Neoadjuvant Treatment Response Research Articles

Body Composition and Response and Outcome of Neoadjuvant Treatment for Pancreatic Cancer

To assess the correlation of body composition with the response and outcome of neoadjuvant treatment (NAT) in patients with pancreatic ductal adenocarcinoma (PDAC). One hundred and nineteen PDAC patients underwent curative resection after NAT. Computed tomography scans of the third lumbar vertebra were used to assess the body composition of these patients before and after NAT. Three distinct wasting phenotypes were identified during NAT, with 51 patients (42.9%) developing muscle and fat wasting (MFW), 17 patients (14.3%) developing fat-only wasting (FW), and 51 patients (42.9%) having no wasting (NW). The response rate was higher in the NW phenotype than in the MFW and FW phenotypes (P = 0.007). In univariate and multivariate analyses, histological grade, sarcopenia before NAT, and MFW during NAT were associated with decreased overall survival (OS). Sarcopenia before NAT and MFW during NAT were associated with decreased disease-free survival (DFS). Body composition was associated with the response and outcome of patients undergoing NAT for PDAC. The response rate was higher in patients having NW during NAT. Sarcopenia before NAT and MFW during NAT were associated with decreased OS and DFS.

Abstract PD9-01: 5-year outcomes in the NBRST trial: Preoperative MammaPrint and BluePrint breast cancer subtype is associated with neoadjuvant treatment response and survival

