How to treat Breast cancer?

['Mamela leqephe lena']

Kankere ea matsoele e phekoloa joang?

Kalafo ea kankere ea matsoele e itšetlehile ka lintho tse sa tšoaneng tse kang hore na kankere e se e fihlile boemong bofe, mofuta oa kankere, boemo ba mokuli ka kakaretso ba bophelo bo botle le hore na mokuli o rata eng.

Mekhoa e meng e tloaelehileng ea phekolo e akarelletsa:

1. Ho buuoa: Ho akarelletsa ho tlosa hlahala le lisele tse e potolohileng.

Ho na le mefuta e sa tšoaneng ea ho buuoa, ho akarelletsa ho ntšoa ha lesela le hlabang, ho ntšoa ha sefuba le ho tlosoa ha lymph node.

2. Phekolo ea mahlaseli: E sebelisa mahlaseli a matla haholo ho bolaea lisele tsa kankere le ho fokotsa hlahala.

Hangata e sebelisoa ka mor'a ho buuoa ho bolaea lisele tsa kankere tse setseng.

3. Chemotherapy: Sena se akarelletsa ho sebelisa lithethefatsi ho bolaea lisele tsa kankere.

E ka fuoa ka ho kenngoa ka mothapong kapa ka molomo'me hangata e sebelisetsoa ho fokotsa lihlahala pele ho buuoa kapa ho bolaea lisele tsa kankere tse setseng ka mor'a ho buuoa.

4. Phekolo ea lihormone: E sebelisoa bakeng sa kankere ea matsoele e nang le lihormone'me e sebetsa ka ho thibela lihormone tse susumetsang ho hōla ha lisele tsa kankere.

5. Kalafo e lebisitsoeng: E sebelisa meriana e lebisang limolek'hule tse itseng tse amehang khōlong le ho jaleng ha lisele tsa kankere.

6. Ho phekola ka ho sebelisa matla a mmele: Sena se thusa tsamaiso ea'mele ea ho itšireletsa mafung hore e lemohe lisele tsa kankere le ho li hlasela.

7. Liteko tsa bongaka: Bakuli ba ka 'na ba nahana ho kopanela litekong tsa bongaka e le hore ba fumane mekhoa e mecha ea phekolo.

Ke habohlokoa hore u buisane le sehlopha sa tlhokomelo ea bophelo ka mekhoa e molemohali ea phekolo, kaha moralo oa phekolo o tla etsoa ho latela litlhoko le maemo a motho ka mong.

Ho phaella moo, tlhokomelo e tšehetsang e kang ho laola bohloko, ho tšehetsa maikutlo le ho fana ka tataiso ea phepo e nepahetseng le eona e ka' na ea e - ba karolo ea phekolo.

['Litšupiso']

PubMed/Medline https://www.nlm.nih.gov/databases/download/pubmed_medline.html

RefinedWeb https://arxiv.org/abs/2306.01116

Kumar L, Baldi A, Verma S, Utreja P: Exploring Therapeutic Potential of Nanocarrier Systems Against Breast Cancer. Pharm Nanotechnol. 2018, 6 (2): 94-110.

Mehrgou A, Akouchekian M: Therapeutic impacts of microRNAs in breast cancer by their roles in regulating processes involved in this disease. J Res Med Sci. 2017, 22 (): 130.

Ng AK, Travis LB: Radiation therapy and breast cancer risk. J Natl Compr Canc Netw. 2009, 7 (10): 1121-8.

Ligresti G, Libra M, Militello L, Clementi S, Donia M, Imbesi R, Malaponte G, Cappellani A, McCubrey JA, Stivala F: Breast cancer: Molecular basis and therapeutic strategies (Review). Mol Med Rep. , 1 (4): 451-8.

Bricou A, Barranger E: Response to the article by Evangelista et al.: Use of a portable gamma camera for guiding surgical treatment in locally advanced breast cancer in a post-neoadjuvant therapy setting. Breast Cancer Res Treat 2014. Breast Cancer Res Treat. 2014, 148 (1): 231-2.

Daniyal A, Santoso I, Gunawan NHP, Barliana MI, Abdulah R: Genetic Influences in Breast Cancer Drug Resistance. Breast Cancer (Dove Med Press). 2021, 13 (): 59-85.

Li X, Bu X: Progress in Vaccine Therapies for Breast Cancer. Adv Exp Med Biol. 2017, 1026 (): 315-330.

['Tlhokomeliso: tsa bongaka']

["Websaete ena e etselitsoe ho ruta le ho fana ka boitsebiso feela'me ha e fane ka keletso ea bongaka kapa litšebeletso tsa litsebi."]

["Boitsebiso bo fanoeng ha boa lokela ho sebelisoa ho hlahloba kapa ho phekola bothata ba bophelo bo botle kapa lefu,'me ba batlang keletso ea bongaka ba lokela ho buisana le ngaka e nang le lengolo la tumello."]

['Ka kōpo hlokomela hore neural net e hlahisang likarabo lipotsong, ha e nepahale haholo-holo ha ho tluoa linomorong.']

