OncologyPASCALENCT04726228
Pain ASsessment in CAncer Patients by Machine LEarning (PASCALE)
An observational study of Oncology and Cancer Pain, run by National Cancer Institute, Naples at 2 sites in Italy.
Trial summary
In cancer patients, the integration between anticancer therapies and palliative care is of fundamental importance. In this context, telemedicine can improve the quality of life (QoL) of chronic patients through self-management and remote monitoring solutions. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Oncology, Cancer Pain, Quality of Life
- How many people
- 40 participants
- Where
- 2 sitesItaly
- Study dates
- 21 Jun 2021to Oct 2025
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Home care patients aged > 18 years, diagnosed with advanced oncological disease (life expectancy ≤ 1 year), suffering from cancer pain.
Who may be able to join
- Patients aged > 18 years
- Home care patients diagnosed with advanced cancer disease and life expectancy ≤ 1 year
- Patients receiving treatment for cancer pain
- Patients who have given their consent
Who cannot join
- Patients aged < 18 years
- Willingness to sign the informed consent form (unable to read or write)
- Cognitive deficit (e.g. Alzheimer disease or senile dementia)
Trial locations and contacts
2 sites in 1 country. Contact the study team or a site near you to ask about taking part.
Italy2 sites, 2 recruiting
- Recruiting
National Cancer Institute of Naples
Naples, Campania, 80131
Marco Cascella, MD, +39 0815903508, m.cascella@istitutotumori.na.it
Arturo Cuomo, MD, +39 0815903508, a.cuomo@istitutotumori.na.it
- Not yet recruiting
A.O.U. Federico II
Naples, Campania
Francesco Cutugno, PhD, +39 081679191, francesco.cutugno@unina.it
About this study
Official title: Home-Based Telemedicine for Automatic Pain Assessment in Cancer Patients: Dataset Creation and Development of Machine Learning Algorithms
In cancer patients, the integration between anticancer therapies and palliative care is of fundamental importance. In this context, telemedicine can improve the quality of life (QoL) of chronic patients through self-management and remote monitoring solutions. This approach can favor the effectiveness of the treatment and therapeutic adherence. Of note, telemedicine can also be applied to the management of cancer pain. In the advanced stages of cancer disease, pain is one of the most obvious and most disabling symptoms. Consequently, proper pain management has a significant impact on the QoL, the ability to withstand treatment, and the recovery of patients. On the other hand, given the complexity of cancer pain, the main obstacle to its proper management is the lack of adequate measurement methods. Although in recent years a great deal of effort has been made in the direction of automatic pain assessment, both concerning the creation of datasets and the development of classification algorithms, the literature is lacking regarding the automatic measurement of pain in the setting of cancer patients. Observation by experienced clinical staff and self-assessment by patients could be useful for obtaining the ground truth and, in turn, for training automatic pain recognition systems.
Read the detailed description
For the entire duration of the study, patients will remain under the care of the Early Palliative Care and Simultaneous Care Outpatient team of the Istituto Nazionale Tumori, Fondazione Pascale, at home. Pain and other symptoms will be managed according to the good clinical practice and patients will receive assistance in agreement to the routine medical care.
The following devices will be used:
- Software
- Instrumentation
- Clinical Assessment Tools: European Organisation for Research and Treatment of Cancer Quality-of-life Questionnaire Core 30 (EORTC QLQ-C30), Daily Pain Diary, 0-10 numeric rating scale (NRS).
The project will be divided into three main Work Packages (WPs), dedicated respectively to the creation of the IT infrastructure to support acquisitions (WP1), the patient data collection campaign (WP2), and the development of machine learning algorithms for automatic pain recognition (WP3). The application of the devices and verification of correct functioning will be carried out at the patient's home by the IT staff involved in the study.
WP1 - The system consists of three main components: the server, with the attached database, the application for mobile devices, also responsible for managing data acquisition from physiological signal acquisition devices, and the desktop application, used by the clinical staff to monitor the progress of data collection.