Abstract Background: MammaPrint (MP) is used to identify breast cancer (BC) patients who can safely forego adjuvant chemotherapy. MP combined with the BluePrint (BP) molecular subtyping signature identifies BC subtypes with distinct therapeutic response rates and survival outcomes. In the Neoadjuvant Breast Symphony Trial (NBRST), MP and BP (MP/BP) predicted rates of pathologic complete response to neoadjuvant chemotherapy (NCT) and partial response to neoadjuvant endocrine therapy (NET). Here, we report 5-year overall survival (OS) and distant metastasis-free survival (DMFS) in patients from the NBRST registry according to MP/BP molecular classification. Methods: The NBRST trial (NCT01479101) prospectively enrolled 1072 patients from 2011 to 2014, who received MP and BP testing. Patients were assigned to receive NCT or NET according to NCCN guidelines and consented to 5 years post-surgery follow-up (FU). Clinical outcomes were available for 913 patients from 67 US institutions. Median FU for OS and DMFS was 5 and 4.6 years, respectively. Tumors classified by MP as High Risk (HR) or Low Risk (LR) were further stratified into four molecular subtypes by BP: Luminal A, Luminal B, HER2, and Basal. Differences in OS and DMFS at 3 and 5 years were assessed by Kaplan Meier analysis and log-rank test. Results: MP results from neoadjuvant patients (N=913) classified 16% of tumors as MP LR and 84% as MP HR. MP and BP classified 15.7% (143/913) of tumors as Luminal A, 32.5% (297/913) as Luminal B, 17.1% (156/913) as HER2, and 34.7% (317/913) as Basal. The 5-year OS and DMFS probabilities were significantly lower in HR compared to LR patients (p < 0.001 for OS and DMFS), and lowest in Basal and Luminal B compared to Luminal A and HER2 subtypes (p < 0.001 for OS and DMFS). Most DMFS events in BP Basal tumors occurred within the first 3 years. Of 841 patients that received NCT with or without HER2-targeted therapy, 12.2% (103/841) were LR and 87.8% (738/841) were HR. MP and BP classified 11.9% (100/841) of these patients as Luminal A, 32.6% (274/841) as Luminal B, 8.3% (154/841) as HER2 subtype, and 37.2% (313/841) as Basal. The 5-year OS and DMFS probabilities were lowest in HR, Basal or Luminal B patients (p < 0.001). In 59 patients who received NET alone, 5-year OS and DMFS were significantly worse in HR patients that had Luminal B or HER2 tumors compared to LR Luminal A patients. In the 39 patients with Luminal A tumors, response to NET at the time of surgery was: 46.2% partial response, 41.0% stable disease, 5.1% progressive disease, 2.6% not reported. Five year DMFS in patients with Luminal A tumors treated with NCT or NET was not significantly different (p=0.67).Conclusions: MammaPrint remained prognostic in BC patients undergoing neoadjuvant therapy. Long -term prognosis was excellent in LR groups who received NCT or NET alone. MP and BP can accurately classify patients into specific subtypes with distinct OS and DMFS outcomes at five years, with BP Basals having the worst outcomes, followed by Luminal B, HER2, and Luminal A subtypes. BP Basal patients had the highest frequency of events within the first 3 years post-surgery, suggesting a genomic risk timeline distinct from other BP subtypes and a potential benefit from a secondary therapeutic immediately post-surgery. Additionally, Luminal A patients had a very low risk of progressive disease while on NET alone prior to surgery, with similar DMFS outcomes to Luminal A-types who received NCT. Number of patientsObserved events% at 5 year (95% CI)p-valueAll patients - MammaPrint Risk GroupOS913134p<0.001Low Risk146794.7 (88.4-97.6)High Risk76712781.1 (77.7-84.0)DMFS913182p<0.001Low Risk1461191.2 (84.2-95.2)High Risk76717175.5 (71.9-78.7)All patients - MammaPrint + BluePrint SubtypeOS913134p<0.001Luminal A143794.6 (88.3-97.6)Luminal B2974484.5 (80.0-88.7)Basal3177472.2 (66.2-77.3)HER2156993.4 (87.1-96.7)DMFS913182p<0.001Luminal A1431191.1 (82.1-94.3)Luminal B2976975.2 (68.0-80.4)Basal3178570.4 (64.6-75.5)HER21561787.2 (79.7-92.0)NCT patients - MammaPrint Risk GroupOS841121p<0.001Low Risk103397.4 (90.1-99.4)High Risk73811881.7 (78.3-84.7)DMFS841167p<0.001Low Risk103792.6 (84.1-96.6)High Risk73816076.2 (72.5-79.4)NCT patients - MammaPrint + BluePrint SubtypeOS841121p<0.001Luminal A100395.5 (86.2-98.6)Luminal B2743978.9 (71.7-84.5)Basal3137168.7 (57.9-77.2)HER2154892.8 (85.9-96.4)DMFS841167p<0.001Luminal A100792.4 (83.8-96.5)Luminal B2746375.7 (65.6-76.5)Basal3138171.4 (65.6-76.5)HER21541687.7 (80.2-92.5)NET alone patients - MammaPrintOS597p=0.01Low Risk39293.0 (74.6-98.2)High Risk20580.0 (55.1-92.0)DMFS598p=0.003Low Risk39293.0 (74.6-98.2)High Risk20674.7 (49.4-88.6)NET alone patients - MammaPrint +BluePrint SubtypeOS597p=0.008Luminal A39293.0 (74.6-98.2)Luminal B18483.3 (56.8-94.3)Basal00N/AHER221N/ADMFS598p=0.005Luminal A39293.0 (74.6-98.2)Luminal B18577.4 (50.3-90.9)Basal00N/AHER221N/A Citation Format: Pat Whitworth, James V Pellicane, Jr., Paul Baron, Peter Beitsch, Laura Lee, Michael Rotkis, Angela Mislowsky, Carrie Dul, Charles Nash, Bichlien Nguyen, Mary Murray, Paul Richards, Mark Gittleman, Stephanie Akbari, Shiyu Wang, Erin B Yoder, Andrea Menicucci, Lisa Blumencranz, William Audeh, NBRST Investigators Group. 5-year outcomes in the NBRST trial: Preoperative MammaPrint and BluePrint breast cancer subtype is associated with neoadjuvant treatment response and survival [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PD9-01.

Abstract PS3-27: 18F-FDG-PET/CT and MRI in the assessment of neoadjuvant chemotherapy treatment response in breast cancer. Correlation with pathological response

