Category: Cancer

Categories
AI Cancer

AI identifies cancer after doctor misdiagnosis, used to personalize care

IBM Watson detected a rare form of leukemia in a patient in Japan, after comparing genetic changes with a database of 20 million research papers. She had been misdiagnosed by doctors for months, and received the wrong treatment for her cancer type.

Watson has created partnerships with 16 US health systems and imaging firms to identify cancer, diabetes and heart disease.  It has just announced a similar partnership with 21 hospitals in China.

While AI systems still occasionally make mistakes, the trend of using the technology for diagnosis, and  personalized treatment, with suggested therapies based on assumptions of success,  is growing rapidly.

Wearable Tech + Digital Health + NeuroTech Silicon Valley – February 7-8, 2017 @ Stanford University

Categories
Cancer

Preparation free, ingestible colorectal screening device

Check-Cap is developing a swallowable imaging capsule that screens for colorectal cancer with out requiring  bowel-cleansing preparation.

The ingestible capsule transmits X-rays to the intestinal wall and back, creating 3D images of the colon’s internal surface and enabling detection of clinically significant polyps.  Given Imaging,  also based in Israel, used light based (vs. X ray) imaging in its similar PillCam capsule. (Which was acquired by Covidien and then Medtronic.)

Data is transmitted to a wearable device that stores the information for offline analysis.  Users are notified when the capsule has passed through his/her system.   In the next phase, physicians will be able to view the images on any device at any time.

The hope is that with out requiring uncomfortable preparation, more people will participate in colorectal screening, and disease will be discovered early.

Wearable Tech + Digital Health + NeuroTech Silicon Valley – February 7-8, 2017 @ Stanford University

Categories
Brain Cancer

Non-invasive electric field treatment for glioblastoma

Optune by Novocure  uses targeted electric fields to disrupt cancer cell division and cause cancer cell death.  500 hospitals globally can prescribe the FDA approved treatment to glioblastoma patients.

“Tumor Treating Fields” are low intensity, alternating electric fields within the intermediate frequency range. TTFields disrupt cell division through physical interactions with key molecules during mitosis. The non-invasive treatment targets solid tumors.

Company founder Yoram Palti said that trials in other tumors will have results starting this year, and he “believes that we will change the way we treat cancer. There are other growths that are more sensitive to our approach than brain cancer. A pilot of 40 lung cancer patients had exciting results in a treatment where the electrodes are only worn 12 hours a day and not 24.”

Click to view Optune US video

Wearable Tech + Digital Health NYC – June 7, 2016 @ the New York Academy of Sciences

NeuroTech NYC – June 8, 2016 @ the New York Academy of Sciences

Categories
Cancer Sensors

Optical sensor could detect cancer, other diseases, earlier

Guiseppe Strangi and Case Western colleagues have developed a highly sensitive optical sensor, based on nanostructured metamaterials.  The researchers claim that is is 1 million times more sensitive than current sensors, and capable of identifying a single lightweight molecule in a highly dilute solution.  This could dramatically improve the detection of cancer and other diseases.

If doctors could detect a single molecule of an enzyme produced by circulating cancer cells, significantly earlier diagnosis is possible, which could dramatically improve one’s prognosis.

According to Strangi:”Very early, most circulating tumor cells express proteins of a very low molecular weight, less than 500 Daltons. These proteins are usually too small and in too low a concentration to detect with current test methods, yielding false negative results. With this platform, we’ve detected proteins of 244 Daltons, which should enable doctors to detect cancers earlier–we don’t know how much earlier yet.”

Wearable Tech + Digital Health San Francisco – April 5, 2016 @ the Mission Bay Conference Center

NeuroTech San Francisco – April 6, 2016 @ the Mission Bay Conference Center

Wearable Tech + Digital Health NYC – June 7, 2016 @ the New York Academy of Sciences

NeuroTech San Francisco – June 8, 2016 @ the New York Academy of Sciences

 

Categories
AI Cancer Machine Learning

Machine learning analysis of doctor notes predicts cancer progression

Gunnar Rätsch and Memorial Sloan Kettering colleagues are using AI to find similarities between cancer cases.  Ratsch’s algorithm has analyzed 100 million sentences taken from clinical notes of about 200,000 cancer patients to predict disease progression.