The mobile application will have the role of interfacing directly with the patient and acquiring biometric data from wearable devices. Specifically, the following signals will be acquired: heart rate, body temperature, non-invasive blood pressure, and galvanic skin response (GSR). The heart rate will be obtained through a wearable device (Garmin Vivosmart 4) while the body temperature, the non-invasive blood pressure, and the GSR will be acquired by an external device (a BITalino platform).To further validate the accuracy of the algorithm that will deal with pain detection, patients will also be given a QoL questionnaire (EORTC QLQ-C30).
In order to acquire the ground truth of the data, the patient will be asked to provide feedback on the level of pain, both at certain intervals of time during the day, and in case of acute pain episodes. This feedback can be based on NRS and multimedia strategies (e.g., videos). Patients will fill out a daily pain diary.
WP2 - The campaign will include a preliminary acquisition phase aimed at testing the IT infrastructure. For obtaining an adequate inter-subject and intra-subject variability, it will be necessary to enroll at least 40 patients, acquiring data for 10-14 days. Thus, the data collection campaign will be conducted for about 6 months. Each subject will use the mobile application and sensors for 2 weeks. Data will be acquired using simultaneously data collection bundles (application, sensors, and any mobile device). Upon enrolment and at the end, EORTC QLQ-C30 will be administered.
WP3 - The objective is the development of algorithms able to predict the level of pain perceived by the patient. Having a considerable amount of labelled data available, the system will learn from the examples.
- Conditions
- Oncology, Cancer Pain, Quality of Life
- Keywords
- Pain assessment, Machine Learning, Automatic Pain Assessment, Palliative Care, Telemedicine, Cancer pain, Quality of Life
- Medical subject headings
- Neoplasms, Cancer Pain
What is being measured
The primary outcome is the main question the study is designed to answer.
To develop a machine learning algorithm useful for predicting the level of pain in cancer patients. A database containing clinical data and pain features will be obtained.
Clinical data: Heart rate (beats per minute, bpm)
Measured: Up to 2 weeks
To develop a machine learning algorithm useful for predicting the level of pain in cancer patients. A database containing clinical data and pain features will be obtained.
Clinical data: Body temperature (Celsius, °C) The patient will use the device provided (BITalino).
Measured: Whenever the patient has a worsening of his/her pain, up to 2 weeks
To develop a machine learning algorithm useful for predicting the level of pain in cancer patients. A database containing clinical data and pain features will be obtained.
Clinical data: Non-invasive Blood Pressure (mmHg). The patient will use the device provided (BITalino).
Measured: Whenever the patient has a worsening of his/her pain, up to 2 weeks
To develop a machine learning algorithm useful for predicting the level of pain in cancer patients. A database containing clinical data and pain features will be obtained.
Clinical data: The Galvanic Skin Response (GSR) refers to changes in sweat gland activity that are reflective of the intensity of the emotional state.
The patient will use the device provided (BITalino).
Measured: Whenever the patient has a worsening of his/her pain, up to 2 weeks
To develop a machine learning algorithm useful for predicting the level of pain in cancer patients. A database containing clinical data and pain features will be obtained.
Pain features:
A daily Pain Diary will be used. Type: how pain is felt (e.g., sharp, ache, shooting, tingling).
Measured: Whenever the patient has a worsening of his/her pain, up to 2 weeks
To develop a machine learning algorithm useful for predicting the level of pain in cancer patients. A database containing clinical data and pain features will be obtained.
Pain features:
A daily Pain Diary will be used. Degree: 0-10 numeric rating scale (NRS) where 0 is no pain and 10 is the worst pain imaginable.
Measured: Whenever the patient has a worsening of his/her pain, up to 2 weeks
To develop a machine learning algorithm useful for predicting the level of pain in cancer patients. A database containing clinical data and pain features will be obtained.