Abstract Introduction: Residual cancer burden (RCB) after neoadjuvant chemotherapy (NAC) can accurately predict disease recurrence and survival in breast cancer. In this context, end of treatment (EOT) image-guided response assessment could be useful to adapt treatment for individual patients. Our main objective was to assess the effectiveness of 18F-FDG PET/CT and MRI in determining NAC response. Methods: Patients with surgically resectable breast cancer (stages II-III) were prospectively included and evaluated with 18F-FDG-PET/CT and MRI before and after standard-of-care neoadjuvant chemotherapy (NAC). Ultrasound guided breast biopsy was performed before NAC and pathologic examination with residual cancer burden (RCB) scoring was performed after surgery. Standard uptake values (SUVmax and SUVmean) from baseline PET scans were measured. EOT images results were compared with RCB scores to assess the sensitivity and specificity of (EOT) 18F-FDG-PET/CT and MRI images. A multinomial logistic regression was performed to assess predictors of pathological response. A p-value of 0.05 was considered significant. Results: 30 patients were included, with 25% showing complete pathological response. EOT 18F-FDG-PET/CT images had a sensitivity of 55% and specificity of 100% to predict RCB > 0, while EOT MRI had a sensitivity of 65% and specificity of 66,6%. Luminal tumors showed significant lower baseline SUVmean and SUVmax values than triple-negative tumors. Ki67 expression didn´t correlate with treatment outcome. High estrogen receptor expression level was significantly associated with non-complete pathological response and higher false negative rates in both EOT 18F-FDG-PET and MRI images (7/8 in PET and 6/7 in MRI). Conclusions: 18F-FDG-PET / CT could be a useful diagnostic tool to assess neoadjuvant chemotherapy treatment response in breast cancer, showing greater specificity than MRI to predict pathological response. A high expression of estrogen receptors could lead to a high rate of false negative findings, both in PET and MRI. A greater number of cases is required to validate these findings. Citation Format: Marco Guarin, M. Eugenia Azar, Carolina Tinetti, M. Dolores Mansilla F., Mauro Namias, Pilar Carballo, Lucia Santamarina, Jorgelina Cavallero, Romina Moreau, Christian Gonzalez, Valeria Caceres, Eduardo Gonzalez, Gabriel Bruno. 18F-FDG-PET/CT and MRI in the assessment of neoadjuvant chemotherapy treatment response in breast cancer. Correlation with pathological response [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS3-27.

Abstract PS18-33: Is inflammation a hero or a villain in human breast cancer?

Abstract Inflammation has been linked with cancer, but whether it is part of the problem or part of the solution remains to be a matter of debate in breast cancer. Our group and others have demonstrated that inflammation aggravates cancer progression, however, some claim that inflammation may support immune cell infiltration and suppress cancer. We defined the gene set variation analysis of the Molecular Signatures Database Hallmark inflammatory response gene set as the inflammatory pathway score and analyzed 3632 tumors in total from 4 breast cancer cohorts (METABRIC, TCGA, GSE25066, and GSE21094). In the whole breast cancer cohort, high score tumors were associated with aggressive clinical characteristics, such as worse disease specific survival (p = 0.002), higher Nottingham histological grade (p < 0.001), as well as younger age (young < 65 yo, 65 yo < as elderly; p < 0.001). SphK1 high expression were associated with higher inflammatory score with the striking consistency in the two cohorts (both p < 0.001). Furthermore, inflammatory score was significantly elevated in tumors that express high levels of S1P receptor 1 (S1PR1) (both p < 0.001), and sphingosine kinase 2 (SphK2) low expression tumors were also associated with higher inflammatory score (both p < 0.001). Inflammatory score was significantly higher in Triple Negative (TNBC) (p < 0.001) as well as Basal and Normal subtypes (p < 0.001) compared with the other subtypes in both METABRIC and the TCGA cohorts, which suggest that the detrimental effect of high level of inflammation may be because it includes a more aggressive subtype. On the contrary, high score within TNBC was significantly associated with better survival (DSS; p = 0.014, OS; p = 0.015). TNBC with high score enriched not only IFN-α, IFN-γ response, IL-2/STAT5 signaling, Allograft rejection, Complement, p53 pathway, Reactive Oxygen, and Apoptosis, but also TNF-α signaling, IL6-JAK-STAT signaling, TGF-β signaling, Coagulation, Angiogenesis, EMT, KRAS signaling, and PI3K-AKT-MTOR signaling gene sets (all FDR < 0.25 in both cohorts). High score was associated with mainly favorable anti-cancerous immune cell infiltration, including CD8 T cell, CD4 memory T cell, M1 Macrophage, and dendritic cell (all p < 0.001 in both cohorts), as well as Leukocyte fraction (p < 0.001), TIL regional fraction (p = 0.027), Lymphocyte infiltration (p < 0.001), IFN-γ response (p < 0.001) and TGF-β response (p < 0.001) and cytolytic activity scores (p < 0.001 in both cohorts). Although the inflammatory pathway score was not associated with neoadjuvant treatment response, it associated with expressions of immune checkpoint molecules (all p < 0.001 in both cohorts). In conclusion, inflammation was associated with worse outcome in the whole breast cancer cohort, but with better outcome in TNBC, which was associated with favorable anti-cancerous immune response and immune cell infiltrations. Citation Format: Masanori Oshi, Newman Stephanie, Yoshihisa Tokumaru, Ryusei Matsuyama, Itaru Endo, Kazuaki Takabe. Is inflammation a hero or a villain in human breast cancer? [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS18-33.