["Kamehla batla keletso ea ngaka ea hao kapa mofani e mong ea tšoanelehang oa tlhokomelo ea bophelo mabapi le boemo ba bongaka. Le ka mohla u se ke ua hlokomoloha keletso ea setsebi sa bongaka kapa ua lieha ho e batla ka lebaka la ntho eo u e balileng websaeteng ena. Haeba u nahana hore u ka 'na ua ba le boemo ba tšohanyetso ba bongaka, letsetsa 911 kapa u ee kamoreng ea tšohanyetso e haufi hang-hang. Ha ho kamano ea ngaka le mokuli e bōptjoang ke websaeteng ena kapa tšebeliso ea eona. BioMedLib kapa basebetsi ba eona, kapa motho leha e le ofe ea tlatsetsang ho websaeteng ena, ha a etse litemoso, tse hlakileng kapa tse sa hlakang, mabapi le boitsebiso bo fanoeng mona kapa tšebeliso ea eona."]

['Tlhokomeliso: litokelo tsa bangoli']

['Molao oa Digital Millennium Copyright Act oa 1998, 17 U.S.C. § 512 (DMCA) o fana ka litokelo tsa beng ba litokelo tsa bangoli ba lumelang hore thepa e hlahang Inthaneteng e hatakela litokelo tsa bona tlasa molao oa litokelo tsa bangoli ba U.S. ']

['Haeba u lumela ka tumelo e ntle hore litaba leha e le life kapa thepa e fumanehang mabapi le websaeteng kapa litšebeletso tsa rona e tlōla litokelo tsa hau tsa molao, uena (kapa moemeli oa hau) a ka re romella tsebiso e kōpang hore litaba kapa thepa e tlosoe, kapa ho fihlella ho eona ho thibeloe. ']

['Ditsebiso di tlameha ho romelwa ka lengolo ka imeile (bona karolo ya "Ho iteanya" bakeng sa aterese ya imeile). ']

["DMCA e hloka hore tsebiso ea hao ea ho tlōla ha molao ho boleloang hore e na le boitsebiso bo latelang: (1) tlhaloso ea mosebetsi o sirelelitsoeng ka molao o boleloang hore o tlōloa; (2) tlhaloso ea litaba tse boleloang hore li tlōla molao le boitsebiso bo lekaneng ho re lumella ho fumana litaba; (3) boitsebiso ba ho ikopanya le uena, ho akarelletsa aterese ea hau, nomoro ea fono le aterese ea imeile; (4) polelo ea hau ea hore u na le tumelo e ntle ea hore litaba ka tsela e belaelloang ha e lumelloe ke mong'a litokelo tsa molao, kapa moemeli oa eona, kapa ka ts'ebetso ea molao leha e le ofe; "]

['(5) polelo e saennweng ke wena, tlasa kotlo ya ho hlapanya leshano, ya hore tlhahisoleseding e tsebisong e nepahetse le hore o na le matla a ho tiisa ditokelo tsa mongodi tseo ho thweng di a tlolwa; ']

["le (6) ho saena ka letsoho kapa ka elektronike ha mong'a litokelo tsa bangoli kapa motho ea lumeletsoeng ho nka khato lebitsong la mong'a litokelo tsa bangoli. "]

['Ho hloleha ho kenyelletsa tlhahisoleseding yohle e ka hodimo ho ka baka ho dieha ha ho sebetswa tletlebo ya hao.']

['Ho Iteanya']

['Ka kōpo re romelle imeile ka potso / tlhahiso leha e le efe.']

How to treat breast cancer?

The treatment for breast cancer depends on various factors such as the stage of cancer, the type of cancer, the patient's overall health, and personal preferences.

Some common treatment options include:

1. Surgery: This involves removing the tumor and surrounding tissue.

There are different types of surgery, including lumpectomy, mastectomy, and lymph node removal.

2. Radiation therapy: This uses high-energy radiation to kill cancer cells and shrink tumors.

It is often used after surgery to kill any remaining cancer cells.

3. Chemotherapy: This involves using drugs to kill cancer cells.

It can be given intravenously or or orally and is often used to shrink tumors before surgery or to kill any remaining cancer cells after surgery.

4. Hormone therapy: This is used for hormone-receptor-positive breast cancers and works by blocking the hormones that fuel the growth of cancer cells.

5. Targeted therapy: This uses drugs that target specific molecules involved in the growth and spread of cancer cells.

6. Immunotherapy: This helps the immune system recognize and attack cancer cells.

7. Clinical trials: Patients may also consider participating in clinical trials to access new and experimental treatments.

It is important to discuss the best treatment options with a healthcare team, as the treatment plan will be tailored to the individual's specific needs and circumstances.

Additionally, supportive care such as pain management, emotional support, and nutritional guidance may also be part of the treatment plan.

Disclaimer: medical

This web site is provided for educational and informational purposes only and does not constitute providing medical advice or professional services.

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Please note the neural net that generates answers to the questions, is specially inaccurate when it comes to numeric content. For example, the number of people diagnosed with a specific disease.

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