Pain features:
A daily Pain Diary will be used. Duration (minutes, hours, days).
Measured: Whenever the patient has a worsening of his/her pain, up to 2 weeks
To develop a machine learning algorithm useful for predicting the level of pain in cancer patients. A database containing clinical data and pain features will be obtained.
Pain features:
A daily Pain Diary will be used. Precipitating factors.
Measured: Whenever the patient has a worsening of his/her pain, up to 2 weeks
To develop a machine learning algorithm useful for predicting the level of pain in cancer patients. A database containing clinical data and pain features will be obtained.
Pain features:
A daily Pain Diary will be used. Name and amount of drug used and time it was taken.
Measured: Whenever the patient has a worsening of his/her pain, up to 2 weeks
Secondary outcomes (1)
Patients' quality of life assessed by the EORTC QLQ-C30 questionnaire.
Quality of life (QoL) of patients according to European Organization for Research and Treatment of Cancer (EORTC) QLQ-C30 questionnaire. It is scored on a metric from 0 to 100. Higher scores mean better outcome.
Measured: At the beginning and at the end of the observation, up to 2 weeks
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted27 Jan 2021
- Study start21 Jun 2021
- Primary completion23 Feb 2022
- Study completionOct 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (11)Papers the sponsor has linked to this study.
- Other trials for OncologyStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 40 participants (estimated)
Sponsor and investigators
- Lead sponsor
- National Cancer Institute, Naples
- Sponsor type
- University, hospital or other
- Collaborators
- Federico II University
- Responsible party
- Sponsor
- Investigators
- Marco Cascella, MD, Principal investigator, Anesthesia and Pain Medicine. Istituto Nazionale Tumori - IRCCS Fondazione Pascale - Napoli, ItalyArturo Cuomo, MD, Principal investigator, Anesthesia and Pain Medicine. Istituto Nazionale Tumori - IRCCS Fondazione Pascale - Napoli, Italy
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04726228
- Sponsor's protocol ID
- 41/20 oss
- Acronym
- PASCALE
- Status last verified
- Jul 2025
- First submitted
- 29 Dec 2020
- Last updated
- 29 Jul 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Study Protocol will be shared after its publication in a peer reviewed journal.
Publications
Reis-Pina P, Lawlor PG, Barbosa A. Adequacy of cancer-related pain management and predictors of undertreatment at referral to a pain clinic. J Pain Res. 2017 Aug 31;10:2097-2107. doi: 10.2147/JPR.S139715. eCollection 2017.
PubMed 28919809Adamse C, Dekker-Van Weering MG, van Etten-Jamaludin FS, Stuiver MM. The effectiveness of exercise-based telemedicine on pain, physical activity and quality of life in the treatment of chronic pain: A systematic review. J Telemed Telecare. 2018 Sep;24(8):511-526. doi: 10.1177/1357633X17716576. Epub 2017 Jul 11.
PubMed 28696152Sirintrapun SJ, Lopez AM. Telemedicine in Cancer Care. Am Soc Clin Oncol Educ Book. 2018 May 23;38:540-545. doi: 10.1200/EDBK_200141.
PubMed 30231354Aung MSH, Kaltwang S, Romera-Paredes B, Martinez B, Singh A, Cella M, Valstar M, Meng H, Kemp A, Shafizadeh M, Elkins AC, Kanakam N, de Rothschild A, Tyler N, Watson PJ, de C Williams AC, Pantic M, Bianchi-Berthouze N. The Automatic Detection of Chronic Pain-Related Expression: Requirements, Challenges and the Multimodal EmoPain Dataset. IEEE Trans Affect Comput. 2016 Oct-Dec;7(4):435-451. doi: 10.1109/TAFFC.2015.2462830. Epub 2015 Jul 30.
PubMed 30906508Gruss S, Geiger M, Werner P, Wilhelm O, Traue HC, Al-Hamadi A, Walter S. Multi-Modal Signals for Analyzing Pain Responses to Thermal and Electrical Stimuli. J Vis Exp. 2019 Apr 5;(146). doi: 10.3791/59057.