Abstract PS3-23: Experience of contrast-enhanced mammography in patients with breast augmentation surgery

Abstract Background: Contrast-enhanced mammography (CEM) is an emerging breast imaging technique utilizing iodinated contrast to highlight areas of neovascularity. The role of CEM in patients with breast implants has not yet been characterized. We report our clinical experience of CEM in patients with breast augmentation surgery to better understand the potential diagnostic utility and limitations of CEM in the setting of breast implants. Materials and Methods: A HIPPA compliant, IRB exempt single-institution review of prospective CEM cases who had “breast implants” in their report between 01/2015 and 03/2020. Medical records were reviewed to supplement database information. Results: Forty-six patients were included with a mean age of 52 years (range 33-72). Clinical indications included: high risk research screen 3 (6%), diagnostic evaluation for abnormal imaging 24 (52%), further evaluation of newly diagnosed breast cancer 12 (26%) or assessment of neoadjuvant treatment response 7 (15%). Thirty patients had malignant lesions. Histology was invasive ductal carcinoma (90%), invasive lobular carcinoma (7%), and ductal carcinoma in situ (3%). CEM identified the index cancer and extent of disease in 28/30 (93%) of malignant cases. In two patients (7%), the malignant lesion was not included in the field-of-view due to its location. One of these lesions was a far medial mass within the breast which was detected by ultrasound alone. The other false negative CEM was a palpable axillary mass negative on both mammogram (MG) and MRI but seen by ultrasound. Twenty-three (50%) underwent additional breast MRI of which 20 had an already diagnosed cancer. the findings on CEM were concordant with MR imaging for the index lesion in 19/20 (95%) cases (kappa=0.86; p <0.001). Six additional lesions were found by CEM and confirmed by MRI. Of these lesions, 33% were found to be malignant and changed the surgical procedure. Four were only seen on CEM (no MRI comparison was available) and 75% were found to be malignant. One was only seen on MRI and was benign. One additional lesion was only seen as an asymmetry on MG without CEM or MRI correlate. This was benign on both the biopsy and surgical pathology. Conclusions: CEM appears to be a valuable breast imaging modality for diagnostic evaluations and surgical staging, including patients with breast implants. Due to technical artifacts and positioning limitations for posterior lesions, we recommend performing CEM with implant displaced views. Breast centers that use CEM, should be aware of field of view as a potential limitation when evaluating extent of disease in patients with breast augmentation. Citation Format: Molly Carnahan, Barbara Pockaj, Victor Pizzitola, Marina Giurescu, Roxanne Lorans, William Eversman, Richard Sharpe, Patricia Cronin, Donald Northfelt, Karen Anderson, Brenda Ernst, Bhavika Patel. Experience of contrast-enhanced mammography in patients with breast augmentation surgery [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS3-23.

Pathological response post neoadjuvant therapy for locally advanced rectal cancer is an independent predictor of survival.

Neoadjuvant treatment (NaT) for locally advanced rectal cancer prior to surgery has led to improved outcomes. However, the relationship between pathological response to NaT and survival is not entirely clear. The aim of this study was to assess the degree of pathological response to NaT on survival outcomes. Clinical and pathological data were collected from a prospectively maintained pathology database between 2005 and 2017. The primary outcome was the overall survival based on pathological response categorized as complete, good partial, partial and minimal. Univariate and multivariate analyses were conducted to identify variables predictive of survival. Cox proportional hazard ratios were used for survival. A total of 596 patients had surgery following NaT for locally advanced rectal cancer. The median follow-up was 4.57years (interquartile range 2.21-8.15years). The overall survival for complete pathological response was 75.6% vs. 37.3% for minimal response (P<0.001). The overall survival at the end of the study in the good partial vs. partial response groups was 58.9% vs. 39% (P<0.001). On multivariate analysis, the degree of pathological response remains an independent variable for overall and disease-specific survival across all categories. In addition to other pathological variables, the degree of pathological response to NaT is an independent predictor for survival outcomes. Future verification of these findings elsewhere could support NaT response being used for adjuvant therapy decision making.

