Neoplasms

Skin and Connective Tissue Diseases

All Conditions

Breast Neoplasms

Patient with complete pathological response

Patient with partial pathological response

To measure muscle mass and predict pCR to NAC and its effect on clinical outcomes.

Neoadjuvant therapy

Aysun Isiklar, M.D.

The investigators aimed to accurately measure muscle mass and predict pCR to NAC and its effect on clinical outcomes.

In patients with operable breast cancer (BC) receiving neoadjuvant chemotherapy (NAC), the pathological response rate was found to be related to body composition. The success of complete pathologic response (pCR) is a known prognostic factor in BC patients treated with NAC. The investigators aimed to accurately measure muscle mass and predict pCR to NAC and its effect on clinical outcomes.

Muscle Parameters and Pathological Response in Breast Cancer Patients

The Relation Between the Initial Value of the Skeletal Mass Index and Pathological Response

Residual cancer burden (RCB) is estimated from routine pathologic sections of the primary breast tumor site and the regional lymph nodes after the completion of neoadjuvant therapy.

skeletal muscle area (cm2)/height (m2) et L3 level

Acıbadem Atunizade Hospital

Principal Investigator

Digestive System Diseases

Rare Diseases

Carcinoma

Esophageal Neoplasms

Gastrointestinal Neoplasms

Digestive System Neoplasms

Head and Neck Neoplasms

Gastrointestinal Diseases

Esophageal Diseases

Esophageal Cancer

Patients undergoing NA-CCRT followed by Surgery

Neo-Adjuvant Radiotherapy via any technique, delivered concurrently with Neo-Adjuvant Chemotherapy.

Neo-Adjuvant Radiotherapy

Neo-Adjuvant Chemotherapy, delivered concurrently with Neo-Adjuvant Radiotherapy.

Neo-Adjuvant Chemotherapy

Esophagectomy, performed 4-6 weeks after completion of Neo-Adjuvant Concurrent ChemoRadiation

Esophagectomy

Kundan S Chufal, MD

In esophageal carcinoma, neoadjuvant concurrent chemo-radiotherapy (NA-CCRT) followed by surgery is the current standard of care and ample evidence has accumulated supporting the view that complete pathological response (pCR) is a positive prognostic marker for improved outcomes. Predicting the probability of achieving pCR prior to neoadjuvant treatment could permit modification of treatment protocols for those patients unlikely to achieve pCR.

Radiomics is a new entrant in the field of imaging where specific features are derived from the intensity and distribution pattern of pixels based on a region-of-interest (ROI). The features thus extracted can then be used for prediction modelling similar to other -omics datasets. Preliminary investigations examining its utility have been performed and its applications have thus far focused on screening and survival prediction after treatment. Due to the multi-dimensional nature of data extracted using radiomics, Artificial Intelligence (AI) methods are ideally suited for analysing and modelling radiomic features.

Machine Learning (ML) and Deep Learning (DL)\[utilising Convolutional Neural Networks (CNN)\] are both part of the AI framework. In contrast to ML, DL is a new entrant and has been utilised by some medical researchers for modelling using prediction-type algorithms. Besides significantly reducing the workflow associated with Radiomics-based research, feature engineering and modelling using DL are immune to the effects of incorrect ROI delineation. However, the main limitation of DL is the 'blackbox' effect, in which the underlying basis of a CNN is not known. This has been mitigated in part by the visualisation of activation maps directly on the image dataset to prove biological plausibility of predictions. The comparative performance of both types of modelling is also not known.

Our objective is to investigate pCR probability in our study population using radiomics-based ML and AI-based modelling. We will also investigate the comparative performance of both modelling techniques. For DL based prediction modelling, we will attempt to provide biological plausibility on the basis of activation maps.

