SUVmax1 and SUVmax2, respectively) after the intravenous administration of 3.7 Mbq/kg 18F-fluorodeoxyglucose. The patients were divided into two groups: high-retention index (retention index ≥29%, 107 patients) and low-retention index (retention index <29%, 44 patients). Retention index was calculated as follows: retention index (%)=[(SUVmax2 - SUVmax1)/SUVmax1]×100. The overall survival and relapse-free survival rates in the high-retention index group were significantly lower than those in the low-retention index group (P<0.001). Our multivariate analysis identified that the high-retention index group contained independent risk factors for overall survival (hazard ratio: 2.44, P=0.009) and relapse-free survival (hazard ratio: 2.61, P=0.002). The high-retention index group exhibited a lower partial response rate to neoadjuvant chemotherapy evaluated by computed tomography (P<0.001) and a lower pathological therapeutic effect in the resected specimen (P=0.019) than the low-retention index group. The retention index was associated with neoadjuvant chemotherapy responses and long-term prognosis for oesophageal cancer.

Imaging lymph node regression can predict the pathological tumor regression grade in gastric cancer after neoadjuvant treatment

BackgroundNeoadjuvant chemotherapy has become the standard treatment for locally advanced gastric cancer. The tumor regression grade system is an effective and widely used tool for the evaluation of treatment response to neoadjuvant chemotherapy. However, whether tumor regression grade could be predicted using clinical characteristics is uncertain. MethodsA total of 287 locally advanced gastric cancer patients from 2014 to 2021 were retrospectively included. According to the College of American Pathologists' tumor regression grade system, patients were classified into response group (tumor regression grade 0–1) and non-response group (tumor regression grade 2–3). Associations between clinical characteristics and neoadjuvant chemotherapy response were performed by the logistic regression model. The Kaplan–Meier method was used to estimate the survival. A prediction scoring system was constructed based on the β coefficients of multivariate analysis. The receiver operating characteristic curve and decision curve analysis were used to evaluate the performance of the predictive scoring system. ResultsSurvival analysis showed that patients with tumor regression grades 0 to 1 had significantly better disease-free survival and overall survival than the tumor regression grades 2 to 3. Tumor differentiation, ycT stage, immunotherapy, and lymph node regression were independent predictors of pathological response to neoadjuvant chemotherapy. We further developed a scoring system to predict the tumor regression grade. The receiver operating characteristic and decision curve analysis showed good predictive performance of the scoring system. ConclusionLymph node regression could be used as a predictor for pathological response. We developed a scoring system to predict the treatment response of patients with gastric cancer receiving neoadjuvant chemotherapy. The scoring system based on the predictors could provide guidance for making clinical decisions.

ABVS-Based Radiomics for Early Predicting the Efficacy of Neoadjuvant Chemotherapy in Patients with Breast Cancers.

Neoadjuvant chemotherapy (NAC) plays a significant role in breast cancer (BC) management; however, its efficacy varies among patients. Current evaluation methods may lead to delayed treatment alterations, and traditional imaging modalities often yield inaccurate results. Radiomics, an emerging field in medical imaging, offers potential for improved tumor characterization and personalized medicine. Nevertheless, its application in early and accurately predicting NAC response remains underinvestigated. This study aims to develop an automated breast volume scanner (ABVS)-based radiomics model to facilitate early detection of suboptimal NAC response, ultimately promoting personalized therapeutic approaches for BC patients. This retrospective study involved 248 BC patients receiving NAC. Standard guidelines were followed, and patients were classified as responders or non-responders based on treatment outcomes. ABVS images were obtained before and during NAC, and radiomics features were extracted using the PyRadiomics toolkit. Inter-observer consistency and hierarchical feature selection were assessed. Three machine learning classifiers, logistic regression, support vector machine, and random forest, were trained and validated using a five-fold cross-validation with three repetitions. Model performance was comprehensively evaluated based on discrimination, calibration, and clinical utility. Of the 248 BC patients, 157 (63.3%) were responders, and 91 (36.7%) were non-responders. Radiomics feature selection revealed 7 pre-NAC and 6 post-NAC ABVS features, with higher weights for post-NAC features (min >0.05) than pre-NAC (max <0.03). The three post-NAC classifiers demonstrated AUCs of approximately 0.9, indicating excellent discrimination. DCA curves revealed a substantial net benefit when the threshold probability exceeded 40%. Conversely, the three pre-NAC classifiers had AUCs between 0.7 and 0.8, suggesting moderate discrimination and limited clinical utility based on their DCA curves. The ABVS-based radiomics model effectively predicted suboptimal NAC responses in BC patients, with early post-NAC classifiers outperforming pre-NAC classifiers in discrimination and clinical utility. It could enhance personalized treatment and improve patient outcomes in BC management.

Efficacy evaluation of neoadjuvant immunotherapy plus chemotherapy for non-small-cell lung cancer: comparison of PET/CT with postoperative pathology.