In a recent study, machine learning was used to classify  patient symptoms, medical histories and doctors’ observations into 10,000 clusters. Each cluster represented a common observation in medical records, including recommended treatments and typical symptoms. Connections between clusters were mapped to  show inter-relationships. In another study, algorithms were used to  find hidden associations between written notes and patients’ gene and blood sequencing.

Wearable Tech + Digital Health San Francisco – April 5, 2016 @ the Mission Bay Conference Center

NeuroTech San Francisco – April 6, 2016 @ the Mission Bay Conference Center

Wearable Tech + Digital Health NYC – June 7, 2016 @ the New York Academy of Sciences

NeuroTech NYC – June 8, 2016 @ the New York Academy of Sciences

 

 

Categories
Cancer Sensors

Sensor + algorithm detect prostate cancer in urine

Chris Probert and University of Liverpool and UWE Bristol colleagues are creating a test that uses gas chromatography to “smell” prostrate cancer in urine.  If proven accurate, the test might be able to be used instead of current invasive diagnostic procedures, at an earlier stage.

155 men were tested. 58 were diagnosed with prostate cancer, 24 with bladder cancer and 73 with hematuria or poor stream without cancer.  The sensor successfully identified patterns of volatile compounds that allow classification of urine in patients with urological cancers.

Urine samples are inserted into the  “Odoreader” and measured using algorithms.  A 30 meter column enables the urine compounds to travel through it at different rate. The algorithm detects cancer by reading the patterns.

Wearable Tech + Digital Health San Francisco – April 5, 2016 @ the Mission Bay Conference Center

NeuroTech San Francisco – April 6, 2016 @ the Mission Bay Conference Center

Wearable Tech + Digital Health NYC -June 7, 2016 @ the New York Academy of Sciences

NeuroTech NYC – June 8, 2016 @ the New York Academy of Sciences

 

Categories
Cancer

Handheld microscope identifies cancer cells during surgery

University of Washington, Memorial Sloan Kettering Cancer Center, Stanford University and Barrow Neurological Institute researchers are developing a handheld, miniature microscope to allow surgeons to “see” at a cellular level in the operating room.  This can enable more precise brain tumor removal, as surgeons try not  to leave cancerous material behind, while protecting healthy brain matter.

According to lead author Jonathan Liu, “Surgeons don’t have a very good way of knowing when they’re done cutting out a tumor. They’re using their sense of sight, their sense of touch, pre-operative images of the brain — and oftentimes it’s pretty subjective. Being able to zoom and see at the cellular level during the surgery would really help them to accurately differentiate between tumor and normal tissues and improve patient outcomes,”

 

The microscope delivers high-quality images at faster speeds than existing devices. In addition to tumor surgery, researchers will begin testing it as a cancer-screening tool in  dental and dermatological clinics, in an effort to avoid invasive biopsies and waiting time at labs.

Wearable Tech + Digital Health San Francisco – April 5, 2016 @ the Mission Bay Conference Center

NeuroTech San Francisco – April 6, 2016 @ the Mission Bay Conference Center

Wearable Tech + Digital Health NYC – June 7, 2016 @ the New York Academy of Sciences

NeuroTech NYC – June 8, 2016 @ the New York Academy of Sciences

 

Categories
Cancer Sensors

Nanofiber sensor glove could detect breast cancer

Tokyo University’s Takao Someya and Harvard’s Zhigang Suo are developing thin, bendable, pressure sensitive, nanofiber sensors that could be be incorporated into gloves to detect breast tumors.

The 1.9 inch square sheet has 144  pressure measuring locations, and can detect pressure even when twisted.  Many researchers are developing flexible pressure sensors, but they are vulnerable when bent and twisted.

According to  Someya, “Sensitive human fingers of a veteran doctor may be able to find a small tumor, but such perceived sensation cannot be measured. The digitization of the sensations means that they could be shared with other doctors who could theoretically experience the same sensations as the physician who performed the examination.”