Response Prediction to Neoadjuvant Chemoradiation in Esophageal Cancer Using Artificial Intelligence & Machine Learning

Pathological Response Prediction to Neo-adjuvant Chemoradiotherapy in Esophageal Carcinoma and Comparison of Engineered Features Versus Deep Learning Models

Dr Kundan Singh Chufal

Senior Consultant & Chief of Thoracic Radiation Oncology, Department of Radiation Oncology

Symptoms and General Pathology

Triple Negative Breast Neoplasms

Pathologic Complete Response

Breast Diseases

Skin Diseases

Antineoplastic Agents

All Drugs and Chemicals

Antirheumatic Agents

Anti-Infective Agents

Capecitabine

Paclitaxel

Cyclophosphamide

Carboplatin

Pembrolizumab

Albumin-Bound Paclitaxel

Epirubicin

Immunomodulating Agents

Taxane

Antimetabolites

Tubulin Modulators

Antimitotic Agents

Immunosuppressive Agents

Immunologic Factors

Antineoplastic Agents, Alkylating

Alkylating Agents

Antineoplastic Agents, Immunological

Immune Checkpoint Inhibitors

Anti-Bacterial Agents

Antibiotics, Antitubercular

Enzyme Inhibitors

ddEC x 4 + pembrolizumab→ PK x 4 + pembrolizumab, Two-weekly epirubicin/cyclophosphamide (EC) x 4 (epirubicin 90 mg/m2 and cyclophosphamide 600 mg/m2), followed after a three-week interval by three-weekly carboplatin x 4 (AUC = 5) together with weekly paclitaxel x 12 (80 mg/m2). Pembrolizumab is given as a 400 mg iv dosis every 6 weeks for the duration of preoperative chemotherapy.\*

CEX x 4→ PK x 4, Three-weekly cyclophosphamide/epirubicin/capecitabine (CEX) (epirubicin 75 mg/m2, cyclophosphamide 600 mg/m2 and capecitabine 900 mg/m2) x 4, followed after a three-week interval by three-weekly carboplatin x 4 (AUC = 5) together with weekly paclitaxel x 12 (80 mg/m2).Pembrolizumab is given as a 400 mg iv dosis every 6 weeks for the duration of preoperative chemotherapy.\*

\*The addition of pembrolizumab is strongly recommended to all participating patients. However, patients with a documented contraindication, or unwilling to receive immunotherapy may be included in the study without the administration of pembrolizumab.

epirubicin, cyclophosphamide, paclitaxel, carboplatin, pembrolizumab

epirubicin, cyclophosphamide, capecitabine, paclitaxel, carboplatin, pembrolizumab

Niklas Loman, MD, PhD

Lina Zander, RN

Jeanette du Pont Jensen, MD

Lone Volmer, MD

Sopie Yammeni, MD

Ulla Brix Tange, MD

Karin Peschardt, MD

Vesna Glavicic, MD

Erik Hugger Jakobsen, MD

Lars Norberg, MD, PhD

MD, PhD

Lars Norberg

Zakaria Einbeigi, MD, PhD

Per Edlund, MD, PhD

Barbro Linderholm, MD, PhD

Alaa Haidar, MD, PhD

Ida Spång Rosén, MD

Kilian Bachmeier, MD

Rosita Nordkvist, RN

Jenny Bergqvist, MD, PhD

Jenny Bergkvist, MD, PhD

Camilla Wendt, MD, PhD

Anna-Karin Wennstig, MD, PhD

Anne Andersson, MD, PhD

Henrik Lindman, MD, PhD

Cecilia Nilsson, MD, PhD

Ulrik Narbe, MD, PhD

Antonis Valachis, MD, PhD

Primary aim: To compare the effect on pathologic complete response (pCR) rate of adding capecitabine to carboplatin based preoperative chemotherapy in early ER-negative and HER2-negative breast cancer. Pembrolizumab is allowed in both arms after approval for TNBC 2022.