PubMed 31009005Pfeifer AC, Uddin R, Schroder-Pfeifer P, Holl F, Swoboda W, Schiltenwolf M. Mobile Application-Based Interventions for Chronic Pain Patients: A Systematic Review and Meta-Analysis of Effectiveness. J Clin Med. 2020 Nov 5;9(11):3557. doi: 10.3390/jcm9113557.
PubMed 33167300Cuomo A, Bimonte S, Forte CA, Botti G, Cascella M. Multimodal approaches and tailored therapies for pain management: the trolley analgesic model. J Pain Res. 2019 Feb 19;12:711-714. doi: 10.2147/JPR.S178910. eCollection 2019.
PubMed 30863143Rashidi P, Edwards DA, Tighe PJ. Primer on machine learning: utilization of large data set analyses to individualize pain management. Curr Opin Anaesthesiol. 2019 Oct;32(5):653-660. doi: 10.1097/ACO.0000000000000779.
PubMed 31408024Dawes TR, Eden-Green B, Rosten C, Giles J, Governo R, Marcelline F, Nduka C. Objectively measuring pain using facial expression: is the technology finally ready? Pain Manag. 2018 Mar;8(2):105-113. doi: 10.2217/pmt-2017-0049. Epub 2018 Feb 22.
PubMed 29468939Coluccia S, Crispo A, Ottaiano A, Santorsola M, Innamorato MA, Cerrone V, De Feo R, Cascella V, Esposito D, Bruno MP, Sabbatino F, Franci G, Vittori A, Cascella M. Feasibility Assessment of Telehealth-Based Cancer Pain Management Through Machine Learning: A Prospective Clinical Study. Pain Res Manag. 2026;2026(1):e9211861. doi: 10.1155/prm/9211861.
PubMed 42142332Cascella M, Di Gennaro P, Crispo A, Vittori A, Petrucci E, Sciorio F, Marinangeli F, Ponsiglione AM, Romano M, Ovetta C, Ottaiano A, Sabbatino F, Perri F, Piazza O, Coluccia S. Advancing the integration of biosignal-based automated pain assessment methods into a comprehensive model for addressing cancer pain. BMC Palliat Care. 2024 Aug 3;23(1):198. doi: 10.1186/s12904-024-01526-z.
PubMed 39097739
Study at a glance
From the registry record, last updated by the sponsor on 29 Jul 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Home-Based Telemedicine for Automatic Pain Assessment in Cancer Patients: Dataset Creation and Development of Machine Learning Algorithms
- ClinicalTrials.gov ID
- NCT04726228
- Acronym / sponsor ID
- PASCALE / 41/20 oss
- Conditions
- Oncology, Cancer Pain, Quality of Life
- Keywords
- Pain assessment, Machine Learning, Automatic Pain Assessment, Palliative Care, Telemedicine, Cancer pain, Quality of Life
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 40 participants (estimated)
- Lead sponsor
- National Cancer Institute, NaplesUniversity, hospital or other
- Collaborators
- Federico II University
- Study start
- 21 Jun 2021 (actual)
- Primary completion
- 23 Feb 2022 (actual)
- Study completion
- Oct 2025 (estimated)
- First posted
- 27 Jan 2021 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
In cancer patients, the integration between anticancer therapies and palliative care is of fundamental importance. In this context, telemedicine can improve the quality of life (QoL) of chronic patients through self-management and remote monitoring solutions. This approach can favor the effectiveness of the treatment and therapeutic adherence. Of note, telemedicine can also be applied to the management of cancer pain. In the advanced stages of cancer disease, pain is one of the most obvious and most disabling symptoms. Consequently, proper pain management has a significant impact on the QoL, the ability to withstand treatment, and the recovery of patients. On the other hand, given the complexity of cancer pain, the main obstacle to its proper management is the lack of adequate measurement methods. Although in recent years a great deal of effort has been made in the direction of automatic pain assessment, both concerning the creation of datasets and the development of classification algorithms, the literature is lacking regarding the automatic measurement of pain in the setting of cancer patients. Observation by experienced clinical staff and self-assessment by patients could be useful for obtaining the ground truth and, in turn, for training automatic pain recognition systems.