To assess the value of positron emission tomography/computed tomography (PET/CT) in the efficacy evaluation of patients undergoing neoadjuvant immunotherapy plus chemotherapy, and to analyze its correlation with postoperative pathology. The PET/CT metabolic parameters and CT size were retrospectively analyzed before and after neoadjuvant immunotherapy plus chemotherapy in 67 patients with resectable stage II/IIIA non-small-cell lung cancer (NSCLC). CT assessment based on immune response evaluation criteria in solid tumor criteria ((i)RECIST) was compared with PET/CT assessment based on the response criteria in solid tumors (PERCIST). The correlations between PET/CT metabolic parameters and postoperative pathology were analyzed. The value of PET/CT in the efficacy evaluation was assessed. The PET/CT assessment showed high consistency with postoperative pathological evaluation, yet the CT assessment showed low consistency with postoperative pathological evaluation. The (i)RECIST and PERCIST criteria showed statistically significant differences (p < 0.001). The postoperative pathological response was negatively associated with ΔSUVmax (%) (r = - 0.812, p < 0.001), ΔSUVmean (%) (r = - 0.805, p < 0.001), and ΔSUVpeak (%) (r = - 0.800, p < 0.001). The cut-off values of 75.8 for ΔSUVmax (%), 67.8 for ΔSUVmean (%), and 74.6 for ΔSUVpeak (%) had the highest sensitivity and specificity. The PERCIST criteria are more sensitive and accurate than (i)RECIST criteria to identify more responders when evaluating the response of neoadjuvant immunotherapy plus chemotherapy for NSCLC. PET/CT shows high accuracy in predicting postoperative pathological response. Our study shows the important role PET/CT plays in the efficacy evaluation of NSCLC patients undergoing neoadjuvant immunotherapy plus chemotherapy, as well as in predicting the prognosis and guiding postoperative treatment. Neoadjuvant immunotherapy plus chemotherapy is highly effective in the treatment of non-small-cell lung cancer. And PET/CT played an important role in the efficacy evaluation following neoadjuvant immunotherapy plus chemotherapy for non-small-cell lung cancer. • Neoadjuvant immunotherapy plus chemotherapy is highly effective in the treatment of NSCLC. • The PERCIST criteria are more sensitive and accurate than (i)RECIST criteria to identify more responders when evaluating the response of neoadjuvant immunotherapy plus chemotherapy for NSCLC. • PET/CT played an important role in the efficacy evaluation; ΔSUVmax (%), ΔSUVmean (%), and ΔSUVpeak (%) following neoadjuvant immunotherapy plus chemotherapy for NSCLC had high consistency and strong correlations with postoperative pathology.

Neoadjuvant chemotherapy response, disease-free survival, and overall survival of breast cancer in a single institution

BackgroundBreast cancer is the most common malignancy among women worldwide. Previous studies have shown factors influencing breast cancer patients' survival, including histopathological grading, stage, histopathological type, hormone receptors, and the number of mitotic images. This study aimed to determine the survival rate in breast cancer patients based on neoadjuvant chemotherapy (NAC) response and regimen. MethodsThis was an observational analytic study with a retrospective design. The population was breast cancer patients at our institution who had undergone NAC. Kaplan–Meier analysis using the log-rank method was used to determine the level of survivability (overall survival [OS] and disease-free survival [DFS]) of patients based on chemotherapy response and regimen. ResultsThe NAC overall response rate of breast cancer patients was 93.17 %, whereas the non-response rate was 6.83 %. Significant differences existed in the DFS of patients by chemotherapy response (p = 0.010). Patients with a complete response had a mean survival of 71.37 ± 2.92 months, those with progressive disease had a mean survival of 64.80 ± 15.58 months, and overall patients had a mean survival of 68.56 ± 10.452 months. Patients with a complete response had a mean recurrence time of 69.54 ± 7.48 months; this was 57.53 ± 19.06 months in those with progressive disease, for an overall time of 65.41 ± 13.81 months. No significant difference existed between the NAC regimens in OS and DFS (p = 0.901 and p = 0.798, respectively). ConclusionGenerally, the response to NAC in breast cancer was very good. The DFS rates were significantly different from the chemotherapy response but not from the NAC regimen.

Temporal Trends and Factors Associated with Receipt of Post-mastectomy Radiation After Neoadjuvant Chemotherapy in Women with cT3 Breast Cancer.

Given the potential impact of increasingly effective neoadjuvant chemotherapy (NACT) on post-mastectomy radiotherapy (PMRT) recommendations, we examined temporal trends in post-NACT PMRT for cT3 breast cancer. We identified women ≥ 18 years in the National Cancer Database (NCDB) diagnosed 2004-2019 with cT3N0-1M0 breast cancer treated with chemotherapy and mastectomy. Multivariable logistic regression and Cox proportional hazards models were used to estimate associations between pathologic NACT response [complete response (CR), partial response (PR), or no response (NR); or disease progression (DP)] and PMRT and between PMRT and overall survival (OS), respectively. We identified 39,901 women (Asian/Pacific Islander 1731, Black 5875, Hispanic 3265, White 27,303). Among cN0 patients with CR, PMRT rates declined from 67% in 2004 to 35% in 2019 but remained unchanged for patients with DP. Relative to NR, CR [odds ratio (OR) 0.36, 95% confidence interval (CI) 0.29-0.46] and PR (OR 0.44, 95% CI 0.36-0.55) in cN0 patients were associated with lower odds of PMRT while DP (OR 1.33, 95% CI 1.05-1.69) was associated with higher odds. Among cN1 patients, PMRT rates decreased from 90% to 73% for CR between 2005 and 2019 and increased from 76% to 82% for DP between 2004 and 2019. Relative to NR, CR (OR 0.78, 95% CI 0.63-0.95) was associated with lower odds of PMRT while DP (OR 1.93, 95% CI 1.58-2.37) was associated with higher odds. PMRT was associated with improved OS among cN1 patients (hazard ratio (HR) 0.77, 95% CI 0.67-0.88). CR was associated with decreased PMRT receipt over time, while temporal trends following PR and DP differed by cN status, suggesting that nodal involvement guided PMRT receipt more than in-breast disease.