Wearable Tech + Digital Health San Francisco – April 5, 2016 @ the Mission Bay Conference Center

NeuroTech San Francisco – April 6, 2016 @ the Mission Bay Conference Center

Wearable Tech + Digital Health NYC – June 7, 2016 @ the New York Academy of Sciences

NeuroTech NYC – June 8, 2016 @ the New York Academy of Sciences

Categories
Cancer

Phone chip may sense cancer in breath

Image:  The Yomiuri Shimbun

A consortium led by Yoshikawa, Genki and National Institute for Materials Science colleagues is in the early stages of developing a phone sensor that they claim is capable of detecting cancer by analyzing breath odor.

A tiny chip is said to determine whether substances linked to cancer exist in one’s breath, and calculates whether he/she is suspected to have the disease.  The result is displayed on a phone’s screen.

NIMS believes that the sensor will be able to distinguish  cancer type in the future. It also may be able to determine odors linked to respiration in diabetes, kidney & liver disease, and asthma.  The researchers are working on multiple applications for both clinical and self quantifying scenarios, and hope for it to be available by 2022.

Wearable Tech + Digital Health San Francisco – April 5, 2016 @ the Mission Bay Conference Center

NeuroTech San Francisco – April 6, 2016 @ the Mission Bay Conference Center

Wearable Tech + Digital Health NYC – June 7, 2016 @ the New York Academy of Sciences

NeuroTech NYC – June 8, 2016 @ the New York Academy of Sciences

 

Categories
Cancer Respiratory

Respiratory motion system for improved lung tumor imaging

U of T professor Shouyi Wang has developed a mathematical model based, personalized respiratory motion system for more precise lung tumor imaging.

Respiratory gating, or a patient’s motion breath-by-breath, is monitored,  and the data is used to focus a radiology beam on the target when the chest cavity is relaxed.  This is the stage that provides the best picture of a cancerous site.

Current techniques depend on expensive, uncomfortable, scanning equipment pressed on a patient’s chest.  This often produces only moderately accurate images.

Wearable Tech + Digital Health San Francisco – April 5, 2016 @ the Mission Bay Conference Center

NeuroTech San Francisco – April 6, 2016 @ the Mission Bay Conference Center

Wearable Tech + Digital Health NYC – June 7, 2016 @ the New York Academy of Sciences

NeuroTech NYC – June 8, 2016 @ the New York Academy of Sciences

 

Categories
Cancer Sensors

Mobile 3D imaging for early breast cancer detection

Vayyar is a 3D imaging company that aims to turn every mobile device into an advanced imaging system.  The company, which focuses on breast cancer detection, among other applications, claims that it can better detect anomalies, at an earlier stage, than traditional methods.    The technology will be demonstrated at CES next month.  ApplySci looks forward to more detail about its sensors, as well as clinical study results.

Wearable Tech + Digital Health San Francisco – April 5, 2016 @ the Mission Bay Conference Center

NeuroTech San Francisco – April 6, 2016 @ the Mission Bay Conference Center

Wearable Tech + Digital Health NYC – June 7, 2016 @ the New York Academy of Sciences

NeuroTech NYC – June 8, 2016 @ the New York Academy of Sciences

Categories
Cancer Data Machine Learning

Machine learning based cancer diagnostics

Search engine giant Yandex is using its advanced machine learning capabilities to detect cancer predisposition.

Yandex Data Factory has partnered with AstraZeneca to develop the RAY platform, which analyzes DNA testing results, generates a report about patient genome mutations, and provides treatment recommendations and side effect information. Testing will begin next month.

The two companies have signed a cooperation agreement  to launch big data projects in epidemiology, pathologic physiology, diagnostics and treatment of diseases (focused on contagious diseases, cancer, endocrinology, cardiology, pulmonology, and psychiatry).

WEARABLE TECH + DIGITAL HEALTH SAN FRANCISCO – APRIL 5, 2016 @ THE MISSION BAY CONFERENCE CENTER

NEUROTECH SAN FRANCISCO – APRIL 6, 2016 @ THE MISSION BAY CONFERENCE CENTER