Detailed description
For the entire duration of the study, patients will remain under the care of the Early Palliative Care and Simultaneous Care Outpatient team of the Istituto Nazionale Tumori, Fondazione Pascale, at home. Pain and other symptoms will be managed according to the good clinical practice and patients will receive assistance in agreement to the routine medical care.
The following devices will be used:
- Software
- Instrumentation
- Clinical Assessment Tools: European Organisation for Research and Treatment of Cancer Quality-of-life Questionnaire Core 30 (EORTC QLQ-C30), Daily Pain Diary, 0-10 numeric rating scale (NRS).
The project will be divided into three main Work Packages (WPs), dedicated respectively to the creation of the IT infrastructure to support acquisitions (WP1), the patient data collection campaign (WP2), and the development of machine learning algorithms for automatic pain recognition (WP3). The application of the devices and verification of correct functioning will be carried out at the patient's home by the IT staff involved in the study.
WP1 - The system consists of three main components: the server, with the attached database, the application for mobile devices, also responsible for managing data acquisition from physiological signal acquisition devices, and the desktop application, used by the clinical staff to monitor the progress of data collection.
The mobile application will have the role of interfacing directly with the patient and acquiring biometric data from wearable devices. Specifically, the following signals will be acquired: heart rate, body temperature, non-invasive blood pressure, and galvanic skin response (GSR). The heart rate will be obtained through a wearable device (Garmin Vivosmart 4) while the body temperature, the non-invasive blood pressure, and the GSR will be acquired by an external device (a BITalino platform).To further validate the accuracy of the algorithm that will deal with pain detection, patients will also be given a QoL questionnaire (EORTC QLQ-C30).
In order to acquire the ground truth of the data, the patient will be asked to provide feedback on the level of pain, both at certain intervals of time during the day, and in case of acute pain episodes. This feedback can be based on NRS and multimedia strategies (e.g., videos). Patients will fill out a daily pain diary.
WP2 - The campaign will include a preliminary acquisition phase aimed at testing the IT infrastructure. For obtaining an adequate inter-subject and intra-subject variability, it will be necessary to enroll at least 40 patients, acquiring data for 10-14 days. Thus, the data collection campaign will be conducted for about 6 months. Each subject will use the mobile application and sensors for 2 weeks. Data will be acquired using simultaneously data collection bundles (application, sensors, and any mobile device). Upon enrolment and at the end, EORTC QLQ-C30 will be administered.
WP3 - The objective is the development of algorithms able to predict the level of pain perceived by the patient. Having a considerable amount of labelled data available, the system will learn from the examples.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Home care patients aged > 18 years, diagnosed with advanced oncological disease (life expectancy ≤ 1 year), suffering from cancer pain.
Key inclusion criteria (4 listed)
- Patients aged > 18 years
- Home care patients diagnosed with advanced cancer disease and life expectancy ≤ 1 year
- Patients receiving treatment for cancer pain
- Patients who have given their consent
Key exclusion criteria (3 listed)
- Patients aged < 18 years
- Willingness to sign the informed consent form (unable to read or write)
- Cognitive deficit (e.g. Alzheimer disease or senile dementia)
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
To develop a machine learning algorithm useful for predicting the level of pain in cancer patients. A database containing clinical data and pain features will be obtained.
Time frame: Up to 2 weeks
Clinical data: Heart rate (beats per minute, bpm)
To develop a machine learning algorithm useful for predicting the level of pain in cancer patients. A database containing clinical data and pain features will be obtained.