A Machine Learning Tool to Predict the Response to Neoadjuvant Chemotherapy in Patients with Locally Advanced Cervical Cancer

Despite several studies having identified factors associated with successful treatment outcomes in locally advanced cervical cancer, there is the lack of accurate predictive modeling for progression-free survival (PFS) in patients who undergo radical hysterectomy after neoadjuvant chemotherapy (NACT). Here we investigated whether machine learning (ML) may have the potential to provide a tool to predict neoadjuvant treatment response as PFS. In this retrospective observational study, we analyzed patients with locally advanced cervical cancer (FIGO stages IB2, IB3, IIA1, IIA2, IIB, and IIIC1) who were followed in a tertiary center from 2010 to 2018. Demographic and clinical characteristics were collected at either treatment baseline or at 24-month follow-up. Furthermore, we recorded data about magnetic resonance imaging (MRI) examinations and post-surgery histopathology. Proper feature selection was used to determine an attribute core set. Three different machine learning algorithms, namely Logistic Regression (LR), Random Forest (RFF), and K-nearest neighbors (KNN), were then trained and validated with 10-fold cross-validation to predict 24-month PFS. Our analysis included n. 92 patients. The attribute core set used to train machine learning algorithms included the presence/absence of fornix infiltration at pre-treatment MRI as well as of either parametrium invasion and lymph nodes involvement at post-surgery histopathology. RFF showed the best performance (accuracy 82.4%, precision 83.4%, recall 96.2%, area under receiver operating characteristic curve (AUROC) 0.82). We developed an accurate ML model to predict 24-month PFS.

Necrosis Is Not the Main Part of Immune-Related Pathologic Response to Neoadjuvant Immunotherapy in Squamous Cell Lung Cancer

Recently, recommendations for pathologic assessment of lung cancer resection specimens after neoadjuvant therapy have been outlined by the International Association for the Study of Lung Cancer,1 and are intended as guidelines for clinical trials and routine clinical practice to improve consistency in the pathologic assessment of neoadjuvant treatment response. A standardized approach was also recommended to assess the percentages of the following: (1) viable tumor, (2) necrosis, and (3) stroma, with a total adding up to 100% for all systemic neoadjuvant therapies.

Advances and challenges of neoadjuvant therapy in pancreatic cancer.

Pancreatic cancer has been becoming the second cause of cancer death in the western world, and its disease burden has increased. Neoadjuvant therapy is one of the current research hotspots in the field of pancreatic cancer, aiming to improve the surgical rate and prognosis of pancreatic cancer. Based on the latest evidence, this review discussed neoadjuvant therapy in pancreatic cancer from the following three aspects: patient selection, protocols selection of neoadjuvant therapy, and treatment response evaluation and resectability prediction. A big controversy existed on the indications of neoadjuvant treatment, but it was agreed that any patient who is likely to achieve R0 resection due to neoadjuvant therapy should be the targeted population. A variety of chemotherapy regimens were tried for neoadjuvant therapy in pancreatic cancer, and FOLFIRINOX and Nab-Paclitaxel plus Gemcitabine are two preferred regimens at present. It was challenging to evaluate treatment response and predict resectability after neoadjuvant therapy, although imaging by CT is widely used. Based on new findings of the remarkable performance of several chemotherapy regimens with or without radiotherapy, the neoadjuvant indications of pancreatic cancer have extended in recent years. However, it is still a challenge to assess the neoadjuvant treatment response and determine the timing of surgery.

P056 - ABC-system in estimating pathologic response of neoadjuvant treatment of prostate cancer

P056 - ABC-system in estimating pathologic response of neoadjuvant treatment of prostate cancer

Neoadjuvant breast cancer treatment response; tumor size evaluation through different conventional imaging modalities in the NeoDense study

Background Neoadjuvant chemotherapy (NACT) is offered to an increasing number of breast cancer (BC) patients, and comprehensive monitoring of treatment response is of utmost importance. Several imaging modalities are available to follow tumor response, although likely to provide different clinical information. We aimed to examine the association between early radiological response by three conventional imaging modalities and pathological complete response (pCR). Further, we investigated the agreement between these modalities pre-, during, and post-NACT, and the accuracy of predicting pathological residual tumor burden by these imaging modalities post-NACT. Material and methods This prospective Swedish cohort study included 202 BC patients assigned to NACT (2014–2019). Breast imaging with clinically used modalities: mammography, ultrasound, and tomosynthesis was performed pre-, during, and post-NACT. We investigated the agreement of tumor size by the different imaging modalities, and their accuracy of tumor size estimation. Patients with a radiological complete response or radiological partial response (≥30% decrease in tumor diameter) during NACT were classified as radiological early responders. Results Patients with an early radiological response by ultrasound had 2.9 times higher chance of pCR than early radiological non-responders; the corresponding relative chance for mammography and tomosynthesis tumor size measures was 1.8 and 2.8, respectively. Post-NACT, each modality, separately, could accurately estimate tumor size (within 5 mm margin compared to pathological evaluation) in 43–46% of all tumors. The diagnostic precision in predicting pCR post-NACT was similar between the three imaging modalities; however, tomosynthesis had slightly higher specificity and positive predictive values. Conclusion Breast imaging modalities correctly estimated pathological tumor size in less than half of the tumors. Based on this finding, predicting residual tumor size post-NACT is challenging using conventional imaging. Patients with early radiological non-response might need improved monitoring during NACT and be considered for changed treatment plans.