Primary aim: Pathological complete response rate after preoperative chemotherapy is the primary end-point of the study, which will be evaluated by comparing the effects of neoadjuvant administration of a carboplatin-based treatment and treatment adding capecitabine on pCR. After the approval of pembrolizumab in the preoperative treatment of early TNBC in 2022 the study will consist of two cohorts, one (cohort 1) without the addition of pembrolizumab, and one (cohort 2) with the addition of pembrolizumab to both study arms. The primary evaluation will be performed on the entire study population including both cohorts.

Primary translational aim: To investigate if the effects of the treatments depend on homologous repair deficiency (HRD)-status. More specifically, the aim is to test for differential effect of the two treatments on pCR for HRD-negative (HRD low and intermediate by oncoscan) and HRD-positive (HRD high by oncoscan) patients.

NordicTrip, a Translational Study of Preoperative Chemotherapy in TNBC

A Translational Randomized Phase III Study Exploring the Effect of the Addition of Capecitabine to Carboplatine Based Chemotherapy in Early "Triple Negative" Breast Cancer

Distribution of different histopathological subsets of Triple Negative Breast Cancer (TNBC).

Distribution of subsets of Triple Negative Breast Cancer (TNBC) associated with different inherited breast cancer genes (BRCA1, BRCA2, PALB2, TP53, CHEK2, ATM, RAD51 C and RAD51D).

Distribution of subsets of Triple Negative Breast Cancer (TNBC) associated with somatic mutations, (Eg BRCA1, BRCA2, PALB2, TP53, and other).

Distribution of different subsets of Triple Negative Breast Cancer (TNBC) defined based on epigenetic alterations associated with silencing of BRCA1, RAD51 and related HRD-associated genes.

pCR rate in different histopathological subsets of Triple Negative Breast Cancer (TNBC).

pCR rate in subsets of Triple Negative Breast Cancer (TNBC) associated with different inherited breast cancer genes (BRCA1, BRCA2, PALB2, TP53, CHEK2, ATM, RAD51 C and RAD51D).

pCR rate in subsets of Triple Negative Breast Cancer (TNBC) associated with somatic mutations, (Eg BRCA1, BRCA2, PALB2, TP53, and other).

pCR rate in different subsets of Triple Negative Breast Cancer (TNBC) based on epigenetic alterations associated with silencing of BRCA1, RAD51 and related HRD-associated genes.

pCR rate in subsets of Triple Negative Breast Cancer (TNBC) associated with the expression of PDL1.

Rate of pathological complete response, allowing residual dcis, at surgery after preoperative chemotherapy.

Pathological complete response rate, allowing residual dcis, stratified for homologous repair deficiency.

Rate of included patients with toxicity grade 3 or greater during study treatment

Study title	NCT ID	Conditions	Interventions	Eligibility	Organization
Muscle Parameters and Pathological Response in Breast Cancer Patients	NCT06151379	Neoadjuvant Che...	Neoadjuvant the...	29 Years - 72 Years	Acıbadem Atunizade Hospital
Response Prediction to Neoadjuvant Chemoradiation in Esophageal Cancer Using Artificial Intelligence & Machine Learning	NCT04489368	Esophageal Neop...	Neo-Adjuvant Ra... Neo-Adjuvant Ch... Esophagectomy	18 Years -	Rajiv Gandhi Cancer Institute & Research Center, India
NordicTrip, a Translational Study of Preoperative Chemotherapy in TNBC	NCT04335669	Breast Cancer Triple Negative...	epirubicin, cyc... epirubicin, cyc...	18 Years - 75 Years	Lund University Hospital
Muscle Parameters and Pathological Response in Breast Cancer Patients	NCT06151379	Neoadjuvant Che...	Neoadjuvant the...	29 Years - 72 Years	Acıbadem Atunizade Hospital
Response Prediction to Neoadjuvant Chemoradiation in Esophageal Cancer Using Artificial Intelligence & Machine Learning	NCT04489368	Esophageal Neop...	Neo-Adjuvant Ra... Neo-Adjuvant Ch... Esophagectomy	18 Years -	Rajiv Gandhi Cancer Institute & Research Center, India

Spots Global Cancer Trial Database for pathologic response