Time frame: Whenever the patient has a worsening of his/her pain, up to 2 weeks
Clinical data: Body temperature (Celsius, °C) The patient will use the device provided (BITalino).
To develop a machine learning algorithm useful for predicting the level of pain in cancer patients. A database containing clinical data and pain features will be obtained.
Time frame: Whenever the patient has a worsening of his/her pain, up to 2 weeks
Clinical data: Non-invasive Blood Pressure (mmHg). The patient will use the device provided (BITalino).
To develop a machine learning algorithm useful for predicting the level of pain in cancer patients. A database containing clinical data and pain features will be obtained.
Time frame: Whenever the patient has a worsening of his/her pain, up to 2 weeks
Clinical data: The Galvanic Skin Response (GSR) refers to changes in sweat gland activity that are reflective of the intensity of the emotional state. The patient will use the device provided (BITalino).
To develop a machine learning algorithm useful for predicting the level of pain in cancer patients. A database containing clinical data and pain features will be obtained.
Time frame: Whenever the patient has a worsening of his/her pain, up to 2 weeks
Pain features: A daily Pain Diary will be used. Type: how pain is felt (e.g., sharp, ache, shooting, tingling).
To develop a machine learning algorithm useful for predicting the level of pain in cancer patients. A database containing clinical data and pain features will be obtained.
Time frame: Whenever the patient has a worsening of his/her pain, up to 2 weeks
Pain features: A daily Pain Diary will be used. Degree: 0-10 numeric rating scale (NRS) where 0 is no pain and 10 is the worst pain imaginable.
To develop a machine learning algorithm useful for predicting the level of pain in cancer patients. A database containing clinical data and pain features will be obtained.
Time frame: Whenever the patient has a worsening of his/her pain, up to 2 weeks
Pain features: A daily Pain Diary will be used. Duration (minutes, hours, days).
To develop a machine learning algorithm useful for predicting the level of pain in cancer patients. A database containing clinical data and pain features will be obtained.
Time frame: Whenever the patient has a worsening of his/her pain, up to 2 weeks
Pain features: A daily Pain Diary will be used. Precipitating factors.
To develop a machine learning algorithm useful for predicting the level of pain in cancer patients. A database containing clinical data and pain features will be obtained.
Time frame: Whenever the patient has a worsening of his/her pain, up to 2 weeks
Pain features: A daily Pain Diary will be used. Name and amount of drug used and time it was taken.
1 secondary outcome
Patients' quality of life assessed by the EORTC QLQ-C30 questionnaire.
Time frame: At the beginning and at the end of the observation, up to 2 weeks
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 29 Jul 2025.
- Background: PubMed 28919809Reis-Pina P, Lawlor PG, Barbosa A. Adequacy of cancer-related pain management and predictors of undertreatment at referral to a pain clinic. J Pain Res. 2017 Aug 31;10:2097-2107. doi: 10.2147/JPR.S139715. eCollection 2017.
- Background: PubMed 28696152Adamse C, Dekker-Van Weering MG, van Etten-Jamaludin FS, Stuiver MM. The effectiveness of exercise-based telemedicine on pain, physical activity and quality of life in the treatment of chronic pain: A systematic review. J Telemed Telecare. 2018 Sep;24(8):511-526. doi: 10.1177/1357633X17716576. Epub 2017 Jul 11.
- Background: PubMed 30231354Sirintrapun SJ, Lopez AM. Telemedicine in Cancer Care. Am Soc Clin Oncol Educ Book. 2018 May 23;38:540-545. doi: 10.1200/EDBK_200141.
- Background: PubMed 30906508Aung MSH, Kaltwang S, Romera-Paredes B, Martinez B, Singh A, Cella M, Valstar M, Meng H, Kemp A, Shafizadeh M, Elkins AC, Kanakam N, de Rothschild A, Tyler N, Watson PJ, de C Williams AC, Pantic M, Bianchi-Berthouze N. The Automatic Detection of Chronic Pain-Related Expression: Requirements, Challenges and the Multimodal EmoPain Dataset. IEEE Trans Affect Comput. 2016 Oct-Dec;7(4):435-451. doi: 10.1109/TAFFC.2015.2462830. Epub 2015 Jul 30.