Modern MR Imaging Technology in Rectal Cancer; There Is More Than Meets the Eye.

MR imaging (MRI) is now part of the standard work up of patients with rectal cancer. Restaging MRI has been traditionally used to plan the surgical approach. Its role has recently increased and been adopted as a valuable tool to assist the clinical selection of clinical (near) complete responders for organ preserving treatment. Recently several studies have addressed new imaging biomarkers that combined with morphological provides a comprehensive picture of the tumor. Diffusion-weighted MRI (DWI) has entered the clinics and proven useful for response assessment after chemoradiotherapy. Other functional (quantitative) MRI technologies are on the horizon including artificial intelligence modeling. This narrative review provides an overview of recent advances in rectal cancer (re)staging by imaging with a specific focus on response prediction and evaluation of neoadjuvant treatment response. Furthermore, directions are given for future research.

Computed tomography-based deep-learning prediction of neoadjuvant chemoradiotherapy treatment response in esophageal squamous cell carcinoma

BackgroundDeep learning is promising to predict treatment response. We aimed to evaluate and validate the predictive performance of the CT-based model using deep learning features for predicting pathologic complete response to neoadjuvant chemoradiotherapy (nCRT) in esophageal squamous cell carcinoma (ESCC). Materials and methodsPatients were retrospectively enrolled between April 2007 and December 2018 from two institutions. We extracted deep learning features of six pre-trained convolutional neural networks, respectively, from pretreatment CT images in the training cohort (n = 161). Support vector machine was adopted as the classifier. Validation was performed in an external testing cohort (n = 70). We assessed the performance using the area under the receiver operating characteristics curve (AUC) and selected an optimal model, which was compared with a radiomics model developed from the training cohort. A clinical model consisting of clinical factors only was also built for baseline comparison. We further conducted a radiogenomics analysis using gene expression profiles to reveal underlying biology associated with radiological prediction. ResultsThe optimal model with features extracted from ResNet50 achieved an AUC and accuracy of 0.805 (95% CI, 0.696–0.913) and 77.1% (65.6%-86.3%) in the testing cohort, compared with 0.725 (0.605–0.846)) and 67.1% (54.9%-77.9%) for the radiomics model. All the radiological models showed better predictive performance than the clinical model. Radiogenomics analysis suggested a potential association mainly with WNT signaling pathway and tumor microenvironment. ConclusionsThe novel and noninvasive deep learning approach could provide efficient and accurate prediction of treatment response to nCRT in ESCC, and benefit clinical decision making of therapeutic strategy.

SMAD4-Expressing Pancreatic Ductal Adenocarcinomas Have Better Response to Neoadjuvant Therapy and Significantly Lower Lymph Node Metastasis Rates.

For many patients whose pancreatic ductal adenocarcinoma (PDAC) is locally advanced, neoadjuvant therapy has been proposed as a way to decrease tumor burden. Pancreatic ductal adenocarcinoma is generally thought to be resistant to chemotherapy and radiation, however, response to neoadjuvant therapy in PDAC has been described in a subset of patients. The SMAD4 status is considered to be an important molecular feature which distinguishes two subsets of PDAC, SMAD4-positive and -negative tumors. The objective of this study was to evaluate the neoadjuvant treatment response rate as well as compare the different clinicopathologic variables between SMAD4-positive and -negative tumors. We analyzed the data of patients who underwent surgical resection for PDAC from 2009-2019. Our cohort from a single institution included 233 patients. Of the 233 cases, 143 (61.4%) were SMAD4-negative and 90 (38.6%) were SMAD4-positive. Overall, SMAD4-positive tumors with neoadjuvant therapy had better treatment response and better tumor regression scores. In addition, SMAD4-positive tumors had a significantly lower lymph node metastasis rate in both the neoadjuvant and nonneoadjuvant setting. Further characterization of the role of SMAD4 within the context of neoadjuvant therapy will lead to improved personalized therapeutic strategies.

The apparent diffusion coefficient is a useful biomarker in predicting treatment response in patients with locally advanced rectal cancer.

BackgroundApparent diffusion coefficient (ADC) values achieve promising results in treatment response prediction in patients with several types of cancers.PurposeTo determine whether ADC values predict neoadjuvant chemoradiation treatment (nCRT) response in patients with locally advanced rectal cancer (LARC).Material and MethodsForty-four patients with LARC who underwent magnetic resonance imaging scans before and after nCRT followed by delayed surgery were enrolled retrospectively. The sample was distributed as follows: responders (R), n = 8; and non-responders (Non-R), n = 36. Three markers of treatment response were considered: post-nCRT measures; ΔADC; and Δ%ADC. Statistical analysis included a Wilcoxon test, a Mann–Whitney U test, and a receiver operating characteristic (ROC) analysis in order to evaluate the diagnostic accuracy for each ADC value marker to differentiate between R and Non-R.ResultsBoth minimum and mean ADC values were significantly higher after nCRT in the R group, while non-significant differences between basal and control ADC values were found in the non-R group. In addition, ΔADC and Δ%ADC exhibited increased values after nCRT in R when compared with non-R. ROC analysis revealed the following diagnostic performance parameters: post-nCRT: ADCmin = 1.05 × 10−3 mm2/s (sensitivity 61.1% and specificity 66.7%), ADCmean = 1.50 × 10−3 mm2/s (sensitivity 72.2% and specificity 83.3%), ΔADC: ADCmin = 0.35 (sensitivity 66.7% and specificity 83.3%), ADCmean = 0.50 (sensitivity 72% and specificity 83%); and Δ%ADC: ADCmin = 44% (sensitivity 66.7% and specificity 83.3%) and ADCmean = 60% (sensitivity 83% and specificity 99%).ConclusionOur findings suggest that post-treatment rectal tumor ADC values, as well changes between pre- and post-treatment values, may be biomarkers for predicting treatment response in patients with LARC who underwent nCRT.

Association of Event-Free and Distant Recurrence–Free Survival With Individual-Level Pathologic Complete Response in Neoadjuvant Treatment of Stages 2 and 3 Breast Cancer

Pathologic complete response (pCR) is a known prognostic biomarker for long-term outcomes. The I-SPY2 trial evaluated if the strength of this clinical association persists in the context of a phase 2 neoadjuvant platform trial. To evaluate the association of pCR with event-free survival (EFS) and pCR with distant recurrence-free survival (DRFS) in subpopulations of women with high-risk operable breast cancer treated with standard therapy or one of several novel agents. Multicenter platform trial of women with operable clinical stage 2 or 3 breast cancer with no prior surgery or systemic therapy for breast cancer; primary tumors were 2.5 cm or larger. Women with tumors that were ERBB2 negative/hormone receptor (HR) positive with low 70-gene assay score were excluded. Participants were adaptively randomized to one of several different investigational regimens or control therapy within molecular subtypes from March 2010 through 2016. The analysis included participants with follow-up data available as of February 26, 2019. Standard-of-care neoadjuvant therapy consisting of taxane treatment with or without (as control) one of several investigational agents or combinations followed by doxorubicin and cyclophosphamide. Pathologic complete response and 3-year EFS and DRFS. Of the 950 participants (median [range] age, 49 [23-77] years), 330 (34.7%) achieved pCR. Three-year EFS and DRFS for patients who achieved pCR were both 95%. Hazard ratios for pCR vs non-pCR were 0.19 for EFS (95% CI, 0.12-0.31) and 0.21 for DRFS (95% CI, 0.13-0.34) and were similar across molecular subtypes, varying from 0.14 to 0.18 for EFS and 0.10 to 0.20 for DRFS. The 3-year outcomes from the I-SPY2 trial show that, regardless of subtype and/or treatment regimen, including 9 novel therapeutic combinations, achieving pCR after neoadjuvant therapy implies approximately an 80% reduction in recurrence rate. The goal of the I-SPY2 trial is to rapidly identify investigational therapies that may improve pCR when validated in a phase 3 confirmatory trial. Whether pCR is a validated surrogate in the sense that a therapy that improves pCR rate can be assumed to also improve long-term outcome requires further study. ClinicalTrials.gov Identifier: NCT01042379.

Current Landscape of Breast Cancer Imaging and Potential Quantitative Imaging Markers of Response in ER-Positive Breast Cancers Treated with Neoadjuvant Therapy.