- Background: PubMed 31009005Gruss S, Geiger M, Werner P, Wilhelm O, Traue HC, Al-Hamadi A, Walter S. Multi-Modal Signals for Analyzing Pain Responses to Thermal and Electrical Stimuli. J Vis Exp. 2019 Apr 5;(146). doi: 10.3791/59057.
- Background: PubMed 33167300Pfeifer AC, Uddin R, Schroder-Pfeifer P, Holl F, Swoboda W, Schiltenwolf M. Mobile Application-Based Interventions for Chronic Pain Patients: A Systematic Review and Meta-Analysis of Effectiveness. J Clin Med. 2020 Nov 5;9(11):3557. doi: 10.3390/jcm9113557.
- Background: PubMed 30863143Cuomo A, Bimonte S, Forte CA, Botti G, Cascella M. Multimodal approaches and tailored therapies for pain management: the trolley analgesic model. J Pain Res. 2019 Feb 19;12:711-714. doi: 10.2147/JPR.S178910. eCollection 2019.
- Background: PubMed 31408024Rashidi P, Edwards DA, Tighe PJ. Primer on machine learning: utilization of large data set analyses to individualize pain management. Curr Opin Anaesthesiol. 2019 Oct;32(5):653-660. doi: 10.1097/ACO.0000000000000779.
- Background: PubMed 29468939Dawes TR, Eden-Green B, Rosten C, Giles J, Governo R, Marcelline F, Nduka C. Objectively measuring pain using facial expression: is the technology finally ready? Pain Manag. 2018 Mar;8(2):105-113. doi: 10.2217/pmt-2017-0049. Epub 2018 Feb 22.
- Derived from this study: PubMed 42142332Coluccia S, Crispo A, Ottaiano A, Santorsola M, Innamorato MA, Cerrone V, De Feo R, Cascella V, Esposito D, Bruno MP, Sabbatino F, Franci G, Vittori A, Cascella M. Feasibility Assessment of Telehealth-Based Cancer Pain Management Through Machine Learning: A Prospective Clinical Study. Pain Res Manag. 2026;2026(1):e9211861. doi: 10.1155/prm/9211861.
- Derived from this study: PubMed 39097739Cascella M, Di Gennaro P, Crispo A, Vittori A, Petrucci E, Sciorio F, Marinangeli F, Ponsiglione AM, Romano M, Ovetta C, Ottaiano A, Sabbatino F, Perri F, Piazza O, Coluccia S. Advancing the integration of biosignal-based automated pain assessment methods into a comprehensive model for addressing cancer pain. BMC Palliat Care. 2024 Aug 3;23(1):198. doi: 10.1186/s12904-024-01526-z.
- Individual participant dataSharing planned: No. Study Protocol will be shared after its publication in a peer reviewed journal.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Marco Cascella, MD principal investigator
Anesthesia and Pain Medicine. Istituto Nazionale Tumori - IRCCS Fondazione Pascale - Napoli, Italy
Arturo Cuomo, MD principal investigator
Anesthesia and Pain Medicine. Istituto Nazionale Tumori - IRCCS Fondazione Pascale - Napoli, Italy
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Oncology and Cancer Pain, run by National Cancer Institute, Naples at 2 sites in Italy.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 29 Jul 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites in Italy. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Marco Cascella, MD (+39 0815903508, m.cascella@istitutotumori.na.it), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
To develop a machine learning algorithm useful for predicting the level of pain in cancer patients. A database containing clinical data and pain features will be obtained. (time frame: Up to 2 weeks). The study lists 9 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT04726228, which also lists 11 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 29 Jul 2025.