In recent years, neoadjuvant treatment trials have shown that breast cancer subtypes identified on the basis of genomic and/or molecular signatures exhibit different response rates and recurrence outcomes, with the implication that subtype-specific treatment approaches are needed. Estrogen receptor-positive (ER+) breast cancers present a unique set of challenges for determining optimal neoadjuvant treatment approaches. There is increased recognition that not all ER+ breast cancers benefit from chemotherapy, and that there may be a subset of ER+ breast cancers that can be treated effectively using endocrine therapies alone. With this uncertainty, there is a need to improve the assessment and to optimize the treatment of ER+ breast cancers. While pathology-based markers offer a snapshot of tumor response to neoadjuvant therapy, non-invasive imaging of the ER disease in response to treatment would provide broader insights into tumor heterogeneity, ER biology, and the timing of surrogate endpoint measurements. In this review, we provide an overview of the current landscape of breast imaging in neoadjuvant studies and highlight the technological advances in each imaging modality. We then further examine some potential imaging markers for neoadjuvant treatment response in ER+ breast cancers.

Deep learning-based radiomic features for improving neoadjuvant chemoradiation response prediction in locally advanced rectal cancer

Radiomic features achieve promising results in cancer diagnosis, treatment response prediction, and survival prediction. Our goal is to compare the handcrafted (explicitly designed) and deep learning (DL)-based radiomic features extracted from pre-treatment diffusion-weighted magnetic resonance images (DWIs) for predicting neoadjuvant chemoradiation treatment (nCRT) response in patients with locally advanced rectal cancer (LARC). 43 Patients receiving nCRT were included. All patients underwent DWIs before nCRT and total mesorectal excision surgery 6–12 weeks after completion of nCRT. Gross tumor volume (GTV) contours were drawn by an experienced radiation oncologist on DWIs. The patient-cohort was split into the responder group (n = 22) and the non-responder group (n = 21) based on the post-nCRT response assessed by postoperative pathology, MRI or colonoscopy. Handcrafted and DL-based features were extracted from the apparent diffusion coefficient (ADC) map of the DWI using conventional computer-aided diagnosis methods and a pre-trained convolution neural network, respectively. Least absolute shrinkage and selection operator (LASSO)-logistic regression models were constructed using extracted features for predicting treatment response. The model performance was evaluated with repeated 20 times stratified 4-fold cross-validation using receiver operating characteristic (ROC) curves and compared using the corrected paired t-test. The model built with handcrafted features achieved the mean area under the ROC curve (AUC) of 0.64, while the one built with DL-based features yielded the mean AUC of 0.73. The corrected paired t-test on AUC showed P-value < 0.05. DL-based features extracted from pre-treatment DWIs achieved significantly better classification performance compared with handcrafted features for predicting nCRT response in patients with LARC.

Radiomics-based Artificial Intelligence System to Predict Neoadjuvant Treatment Response in Rectal Cancer

Radiomics-based Artificial Intelligence System to Predict Neoadjuvant Treatment Response in Rectal Cancer

Comparison between body composition parameters and response to neoadjuvant chemotherapy by using pre-treatment PET CT in locally advanced breast cancer

PurposeTo compare the adipose and muscle tissue areas in patients who responded differently to neoadjuvant chemotherapy. MethodsOne hundred and eighty six patients diagnosed with breast cancer who underwent neoadjuvant chemotherapy between January 2015- October 2019 and were operated after the treatment were retrospectively included in the study. Pathological results were divided into five groups using the Miller-Payne grading systems. Grade 1 indicating no significant reduction in malignant cells; Grade 2: a minor loss of malignant cells (≤ 30 %); Grade 3: reduction in malignant cells between 30 % and 90 %; Grade 4: disappearance of malignant cells >90 %; Grade 5: no malignant cells identifiable. Pre-treatment PET CT scans were evaluated, and calculation of body composition parameters were performed on a single axial section passing through the L3 vertebrae. Spearman’s correlation test was used to analyze the correlation between SAT, VAT, MT parameters and pathological responses. ResultsThere was no strong correlation between the 5 groups separated according to neoadjuvant chemotherapy treatment response and tissue distributions. However, that there was a very low correlation found between superficial adipose tissue and pathological response (r=, 156). ConclusionIn conclusion, our results have provided a very low correlation between SAT and more than 30 % response. More research is required to evaluate the role of the body fat and muscle parameters in response to neoadjuvant chemotherapy in larger patient populations.